asset management in power systems - paper collection - 2005 - a ph.d. course

Asset ManagemPower Systems A Ph.D. course in co-operation beProf. Matti Lehtonen & Dr. Lin

Kungliga Tekniska Högskolan HelsSchool of Electrical Engineering Pow100 44 Stockholm 0201A-ETS/EEK-0505

ent in

tween KTH and TKK by a Bertling 2005

inki Univeristy of Technology er Systems Laboratory 5 Helsinki

List of Content 1. Course program 2. Start up meeting 3. Course schedule with list of participants 4. Project reports

Course group, August 2005, TKK

Thanks all for a great work!

Lina&Matti August 2005

D

Course program

2004-03-29

Asset Management in Power Systems (6 or 4 credits)

Helsinki University of Technology (TKK), Power systems and high voltage engineering and the Department of Electrical Engineering at KTH in Stockholm, will jointly organize a post-graduate course about asset management in power systems in 2005. Course subjects: The overall topic of asset management has been devided into four themes for the course content that are: (1) Reliability data assessment and reliability modelling and assessment, (2) Reliability centred maintenance for maintenance optimization, (3) Condition monitoring and diagnostics methods and (4) Computer tools supporting techniques for maintenance planning. Course description: The course includes specialists lectures and own student work with an individual task. A list of proposed topics for the task is included in the Appendix. The students shall prepare a 10-15 page summary report, and a presentation (about 6-8 slides for approx 20minutes presentation) about their individual task. The working language is English. Course examination: The examination for the course includes the following two activities:

1. The individual task that shall be presented in a report and as an oral presentation. Approved task result in 2 course points.

2. The written examination will be based on all course material, that includes the four different themes for the course content. There will be an option to take a full examination of 4 course points that includes examination of all themes or an examination of a selected part of themes for 2 course points. The course participant should announce at the registration, which target that is selected for the written examination.

Course material: Lecture handouts, summary reports, conference and journal papers. A list of recommended literature that is related to the identified tasks will be delivered in the first course day. Course schedule:

- Registrations: by 18th April to [email protected] or [email protected]. The number of participants is limited to 45.

- Start-up meetings to deliver material / homework: 25th April at KTH and 27th April at TKK.

- Students have to prepare their project reports and presentation slides by 10th August

- Lecture days: 17th to 19th August, 2005. From 10-16 o´clock at TKK, Power systems and high voltage engineering, room I345.

mailto:[email protected]


- Examination: 5th September. Examinations at same time in Sweden and Finland.

Course responsible and lecturers: Prof. Matti Lehtonen, TKK and Dr. Lina Bertling, KTH.

Matti Lehtonen (1959) was with VTT Energy, Espoo, Finland from 1987 to 2003, and since 1999 has been a professor at the Helsinki University of Technology, where he is now head of Power Systems and
High Voltage Engineering. Matti Lehtonen received both his Master’s and Licentiate degrees in Electrical Engineering from Helsinki University of Technology, in 1984 and 1989 respectively, and the Doctor of Technology degree from Tampere University of Technology in 1992. The main activities of Dr. Lehtonen include power system planning and asset management, power system protection including earth fault problems, harmonic related issues and applications of information technology in distribution systems. (Helsinki University of Technology, Power Systems and High Voltage Engineering, P.O.Box 3000, FIN-02015 HUT, Finland, Tel. +358 9 4515484, Fax +358 9 460224, E-mail: [email protected])
Lina Bertling was born in Stockholm in 1973. She completed her Ph.D. 2002 and TechLic 1999 in Electric power systems at the Department of Electrical Engineering, and M.Sc. 1997 at the Vehicle engineering program specialized in Systems Engineering at the Department of Mathematics, all at the Royal Institute of Technology (KTH), Stockholm, Sweden. She was a visiting postdoctoral student at the University of Toronto associated with Kinectrics Inc. during 2002-2003. She is currently engaged at KTH as research associate and project leader of the research program on asset management in power systems. Her research interests are in reliability evaluation of power systems, and development of methods for maintenance optimization with special interest in reliability centered maintenance methods. Current involvements include organizing of the 9th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) in June 2006, www.pmaps2006.org. (KTH Dept. of Electrical Engineering, 100 44 Stockholm, SWEDEN, Phone: +46 8 790 6508 E-mail: [email protected], www.ets.kth.se/eek/lina.)

Course fee: For PhD students the course is free, for others the fee is 500 euros


http://www.pmaps2006.org/


http://www.ets.kth.se/eek/lina

Appendix: Proposed topics for individual tasks

Theme 1: Reliability data assessment, reliability modelling and assessment

1. Reliability assessment of surge arresters 2. Failure causes in medium voltage networks 3. Reliability analysis methods for distribution systems 4. Reliability engineering in distribution system planning 5. Reliability modelling of HV switchgear 6. MonteCarlo simulation methods for reliability modelling and assessment

Theme 2: Reliability centred maintenance for maintenance optimization

7. Different maintenance strategies and their impact on power system reliability 8. Prioritization of maintenance methods (RCM,CBM,TBM,CM) 9. RCM applications for overhead lines 10. RCM applications in underground power systems 11. RCM applications for power transformers 12. RCM applications for switchgear 13. RCM applications for secondary substations 14. RCM applications for generator systems 15. Maintenance optimization techniques for distribution systems

Theme 3: Condition monitoring and diagnostics methods

16. Condition monitoring of wooden poles 17. Maintenance and condition monitoring on large power transformers 18. Aging phenomena of paper-oil insulation in power transformers 19. On-line monitoring applications for power transformers 20. Tests and diagnostics of insulating oil 21. Diagnostics of dissolved gas in insulating oil of transformers 22. Dielectric diagnostics measurements of transformers and their interpretation 23. PD-measurements for transformers and their interpretation 24. Diagnostics and condition monitoring of power cables 25. Aging phehomena of cable insulation materials 26. Dielectric diagnostics measurements of power cables and their interpretation 27. Condition monitoring of cable acessories (joints, terminal) 28. PD-measurements of power cables and their accessories 29. On-line monitoring applications for power cables 30. Condition monitoring of circuit breaker and switchgear 31. Condition monitoring and diagnostics of GIS

32. Condition monitoring of high voltage circuit breakers 33. Condition monitoring of surge arresters 34. On-line monitoring applications for secondary substations

Theme 4: Computer tools supporting techniques for maintenance planning

35. Evaluation of different tools for reliability assessment e.g. RADPOW, NEPLAN, NetBAS etc.

36. Evaluation of using the tool VeFoNet for purpose of maintenance planning, RCM. 37. Evalution of using the tool NEPLAN for the purpose of maintenance planning 38. Compliation of existing functions for RCM in commercial tools like NetBas,

NEPLAN. Meldis, Maximo etc. In addition, the students are free to propose a subject of their own, as long as the subject is in line to the themes above. It is recommended that, if only possible, the students select a subject which is close to their PhD-project.

1

AM course Start-up meeting April 2005

Welcome to the course on Asset management in power systems

KTH and TKK 2005

Prof. Matti Lehtonen (Course responsible at TKK)Dr. Lina Bertling (Course responsible at KTH )


Contents – Start-up meeting

1. Introduction to the course– Concepts*– Challenges*– Objectives and approach

2. Presentation of the course program 3. Selection of topics 4. Closure

*Material based on key-note presentation: Lina Bertling, Asset management using reliability-centered maintenance methods, NORDAC, Espoo, 23-24 August 2004

2


1 AM course – Concepts• Asset management (AM) is a concept used today for

planning and operation of the electrical power system

• The aim of AM is to handle physical assets in an optimal way in order to fulfil an organisations goal whilst considering risk where: − the goal could be maximum asset value, maximum

benefit or minimal life cycle cost− the risk could be defined by the probability of failure

occurrence and it consequence e.g. unavailability in power supply to customers


1 AM course – Concepts• There are different possible actions to handle these assets:

e.g. acquire, maintain, replace or redesign

• AM implies consequently to make right decisions on:− what assets to apply actions for− what actions to apply− how to apply the actions− when to apply the actions

3


1 AM course – Concepts• To make the right decisions there is a need of;

− condition data− failure statistics− reliability modelling techniques− reliability assessment tools− maintenance planning tools− systematic techniques for maintenance planning e.g.

reliability-centred maintenance (RCM) method• This different type of needs are covered within the course

subject for this course.


1 AM course – Concepts

− Equipment maintenance provides a tool for handling reliability either by preventive (PM) or corrective maintenance (CM) of assets.

− PM could be separated into either:

• time-based maintenance (TBE) traditional

• condition based maintenance (CBM)

− RCM provides a tool for AM by balancing between CM and PM to reach cost-effective maintenance plans

4


1 AM course – ConceptsBackground for RCM− Originated in the civil aircraft industry in 1960s− US Department of Commerce defined the concept RCM in 1975

declared that all major systems should apply RCM− First full description of RCM (Nowlan 1978)− Introduced for nuclear power industry 1980s by EPRI− Introduced for hydro power plants in Norway in 1990s and at

Vattenfall from 1997-2004− Ongoing introduction for transmission and distribution system

operators e.g. pilot studies by Swedenergy and research at KTH (quantitative method Reliability-Centered Asset Maintenance).


1 AM course – Concepts• The aim of RCM is to optimize the maintenance

achievements (efforts, performance) in a systematic way.• The following features define and characterize RCM:

− preservation of system function,− identification of failure modes,− prioritizing of function needs, and− selection of applicable and effective maintenance tasks.

• RCM does not add anything new in a technical sense.• The method is generally qualitative.

5


1 AM course – Challenges• To get support and confident in incorporating new

systematic techniques to support in decision process of AM – not new technique but new working process

• Develop, implement and integrate information systems to support and handle required data in detail and range

• Develop general models of component reliability and relationships to change in component condition

• Develop decision support methods and optimization algorithms that are applicable and could be supported with real data.

• Develop measurement techniques to support CBM and RCM

• Further develop RCM methods like RCAM


2 AM course – Course objective

• The objective for this course is to investigate solutions to these challenges by studying different needs for AM.

• The different needs for AM has been divided into four themes for the course content that are: 1. reliability data assessment and reliability modelling,2. RCM for maintenance optimization, 3. condition monitoring and diagnostics methods, and 4. computer tools supporting techniques for

maintenance planning• The objective is that the course participants gain

knowledge in all these 4 themes as tools for AM.

6


2 AM course – Course approach

– There are expertise knowledge in AM at both TKK and KTH including several research activities e.g.:

– development of techniques and methods for CBM at TKK and KTH

– development of methods for RCM at KTH

– The approach for this course is to use this knowledge as a platform for investigating AM and to communicate the different experiences in the area.


2 AM course – Course program

– The course program has been handed out and is available at http://www.ets.kth.se/seminarium.htm

– It summarizes relevant information about the course as:course subjectscourse descriptioncourse examination course materialcourse schedule course responsible and lectures

7


2 AM course – Course subject

– For this course the overall topic of AM has been divided into four themes for the course content that are: 1. reliability data assessment and reliability modelling,2. RCM for maintenance optimization, 3. condition monitoring and diagnostics methods, and 4. computer tools supporting techniques for

maintenance planning


2 AM course – Course description

– The course includes lectures and own student work with an individual tasks.

– A list of proposed topics for the tasks is included in the course program.

– The students shall prepare a 10-15 page summary report, and a presentation (about 8-10 slides for approx 20minutes presentation) about their individual task.

– Guidelines are provided for writing the report. See “GUIDELINES FOR THE PREPARATION OF REPORTS FOR THE COURSE “ASSET MANAGEMENT IN POWER SYSTEMS”

– The working language is English.

8


2 AM course – Course examination

• The examination for the course includes two activities i.e.:1. Individual task that shall be presented in a report and

as an oral presentation. Approved task result in 2 course points.

2. Written examination that will be based on all course subjects. There will be an option to take a full examination of 4 course points that includes examination of all themes or an examination of a selected part of themes for 2 course points.

• The course participants should announce at the start-up meeting, which target that is selected for the written examination. More information about the exam will be provided after the start-up meeting.


2 AM course – Course material

• The course material includes: – Lecture handouts– Summary reports and presentation slides for the tasks– Recommended literature

• A summary presentation of the course material will be presented and published as course material at KTH/TKK, and will be available in pdf-format.

• In the following slides some recommended literature with links are provided

9



• The main source for literature is the IEEE Explore (with approximately 1 million research papers) http://www.ieeexplore.ieee.org/Xplore/dynhome.jsp

• A list of references is handed out as a result from a general literature search at IEEE Explore for the AM course.



• Links with reference literature in power industry:– EPRI http://www.epri.com/– Cigré http://www.cigre.be/– SINTEF http://www.sintef.no/– Elforsk http://www.elforsk.se/– SwedEnergy http://www.svenskenergi.se/

10



• Links with reference literature at KTH:– PhD theses at KTH (e.g. Bertling 2002 on RCM)

http://media.lib.kth.se/kthdiss.asp– Research at KTH see annual report

http://www.ets.kth.se/annual_report.htm– Research at KTH/EKC see list of publications at

http://www.ets.kth.se/comp– Course material and reference material:

http://www.ets.kth.se/eek/lina/2C4030.html


2 AM course – Course material• Links with reference to different standards:

− IEEE http://standards.ieee.org/

− Military standards e.g. MIL STD

− ISO/IEC http://www.iso.org/iso/en/ISOOnline.frontpage

− SIS http://www.sis.se/

− Utek http://www.utek.se/

11


2 AM course – Course materialExample on useful standards for reliability assessment:

• IEC 300 ”Dependability management” (SEK SS 441 05 05 ”Tillförlitlighet-ordlista”)

• British Standard BS5760 ”Reliability of systems, equipments and components”

• MIL-STD 785 ”Reliability program for systems and equipment- development and production”

• MIL-STD 882 ”System safety program requirements”


2 AM course – Course materialExample on useful standards for reliability assessment:

• MIL-STD 756 ”Reliability prediction”

• IEEE Std. 352 ”General principles of reliability analysisof nuclear power generating station protection systems”

• ISO9000 Quality system standards that is identical with• European: CEN/CENELEC EN29000

• American ANSI/ASQC Q90

12


2 AM course – Course materialExample on books on reliability assessment and RCM:

– Hoyland A., Rausand M., System reliability theory - models and statistical methods, Wiley Series, 2004

– Patrick D. T. O´Connor: Practical Reliability Engineering, Wiley&Sons Ltd, 2002

– Roy Billinton and Ron Allan, Reliability Evaluation of Power Systems

– Lina Bertling, RCM for Electric Power Distribution Systems, KTH,Doctoral thesis 2002 (that includes a reference list with fundamental books on distribution systems and RCM)


2 AM course – Course schedule

• The course schedule involves the following activities and deadlines:– Start-up meeting with selection of student tasks and

hand out of recommended literature 25th (KTH ) and 27th April (TKK)

– Individual project work with presentation in report with deadline for submission for 10th August.

– Lecture days with introduction lectures by course leaders followed with presentations of tasks by course participants 17th-19th August

– Examination 5th September

13


2 AM course – Course responsible

• The course responsible and lectures in the course are: – Prof. Matti Lehtonen at TKK (that is the main co-

ordinator for the course)– Dr. Lina Bertling at KTH (that co-ordinate

participants from Sweden)



• Contact information to Prof. Matti Lehtonen, TKK – Helsinki University of Technology– Power Systems and High Voltage Engineering, – P.O. Box 3000, FIN-02015 HUT, – Finland, Tel. +358 9 4515484, – Fax +358 9 460224, – E-mail: [email protected]

14



• Contact information to Dr. Lina Bertling, KTH. – KTH Dept. of Electrical Engineering – 100 44 Stockholm, SWEDEN – Phone: +46 8 790 6508 – E-mail: [email protected]– WWW: www.ets.kth.se/eek/lina


3 AM course – Selection of tasks

• Theme 1: Reliability data assessment, reliability modelling and assessment 1. Reliability assessment of surge arresters2. Failure causes in medium voltage networks3. Reliability analysis methods for distribution systems4. Reliability engineering in distribution system planning5. Reliability modelling of HV switchgear6. MonteCarlo simulation methods for reliability modelling and

assessment

15



– Theme 2: Reliability centred maintenance for maintenance optimization 7. Different maintenance strategies and their impact on power

system reliability8. Prioritization of maintenance methods (RCM,CBM,TBM,CM)9. RCM applications for overhead lines10. RCM applications in underground power systems11. RCM applications for power transformers12. RCM applications for switchgear13. RCM applications for secondary substations14. RCM applications for generator systems15. Maintenance optimization techniques for distribution systems



– Theme 3: Condition monitoring and diagnostics methods16. Condition monitoring of wooden poles17. Maintenance and condition monitoring on large power

transformers18. Aging phenomena of paper-oil insulation in power transformers19. On-line monitoring applications for power transformers20. Tests and diagnostics of insulating oil21. Diagnostics of dissolved gas in insulating oil of transformers22. Dielectric diagnostics measurements of transformers and their

interpretation23. PD-measurements for transformers and their interpretation24. Diagnostics and condition monitoring of power cables25. Aging phenomena of cable insulation materials

16



– Theme 3: Condition monitoring and diagnostics methods26. Dielectric diagnostics measurements of power cables and their

interpretation27. Condition monitoring of cable accessories (joints, terminal)28. PD-measurements of power cables and their accessories29. On-line monitoring applications for power cables30. Condition monitoring of circuit breaker and switchgear31. Condition monitoring and diagnostics of GIS 32. Condition monitoring of high voltage circuit breakers33. Condition monitoring of surge arresters34. On-line monitoring applications for secondary substations



– Theme 4: Computer tools supporting techniques for maintenance planning 35. Evaluation of different tools for reliability assessment e.g.

RADPOW, NEPLAN, NetBAS etc.36. Evaluation of using the tool VeFoNet for purpose of

maintenance planning, RCM.37. Evaluation of using the tool NEPLAN for the purpose of

maintenance planning38. Compilation of existing functions for RCM in commercial tools

like NetBas, NEPLAN. Meldis, Maximo etc.

17


4 AM course – Closure

– As a result from the two start-up meetings at KTH and TKK the following material will be distributed:• a list of participants and their selected tasks• details about the course exam

– During the following period of work with individual tasks the lectures will be available to support with material and discussions.

– Time for questions and discussions!!

Asset Management in Power Systems Course schedule for student presentations

Wednesday, the 17th August: Theme 1: Reliability data assessment, reliability modelling and assessment

10.00-12.00 Reliability assessment of surge arresters, Vesa Latva-Pukkila Failure causes in medium voltage networks, Kimmo Kivikko Investigation of the impact of non uniformal distributed failures on customer interruption costs for electrical distribution systems, Patrik Hilber Optimal strategy for variance reducation based on component improtanc measures in electrical distribution systems, Torbjörn Solver Reliability engineering in distribution system planning, Markku Hyvärinen 12.00-13.00 Lunch break 13.00-14.30 A bayesian method for reliability modelling applied to HV circuit breakers, Tommie Lindqvist

MonteCarlo simulation methods for reliability modelling and assessment, Jussi Palola

Theme 2: Reliability centred maintenance for maintenance optimization

Different maintenance strategies and their impact on power system reliability, Anna Brådd Maintenance optimization techniques for distribution systems, Sirpa Repo 14.30-15.00 Coffee break 15.00-16.30 Prioritization of maintenance methods (RCM,CBM,TBM,CM), Sanna Uski RCM applications for overhead lines, Sauli Antila RCM applications in underground power systems, Samuli Honkapuro RCM applications for switchgear and high voltage breakers, Richard Thomas

Thursday, the 18th August: 10.00-12.00 RCM applications for generator systems, Nathaniel Taylor RCM application for LV process electrification and control systems, Helmuth Veiler Theme 3: Condition monitoring and diagnostics methods Condition monitoring of wooden poles, Osmo Auvinen Maintenance and condition monitoring on large power transformers, Pramod Bhusal

Aging phenomena of paper-oil insulation in power transformers, Henry Lågland 12.00-13.00 Lunch break 13.00-14.30 On-line monitoring applications for power transformers, Pekka Nevalainen Tests and diagnostics of insulating oil, Kaisa Tahvanainen Gas diagnostics in transformer condition monitoring, Pauliina Salovaara Dielectric diagnostics measurements of transformers and their interpretation, Xiaolei Wang 14.30-15.00 Coffee break 15.00-16.30 Condition monitoring of generator systems, Matti Heikkilä On-line monitoring applications for secondary substations, Petri Trygg PD-measurements for diagnosis of covered conductor overhead lines, Murtaza Hashmi

Theme 4: Computer tools supporting techniques for maintenance planning

Evaluation of the representation of power system component maintenance data in IEC standards 6180/61968/61970”, Lars Nordström 17.00-21.00 Sauna and dinner in Micronova

Friday, the 19th August: 10.00-12.00 Theme 3: Condition monitoring and diagnostics methods, continued Condition monitoring of high voltage circuit breakers, Tuomas Laitinen On-line condition monitoring of high voltage circuit-breakers, Shui-cehong Kam

Theme 1: Reliability assessment of protection systems, Gabriel Olguin

12.00 The closing of the course

1

RELIABILITY ASSESSMENT OF SURGE ARRESTERS

Vesa Latva-Pukkila Tampere University of Technology

[email protected]

INTRODUCTION Surge arresters are used in electricity networks mainly to protect sensitive network apparatus against overvoltages. The purpose of lightning arresters is to provide a path which the surge can pass to the ground before it has a chance to seriously damage the insulation of the transformer or other electrical equipment. The major change from gapped silicon carbide arresters to gapless metal oxide surge (MO) arresters started some 25 years ago. The shift from porcelain to polymers as housing material started little later, 15 – 20 years ago. These two technology changes have changed the behavior and properties of surge arresters considerably. Today polymer housed metal oxide arrester designs are used in most of the new arrester applications at distribution level and increasingly also at higher voltages. [Lah03] SURGE ARRESTER TYPES In general, there are two types of lightning arresters: gapped silicon carbide (SiC) and gapped or gapless metal oxide (MO). There are also two types of housing materials: porcelain and polymeric. The porcelain housed gapped SiC arresters are the earlier form of lightning arresters, but are still used in the power systems. Today most of the new arresters are polymer housed gapless metal oxide arresters. [Grz99, Lah03, Kan05] The elementary SiC arrester consists of air gaps in series with a SiC resistive element. These are hermetically sealed in porcelain housing. Multiple series of gaps are utilized to improve the gap reliability versus a single gap. [Grz99] When an overvoltage occurs, a SiC arrester will operate and let through a large current caused by the overvoltage. After the overvoltage impulse disappears, the SiC arrester will continue to carry a power-frequency follow current of several hundred amperes for a few milliseconds until a current zero or until the arrester gaps deionize. Energy dissipated in the SiC arrester is mainly due to power-frequency follow currents and not due to surge impulses. [Goe00] The gapless metal oxide arrester is composed of metal oxide varistor blocks in series. MO arresters have extremely non-linear characteristics and when they operate, they let through less power-frequency current than SiC arresters. An MO arrester will let through only the current impulse caused by the overvoltage and does not have power-frequency follow-current. This allows MO arresters with less energy capability to be used when replacing SiC arresters. [Goe00, Kan00]

2

In year 2000, there were approximately 100000 surge arresters in medium voltage power lines in Finland. Of the arresters approximately 55 % were gapped SiC arresters and 45 % gapless MO arresters. Approximately 40 % of MO arresters are porcelain housed and 60 % polymer housed. The age of SiC arresters varied between 15 and 50 years while oldest MO arresters were 18 years old. The share of the MO arresters is estimated to rise to 90 % of all surge arresters before year 2010. [Kan00] Structure of MO arrester The mechanical structure and design of surge arresters naturally varies between arrester types and manufacturers. Structures of porcelain and polymer housed arresters differ significantly. The main principles of the mechanical structures used in polymer and porcelain housed metal oxide surge arresters are illustrated in Fig. 1. The figures were prepared for distribution class arresters, but the same kind of basic structures can also be found in arresters of different voltage levels. [Lah03]

Fig. 1 Basic structures of metal oxide surge arresters (a: porcelain housed MO arrester, b and

c: polymer housed MO arresters) [Lah03] The main differences between the structures in porcelain (Fig. 1 a) and polymer housed (Fig. 1 b and c) arresters are the need for separate mechanical support in polymeric arresters and unavoidable internal gas space and the pressure relief system needed in porcelain arresters. In porcelain housed arresters the housing also serves as a mechanical support. In polymeric MO arresters, due to the elasticity of polymeric housings, a separate support system, formed by a glass fiber reinforced epoxy tube or rods inside an arrester, is needed to take care of mechanical

3

loads. In all, the mechanical structure of porcelain housed MO arresters is more complex and includes more separate parts than polymer housed MO arresters. [Lah03] The structures of polymer housed arresters can be separated into two main categories according to their manufacturing technique; polymeric housing molded directly onto internal parts (Fig. 1 c) and separately molded housing (Fig. 1 b). In the latter case, the arrester is assembled after molding and it needs separate end caps (7) to seal the housing to the end electrodes. [Lah03] In designs with housing molded directly onto the internal parts, the interfaces between the housing and other parts are tightened using primers during the molding process to achieve a chemical bonding between the materials. In separately molded designs, this interface is often filled with an elastic sealing paste to ensure a totally void free structure. In some designs voids and even remarkable gas spaces are found in this interfacial region. [Lah03] Polymer housing versus porcelain housing The change from porcelain as a housing material towards polymeric housings has been the most recent major change in the history of metal oxide surge arresters. The reason for this change is, that the polymeric housings have several advantages over porcelain housings: [Lah03]

• better resistance to harmful effects of pollution • fewer components, lower weight and compact size • faster and more simple manufacturing process, allows more complex geometry of housing • better thermal properties (higher conductivity) • reduced risk of shattering and explosion (increased safety) • better resistance to moisture ingress • less vulnerable to vandalism or mishandling during installation and transportation

Although polymeric materials have many superior properties over porcelain, there are also negative aspects. Generally, polymers are much more complex to use because they may degrade under service stresses when not properly used. Several aspects have to be considered, solved and tested when designing polymer housed high voltage apparatus. Some of these aspects are given in the following: [Lah03]

• weathering degradation is possible o polymers have weaker bonds than porcelain (they can be aged and degraded)

careful material development and product design needed o polymer material formulations are organic and their foundation element, carbon, is

conductive in most of its uncombined forms in case of material degradation conductive paths or tracks may be formed (tracking)

• lower mechanical strength separate mechanical support needed • higher raw material costs • complex material formulation and manufacturing process, material compatibility is not

easy to handle o polymers may suffer from stress corrosion or brittle fracture if not properly

formulated in the specific application

4

Further, the housing of the polymer housed arresters, which fail in networks, frequently remains unbroken and hence the damaged arresters are very difficult to detect. This delays fault location, isolation, and restoration measures in the network. At the moment, there is no simple and reliable means to determine the condition of an in-service medium voltage surge arrester on site. [Kan05] FAILURE MODES OF SURGE ARRESTERS Studies have shown that the vast majority of porcelain distribution arresters failures are moisture ingress related. To prevent moisture ingress, an arrester must have a robust sealing system which completely seals the housing-varistor assembly interface as well as the terminal ends. In addition, the arrester must be essentially void-free to prevent moisture vapor transmission. If not void-free, moisture vapor permeates the housing, condenses and collects in internal air spaces. This means also that polymer arresters with free internal air space, including gapped arresters or designs with an unfilled interface, are prone to premature moisture-induced failure. [Mac94] If one can protect against moisture ingress, then the next likely cause of arrester failure will be excessive energy input. Gapless distribution arresters absorb energy from power frequency temporary overvoltage, which occurs during ground faults, switching surges or lightning discharge. [Mac94] Stresses often act simultaneously and sometimes sequentially, e.g., formation of salts followed by absorption of moisture into the arrester. Additionally, the varistor elements of a MO arrester are continuously subject to considerable voltage stress, and a small leakage current continuously passes through a MO arrester giving rise to new kinds of phenomena unknown in arresters with gaps. [Kan05] This chapter presents shortly the operating environment of surge arresters, typical failure modes of silicon carbide and metal oxide arresters as well as moisture penetration phenomena. Operating environment of surge arresters Arresters encounter a large range of different stresses in their operating environment. Resistance to the harmful effects of these ambient stresses is thus one of the fundamental properties of any arrester. The basic ambient stresses are illustrated in Fig. 2. In case of metal oxide arresters, ambient stresses may degrade arrester either directly (e.g. mechanical forces) or more often indirectly by causing changes in the materials (e.g. UV radiation, acids) or in the electrical behavior (e.g. leakage current formation by humidity ingress or pollution). All these changes often interact with the other degradation processes. Failure of an MO arrester is often finally caused by the electrical stresses also in the case of an environmentally degraded arrester when the weakened arrester can no longer withstand the field strengths needed. [Lah03]

5

Fig. 2 Basic ambient stresses affecting MO arresters in different service conditions [Lah03] Surface contaminations, pollutions and other kinds of layers accumulating on the surfaces, may cause both internal and external problems in arresters. The conductive layer may change the potential distribution and increase the internal leakage current of an arrester. This may lead to uneven heat distribution and overheating of the metal oxide discs. Conductive layers may also cause internal partial discharge activity in arresters with internal gas space. External problems include surface leakage current and partial discharge formation that may lead to material erosion, puncture of the housing and flashovers. [Lah03] Humidity in all forms is more or less conductive. The humidity stressing of arresters is caused by different forms of ambient humidity; precipitation and content of moisture in air. Factors like length and recurrence of moist periods also affect the stressing, likewise the properties of periods with lower air humidity content and precipitation. If humidity penetrates the inside of an electrical insulation it may form conductive paths leading to internal leakage currents and changes in electrical field distribution. Internal leakage currents may also cause tracking and erosion of materials. In silicon carbide type arresters internal humidity has been found to be the most important reason for failures in the field. [Lah03] Failure modes of silicon carbide arresters The most important cause of failures of gapped SiC arresters is internal degradation due to moisture penetration. Other reasons for failures include severe surges, external contamination,

6

damaged air caps due to several events and deterioration of SiC material due to several events of moisture. According to many sources, the dominant cause of failure of gapped silicon carbide surge arresters is internal degradation due to moisture ingress resulting from inadequate seals. Some studies suggest that nearly 86% of all SiC arrester failures could be associated with moisture ingress. Another study showed that moisture was present in 10% of about 3000 arresters examined after about 12 years of service life. [Dar96] Some SiC arrester failures (about 5%) are caused by very severe lightning surges, e.g. strokes of very high current magnitude or long continuing current durations, and/or multiple-stroke flashes with high multiplicity of stroke currents and power-follow currents. [Dar96] Further, some arrester failures are caused by external contamination on the housing. Such failures are more likely for arresters without non-linear resistance grading of the internal gaps. [Dar96] Another reason for failures is several spark-over events caused by lightning surges during the service. Therefore, inside the arresters, the surfaces of air gaps are slightly damaged by arc due to energy dissipation. The inherent smooth surfaces are accumulated by some small particles emitted during arcing. Consequently, the withstand voltage level is reduced as breakdown is easy to be caused via the air gaps. [Grz99] Furthermore, the property of silicon carbide may also be changed. During energy dissipation the silicon carbide sustains a very huge current, and the material structure of SiC has some variations. Moisture may also deteriorate the SiC material, even hermetically sealed in the porcelain housing. [Grz99] Failure modes of metal oxide arresters Metal oxide arresters in service are still relatively new and it seems that there isn’t clear information on their typical aging and failure mechanisms. Naturally severe surges cause failures, but energy related failures are considerably less than 1 % of all failures. The majority are caused by high temporary overvoltages, which exceed arrester limits, and animals. [Goe00] For gapped MO arresters, it is clear that the principal cause of failure is moisture ingress through faulty or inadequate seals on both porcelain and polymer housings. The moisture ingress degrades the series gap structure by either destroying the properties of the insulation associated with the gaps or by damaging the non-linear grading resistors. Eventually all the system voltage is applied to the varistor blocks, leading to their failure and so failure of the arrester. It is clear that the presence of lightning is not a part of this process, but there is evidence that the likelihood of this mode of failure is increased by the occurrence of high temporary over-voltages. [Dar00] Formation of acids and nitrates inside porcelain housed MO arresters with internal gas space may also cause problems. [Kan97]

7

For gapless MO arresters, moisture ingress does not appear to be a significant factor. While the cause of block failure in such arresters is often masked by the damaging effects of fault currents, most of the blocks failed in service by flashover of the surfaces or perhaps near-surface flashovers. This may occur due to high temporary over-voltages or lightning. [Dar00] Moisture penetration The ambient conditions naturally define the external "humidity stress" tending to access inside an MO arrester. The surface properties, like hydrophobicity and roughness, of the housing material affect humidity ingress by affecting the moisture ingress processes. For example, high water repellence due to hydrophobicity limits water permeation. The housing material itself greatly affects moisture permeation. Cracks and other flaws cause direct moisture ingress by capillary effect but these problems are rather rare in modern arrester types. Instead, moisture permeation by diffusion takes place in all polymers if the stresses are high enough. The `recipe' of a housing material defines the permeability of the material to water. [Lah03] In some cases moisture can also be formed internally as a product of chemical reactions initiated by internal discharge activity, initiated, for example, when a conductive layer covers the external surface of an arrester. Internal partial discharge activity is normally possible only in arrester types having internal gas space. These possible moisture penetration or formation processes in polymer housed arresters are illustrated in Fig. 3. [Lah03]

Fig. 3 Moisture penetration and formation possibilities in polymer housed arrester (1 = end

electrode, 2 = MO disc, 3 = mechanical support, 4 = polymeric housing) [Lah03] The internal structure of an MO arrester affects possible moisture problems remarkably. A good design should not leave any places for moisture to collect to (e.g. voids, gas spaces) because moisture diffusion through polymeric housing is possible. In the newest designs, where housing is typically directly molded onto the internal parts of the arrester, the quality of bondings over

8

internal interfaces is of importance. The bonding areas should not be porous and the bondings should withstand the stresses and conditions present inside an arrester throughout its lifetime. These stresses may be very severe, for example the ambient temperature in Finnish Lapland may reach -50°C in winter while the temperature during summertime may reach +30°C. The operation of an arrester naturally heats the arrester up to a higher temperature. [Lah03] In cases where moisture has penetrated an arrester the internal leakage current will start to flow when the arrester is energized and a leakage current path is available. The leakage current may cause tracking of the materials depending on the flowing current and the dissipated power. Degradation caused by the internal leakage current and the humidity can increase the humidity ingress into the arrester interior by weakening the material properties and the internal structure. This degradation may lead to loosening of materials from each other. [Lah03] FAULT RATES AND EXPERIMENTS In general, lightning arresters are designed to service 30 or 40 years in power systems. However, the actual service life greatly depends on the property of the varistor element, the sealing of the arresters and the working conditions such as surge magnitudes, the frequency of surge or fault occurrences. [Grz99] Depending on the source, the annual rate of surge arrester failures varies between 0.1 – 3 %. In some cases, failed arresters have included also relatively new metal oxide arresters. [Kan00] It is a common experience that metal-oxide arresters are more reliable than the technologically outdated gapped SiC arresters. This is to be expected, as the MO arrester (particularly the gapless type) is much simpler than the gapped SiC arrester, Also, they have not been in service much in excess of 10 years, so if any degradation due to age is to occur, this should not be evident at this early stage of their life. Even so, during the 1990’s and only on distribution systems, some electricity companies experienced significant failure rates for a few makes, both porcelain and polymer housed, and gapped and gapless arresters. [Dar00] Still, properly designed and constructed gapless polymer arresters, with no internal air space, can provide significant improvement in overvoltage protection and arrester reliability over porcelain arresters. [Mac94] According to study made by Detroit Edison normal rate of failure for the metal oxide lightning arresters is roughly 2.99/1000 each year. [Boh93] This chapter presents the results of some case studies focused on reliability and failure modes of both silicon carbide and metal oxide surge arresters. Tests of SiC and MO arresters gathered from various 24 kV networks in Finland The devices to be studied were gapped silicon-carbide (SiC) arresters and gapless metal-oxide (MO) surge arresters gathered from various 24 kV networks in Finland (during the years 1999–2000). A total of 246 silicon-carbide arresters (age from 14 to 38 years) and 164 MO arresters (age from 4 to 15 years) were studied. The results of the measurements on MO arresters were

9

compared to the measurements conducted earlier (in 1994) on 16 new, unused MO arresters of the same types as those taken from the field. According to results presented in Table 1, the type (not just the age) of the arrester seems to have considerable influence on the reliability of arrester. For example, the type S1 (manufactured 1969-72) performed well in the tests while the type S5 (1980-85) did not.

Table 1 Results of Finnish tests on silicon-carbide arresters [Kan05] Arrester Manu- AC Voltage Test Impulse Current Test

Type facturing Housing Number Failed [%] Number Failed [%] S1 1969-72 Porcelain 30 1 3.3 30 0 0.0 S2 1980-85 Porcelain 31 3 9.7 31 0 0.0 S3 1973-76 Porcelain 54 4 7.4 54 10 18.5 S4 1970-79 Porcelain 56 29 51.8 11 10 90.9 S5 1980-85 Porcelain 19 1 5.3 17 11 64.7 S6 1967-69 Porcelain 22 3 13.6 5 5 100.0 S7 1962-70 Porcelain 17 0 0.0 13 9 69.2 S8 1971-74 Porcelain 5 0 0.0 2 2 100.0 S9 1976 Porcelain 3 0 0.0 3 0 0.0 S10 1962-74 Porcelain 9 0 0.0 9 4 44.4

A total of 41 gapped SiC arresters out of 246 specimens (Table 1) failed in the ac voltage withstand test. Of the gapped SiC arresters tested 34.5% (85 out of 246, not presented in Table 1) did not pass the lightning impulse (LI) sparkover test, although for most of the “failed” arresters the LI sparkover voltages were only a few kV higher than the required value and, hence, the protective margins were still rather good. [Kan05] The current impulse test of the gapped silicon-carbide arresters resulted in a lot of damage. With many types nearly half or more of the arrester were damaged (Table 1). In many cases the internal failure also caused disintegration of the arrester porcelain covering. Due to the damage and failures in the electrical tests the authors recommend the electricity companies in Finland to exchange their gapped silicon-carbide arresters of types S3, S4, S5, S6, S7, S8, and S10 to new metal oxide surge arrester types in order to reduce the risk of arrester failures and to improve the protection levels in the MV networks. [Kan05] All the ten MO arrester types studied were generally in good condition after being used 4–15 years in Finnish 24 kV networks. Only three specimens out of a total of 164 MO arresters tested could be evaluated to be faulty or near failure (Table 2). Comparisons between the MO arresters gathered from the networks and the unused MO arresters showed that the protection levels of the MO arrester types studied had remained stable after being used 4–15 years in networks. In addition, the ac durability of these MO arrester types had not weakened remarkably during the use in networks. The main information of arresters and test results is presented in Table 2. [Kan05] It was also discovered, that some MO arrester types were undervalued compared to the rather high values of temporary overvoltages typical for Finnish 24 kV networks. [Kan05]

10

Table 2 Results of Finnish test on metal oxide arresters [Kan05] Arrester Manu- AC Voltage Test Impulse Current Test

Type facturing Housing Number Failed [%] Number Failed [%] M1 1984-91 Porcelain 64 1 1.6 64 1 1.6 M2 1986-91 Porcelain 12 0 0.0 12 0 0.0 M3 1987-90 Porcelain 21 0 0.0 21 1 4.8 M4 1990 EPDM 3 0 0.0 3 0 0.0 M5 1991 EPDM 6 0 0.0 6 0 0.0 M6 1990 EPDM 3 0 0.0 3 0 0.0 M7 1991-94 EPDM 9 0 0.0 9 0 0.0 M8 1991-94 EPDM 25 0 0.0 25 0 0.0 M9 1991-95 EPDM 8 0 0.0 8 0 0.0 M10 1992-95 Silicone 4 0 0.0 4 0 0.0 M11 1991-92 Silicone 9 0 0.0 9 0 0.0

According to these measurements, there is no clear correlation between insulation resistance value (measured with a dc voltage of 1000 V) and ac withstand voltage of gapped silicon carbide arresters. Hence, a simple insulation resistance measurement (with rather low test voltage) cannot be used as a reliable method to evaluate the ac voltage withstand level of a gapped silicon-carbide arrester. [Kan05] The silicon-carbide arresters with high PD levels did not show any worse performance in the other tests compared to the arresters of the same type but with low PD levels. In conclusion, there is no correlation between PD levels and the durability or operational condition of gapped silicon-carbide arresters. [Kan05] Tests of SiC arresters gathered from networks in Australia There is clearly an upturn in the incidence of unsatisfactory insulation resistance after about 8 to 10 years of service, and this is followed by a more marked upturn in failure on power frequency tests at about 13 to 15 years of service. The incidence of visible deterioration of internal components, as revealed by inspection, begins to appear significant after only a few years of service, and rises steadily thereafter. [Dar96] There are clear differences in the results for four manufacturers' arresters. Some of the differences are attributable to different ages of the arresters, e.g. in respect of insulation resistance, arresters of one make, having median age of 6 years, had a low insulation resistance failure rate (4%) compared to 32 to 41 % for three other makes, with median ages of 11 to 22 years. The same type of comment can be made (but less clearly so) in respect of the results of power frequency tests. But most of the arresters with high lightning impulse sparkover failures (31 %) were of one make having median age of 6 years (newest). The seal failure rates of all arresters subjected to seal tests were uniformly high among nearly all the makes. The inspection results of the four makes were mostly similar - high percentages (63 to 100%) showing evidence of moisture ingress and significant percentages (11 to 31 %) exhibiting electrical damage. [Dar96]

11

Comparisons were made of test results for arresters from electricity authorities in different climates, but with somewhat similar median lengths of service. These showed that, although there appeared to be a small effect on seal failure rates for service periods of about 7 years, for longer periods of service the effects due to length of service were much greater than any effect due to climate. [Dar96] Of the failed arresters opened for inspection, 74 (about 48% of the 155) arresters showed evidence of moisture-related degradation. As a percentage of the whole sample (365) of arresters included in the project, the percentages of the moisture-related degradation and the moisture-evident numbers were 20% and 7% respectively. Block damage such as surface flashover and block puncture or rupture was also evident in 5.7% of the 365 arresters (13.5% of the inspected arresters). 9 of the 365 arresters showed external flashover marks - 8 were associated with one particular make of arrester. [Dar96] Most gapped silicon carbide distribution lightning arresters of age in excess of 13 to 15 years have characteristics which render them no longer suitable for service on a distribution system and authors recommended that all silicon carbide distribution arresters older than 13 years be progressively replaced by modern metal oxide arresters. [Dar96] Inspection of failed arresters gathered from networks in Australia The inspection of gapped MO arresters failed in service or laboratory tests show that most of failures were clearly caused by moisture ingress through ineffective seals had caused degradation of the internal series gap structures and other components. Even gapped MO arresters with satisfactory test results showed signs of internal degradation due to moisture ingress. Clearly the seals were not adequate. The in-service failure rate of such arresters on one 11kV system was as high as 14%. [Dar00] Internal components of failed polymer housed gapless MO arresters generally showed the effects of power frequency fault currents which damaged the MO blocks and their protective casings to varying degrees and which often ruptured the arrester housings. Internal components only occasionally indicated degradation attributable to moisture ingress. In most cases, it was not obvious why the arresters had failed. The majority showed surface or near-surface damage to the MO blocks which is common in with very high temporary over-voltages (WTOV) and with multipulse (MP) lightning impulse currents. [Dar00] Apart from inadequate seals on gapped MO arresters, the factor that seems to have most influence on the performance of metal oxide arresters in the field is the quality of the surface coatings which surround or encapsulate the MO blocks. [Dar00]

12

Laboratory moisture ingress tests The arresters tested can be divided into three groups according to their internal structure. The arresters with housing molded directly on the MO-active part had the best performance under very humid conditions. In fact the arresters of this typical design are the only ones that passed the long term tests without failures, and which could still be used in the system after the test cycle. This correlates with the good service experience with approx. 50 000 arresters of this design installed in tropical countries since 1994. [Lah99] Other arresters designs seem to have problems with sealing and/or the interfaces between the different polymeric materials. The types with separately manufactured housing showed typically no or very little increase of the internal leakage current before the failure. Therefore detection of the arresters (before failure) seems to be a problem. In the case of the arresters with separately manufactured housing and with clear internal gas space the leakage currents started to rise soon after the beginning of the humidity stress. [Lah99] Comparison of SiC and MO arresters There is little doubt that the frequency of failures for the reasons given varies from system to system because of differences in the frequency of lightning storms, application practices, and a number of other factors including the quality of arrester designs in service on the system; however, the results of most studies appear to fit a similar pattern. The failure causes can be grouped roughly into three categories: 1) moisture leakage and contamination; 2) overvoltages including resonance and switching overvoltages; 3) surges of excessive magnitude and duration. [Sak89] According to inspection of 250 failed silicon carbide arresters from a large system the causes of failure were: [Sak89]

Moisture ingress 85.6% Surges 5.9% Contamination 4.5% Misapplication 2.5% Unknown 1.5%

Moisture leakage and severe contamination should have several times less effect on metal oxide distribution arresters than on silicon carbide arresters. Failures for these two reasons evidently account for about 80% or 90% of total failures of silicon carbide arresters; therefore, metal oxide arresters should be more reliable than silicon carbide arresters from the very important point of view of moisture leakage and contamination. It should be understood, however, that design and rigorous quality control practices may result in very high reliability, and less rigorous practices might result in lower reliability, for either kind of arrester. Both metal oxide and gapped silicon carbide arresters in conventional porcelain housing will fail, once a leak has been established in their sealing system. [Sak89]

13

Metal oxide distribution arresters should be highly reliable in most applications because the arresters are far less likely than silicon carbide arresters to fail as a result of moisture ingress and contamination. Metal oxide arresters are more likely to fail as a result of system overvoltages because they conduct current in response to the overvoltages, and for this reason, somewhat more care must be exercised in application to match the magnitude and time duration of system overvoltages to the temporary overvoltage capability of the arresters. [Sak89] SUMMARY In the last 25 years, the type of the installed surge arresters has changed from porcelain housed gapped silicon carbide (SiC) arresters to polymer housed gapless metal oxide (MO) arresters. In year 2000, approximately 55 % of surge arresters in medium voltage networks in Finland were porcelain housed gapped SiC arresters, approximately 18 % were porcelain housed gapless MO arresters and approximately 27 % were polymer housed gapless MO arresters. The age of SiC arresters varied between 15 and 50 years while oldest MO arresters were 18 years old. Today most of the new arresters are polymer housed gapless MO arresters and older designs will be phased out. Depending on the source, the annual rate of surge arrester failures varies between 0.1 – 3 %. Main cause of failures of SiC arresters is deterioration of internal parts caused by moisture ingress. The gapless MO arresters are clearly more reliable than SiC arresters but possible internal gas space in porcelain and some separately molded polymeric housings increases the possibility of moisture penetration and decreases reliability. For same reason, gapless designs are in general more reliable than gapped (SiC or MO) designs. At the moment, there is no simple and reliable means to determine the condition of an in-service surge arrester on site. The reliability of the surge arresters depends on the property of the varistor element, the sealing of the arresters and the working conditions such as surge magnitudes, the frequency of surge or fault occurrences. Many studies have shown that the manufacturing quality has a lot of influence on the reliability of the arresters and in some cases the type and quality of the arrester seems to be more important than the age of the arrester.

14

REFERENCES [Boh93] Bohmann, L.J., McDaniel, J. and Stanek, E.K. Lightning arrester failure and

ferroresonance on a distribution system. IEEE Transactions on industry applications, Vol. 29, No. 6, November 1993.

[Dar96] Darveniza, M., Mercer, D.R. and Watson, R.M. An assessment of the reliability of in-service gapped silicon-carbide distribution surge arresters. IEEE Transactions on Power Delivery, Vol. 11, No. 4, October 1996.

[Dar00] Darveniza, M., Saha, T.K. and Wright, S. Comparisons of in-service and laboratory failure modes of metal-oxide distribution surge arresters. IEEE Power Engineering Society Winter Meeting, 23-27 Jan. 2000. p 2093 – 2100.

[Goe00] Goedde, G.L., Kojovic, Lj.A. and Woodworth, J.J. Surge arrester characteristics that provide reliable overvoltage protection in distribution and low-voltage systems. IEEE Power Engineering Society Summer Meeting, 16-20 July 2000.

[Grz99] Grzybowski, S. and Gao, G. Evaluation of 15-420 kV substation lightning arresters after 25 years of service. Proceedings of Southeastcon '99, 25-28 March 1999.

[Kan97] Kannus, K., Lahti, K. and Nousiainen, K. Aspects of the performance of metal oxide surge arresters in different environmental conditions. CIRED 97, 2-5 June 1997.

[Kan00] Kannus, K. Keskijänniteverkkojen ukkossuojaus Suomessa – taustaa ja nykytilanne (in Finnish). Lecture notes of Lightning and lightning protection of electric networks, Tampere, Finland, 16 November 2000.

[Kan05] Kannus, K. and Lahti, K. Evaluation of the operational condition and reliability of surge arresters used on medium voltage networks. IEEE Transactions on Power Delivery, Vol. 20, No. 2, April 2005.

[Lah99] Lahti, K., Richter, B., Kannus, K. and Nousiainen, K. Internal degradation of polymer housed metal oxide surge arresters in very humid ambient conditions. IEE High Voltage Engineering Symposium, 22-27 August 1999.

[Lah03] Lahti, K. Effects of internal moisture on the durability and electrical behaviour of polymer housed metal oxide surge arresters. Tampere University of Technology Publications 437. Tampere 2003.

[Mac94] Mackevich, J.P. Evaluation of polymer-housed distribution arresters for use on rural electric power systems. IEEE Transactions of industry applications, Vol 30., No. 2, March 1994.

[Sak89] Sakshaug, E.C., Burke, J.J. and Kresge, J.S. Metal oxide arresters on distribution systems – fundamental considerations. IEEE Transactions on Power Delivery, Vol. 4, No. 4, October 1989.

FAILURE CAUSES IN MEDIUM VOLTAGE NETWORKS

Kimmo Kivikko Tampere University of Technology

[email protected]

INTRODUCTION Failures of hardware may be defined perhaps most commonly in abstract functional terms such as: the loss of ability to perform a required function. Failures of equipment or materials may be defined technically in terms of definitive specification parameters which are not met during established tests or inspections. Failures of complex systems may be defined in terms of system functions which are disabled or degraded by the failure. These failures may also be defined in terms of the degradation or loss of specific functions by the subsystem in which the failure occurred. [5] Some types of failures are classified on the basis of the statistical distributions representing their frequency of occurrence. These include the exponentially distributed random failures having a constant hazard rate, and the Gaussian or Weibull distributed wearout failures having an increasing hazard rate. In tracing system failures, often a primary or root-cause failure (which initiated a series of events leading to the failure of the system), and one or more secondary or contributing failures (failures which are caused directly or indirectly by the root-cause failure), are detected. [5] Failure mechanism defines the physics of the failure. For example, electrical or mechanical overloads which involve a level of stress which exceeds the rated electrical or mechanical strength of an item will cause physical damage resulting in loss of functional capability. The failure mechanism is the process which occurred to change the physical or functional characteristics, or both, of the materials in the failed item. Failure mode is defined as the effect by which a failure is observed to occur and is usually characterized by description of the manner in which failure occurs. It is the description of the failure itself. A failure mode provides a descriptive characterization of the failure event in generic terms, not in terms of the failure mechanism, and not in terms of failure effect. The effects of a failure within a system may be propagated to higher and lower levels of assembly, or the system design may prevent such propagation. POWER SYSTEM FAILURES Power system is composed of a number of components, such as lines, cables, transformers, circuit breakers, disconnectors etc. Every component in a power system has an inherent risk of failure. In addition, outside factors influence the possibility of component failure; e.g. the current loading of the component, damage by third parties (human or animal), trees and atmospheric conditions – temperature, humidity, pollution, wind, rain, snow, ice, lightning and solar effect

1

[1]. On the other hand, a certain component can increase reliability, but on the other hand all components can have failures and thus cause a supply interruption. One important thing in the clarification of different failure causes in medium voltage network is collecting sufficient fault statistics in the company level and in national or international level. Failure causes can also be investigated with e.g. questionnaires. There are a lot of uses for fault statistics. They can be used, for example, in network planning to decide which components and network structures are most suitable in a certain case. In general, a bathtub curve may be used as an adequate representation of component failure mode with varying lifetimes. Early failures occur during the first years, and usually they are caused by inherent defects due to poor materials, workmanship or processing procedures or manufacturer’s quality control beside installation problems. Random failures are produced by chance or operating conditions such as failure from switching surges or lightning. This failure mode is usually present at very low percentages. Wearout failures usually result of dielectric or mechanical material wearout. Normally, the wearout mode becomes predominant after tens of years of operation, and it can be seen in the curve as an increasing failure rate. (See Figure 1)

0 %

20 %

40 %

60 %

80 %

100 %

120 %

1 2 5 5.5 5.7 6 7 10 15 20 25 27 30 35 40Tim e / years

Failu

re ra

te /

%

Early failures

Random failures

Wearout failures

Figure 1: Bathtub curve. One questionnaire about failure causes has been done in USA and Canada by IEEE in 1972 and it offers quite a good overview to different failure causes in power systems. The network components discussed here are transformers, circuit breakers, disconnect switches, cables, cable joints and cable terminations. The failures were predominantly flashovers involving ground, caused by lightning during severe weather or by dig-ins or vehicular accidents. Below is a short summary from [2] about the research:

2

• For transformers, damaged insulation in windings or bushings accounted for the majority of the transformer damages. The insulation can damage e.g. because of lightning or switching surges or due to manufacturing and material errors.

• In the case of circuit breakers the bulk of failures involved flashovers to ground with damage primarily to the protective device components and the device insulation. Many cases also report a circuit breaker misoperation, i.e. the breaker opens when it should not open.

• Electrical defects, mechanical defects and flashovers to ground resulted in damage to mechanical components and insulation in disconnect switches. Some form of mechanical breaking or contact from foreign sources, exposure to dust and contaminants accounted for the majority of cases. There were also many unclassified cases.

• For cables, the majority of failures involved flashovers to ground, resulting in insulation damage. The flashovers can be due to e.g. transient overvoltages, mechanical failures or normal deterioration.

• In the case of cable joints and terminations the primary damage was insulation involving either a flashover to ground or other electrical defect. Causes for the faults were abnormal moisture, exposure to aggressive chemicals, inadequate installation, severe weather and normal deterioration were the most common failure causes.

FAILURE CAUSES Failures can originate from several different causes. Below is a list about different failure causes and how they affect the network components. Figure 1 presents the failure causes and their percentages according to Finnish interruption statistics. This figure relates to the number of faults causing customer interruptions (see Figure 2).

Other weather2 %

Animals4 %

Faulty operation or improper materials

11 %

External6 %

Unknown17 %

Snow and ice13 %

Wind and storm35 %

Thunder and lightning

12 %

Figure 2: Failure causes and their percentages in Finland in 2003 [4].

3

Lightning Lightning strikes can be either direct, which strike directly to a phase-conductor in the network, or indirect strikes, which strike near the network and cause an induced overvoltage. These overvoltages can be even thousands of kilovolts in the case of direct lightning strikes and 500 kV in the case of indirect strikes, and thus they cause much higher voltage stresses to the network components than the normal operating voltages. Even surge arresters are used, these overvoltages can cause failures due to insulation breakdowns in transformers, insulators, cables etc. Also back flashovers i.e. flashovers due to a lightning strike in a grounded part in the network can cause failures [3]. According to the Finnish interruption statistics from year 2003, about 12 % of faults were caused by thunder or lightning [4]. Wind and storm Winds and storms usually cause faults that originate from trees. Trees falling to the lines can cause conductor breakage or poles falling down, and short-circuits or earth faults, which lead to customer interruptions. Also tree branches falling to lines or transformers can cause short-circuits. According to the Finnish interruption statistics from year 2003, about 35 % of faults were caused by wind and storm [4]. Snow and ice Snow and ice cause a little similar failures as wind and storm. Snow burden can cause the trees to fall to the lines and they cause conductor breakage or short-circuits or earth faults. On certain weather, conductors collect frost and ice, and they can break the conductors because the conductors can’t take the weight of the ice. According to the Finnish interruption statistics from year 2003, about 13 % of faults were caused by snow and ice [4]. Other weather dependent cause Other weather dependent cause includes those weather dependent failures that can’t be categorized to any other class. These are, for example, extremely heavy rain that causes floods in the basements etc. 2 % of the faults were classified to this category in Finland in 2003 [4]. Animals Animals can cause short-circuits and earth faults. Squirrels may go on the top of transformers or between line insulators and cause faults that way. Also birds sometimes fly to the lines and cause faults. 4 % of faults in Finland in 2003 were caused by animals [4].

4

Network owner’s actions • About 11 % of faults in 2003 in Finland were due to network owner’s actions [4]. • Neglected maintenance: Proper maintenance can increase the lifetime of network

components. Neglected maintenance can lead to premature failures that could have been avoided with maintenance. This includes also line faults due to neglected tree trimming.

• Planning or installation error: This category includes failures caused by planning or installation errors. Usually these are cases where a wrong type of component is planned to be installed in the network.

• Faulty operation can cause failures, for example, if disconnector is used to break high currents.

• Overload: If overload causes component failures, it usually is a consequence of planning error or faulty operation.

External people actions

• According to the Finnish interruption statistics from year 2003, about 6 % of faults were caused by external people actions [4].

• Digging: Even though distribution network companies usually have cable location services, digging sometimes causes cable failures.

• Vandalism FAILURE CAUSES BY COMPONENT Above the failure causes were classified by the cause of the failure. Below the classification is made by the component, and more details about failure mechanisms and causes are presented for each component. In Finland failure causes are not collected in the national level, but FASIT statistics [7] from Norway give some kind of picture about the meaning of different failure causes in the case of different components. Transformers Transformers can fail in a variety of ways and for a variety of reasons. Important factors are design weaknesses, abnormal system conditions, aged condition, pre-existing faults and timescales for fault development [8]. Most transformers in medium voltage network are paper/oil insulated. In addition to the stresses caused by persistent operating voltage, the insulations get older due to high temperatures caused by load and fault currents, humidity and mechanical stresses caused by e.g. fault currents. Small discharges in the insulations weaken the paper insulations and dissolve gases in oil. According to the gases dissolved in the oil the condition of oil can be investigated. The condition of the paper insulations can be detected with e.g. furfural analysis. There are also other methods like partial discharge measurements, infra red emission tests and acoustic emission tests.

5

Bus bars In the case of bus bars the leading contributor was exposure to moisture (30 %) in the insulated bus category and exposure to dust and other contaminants (19 %) in the bare bus category. Other major causes were normal deterioration from age, shorting by tools and metal objects (bare buses), mechanical damages and obstruction of ventilation [6]. Moisture can cause corrosion to the components, which may damage the insulations so that e.g. SF6 gases leak from the bus bar shielding. Dust and other contaminants may decrease the breakdown voltage causing unwanted breakdowns in the insulations. Circuit breakers and disconnectors According to [6], in the case of circuit breakers the most common failure contributors were lubricant loss, lack of preventive maintenance, overload and normal deterioration from age. Lubricant loss can damage the breaker mechanically when stuck mechanisms do not work properly. In the case of oil filled circuit breakers oil loss can damage the live parts of the breaker, when arc remains burning after the breaker operation. Overload is usually caused due to planning error when the circuit breaker has too small rated current compared to fault currents or load currents. Reasons for inadequate circuit breaker sizing can also be due to load growth, network development or unusual switching state. Normal deterioration causes also failures. Circuit breakers have a limited number of breaker operations, even though the circuit breaker was adequately maintained. The number of breaker operations depend e.g. on breaking currents and many other things. These failures may be also due to minimizing the assets in the network, when old components are not replaced before failure. Same failure causes apply usually for disconnectors, too. Failure causes according to FASIT statistics are shown in Figures 3 and 4.

Unknown, 87.2 %

Stand/rack, 0.0 %Mechanics, 5.1 %Base, 0.0 %

Several components, 0.0 %

Isolation to ground, 2.6 %

Grounding, 0.0 %

Live part, 5.1 %

Figure 3: Circuit breaker failure causes (2003) according to FASIT statistics [7].

6


Base, 0.0 %

Unknown, 45.6 %

Stand/rack, 1.4 %

Mechanics, 0.8 %

Live part, 17.6 %

Grounding, 1.4 %

Isolation to ground, 32.5 %

Figure 4: Disconnector failure causes (2003) according to FASIT statistics [7].

Overhead lines Most overhead line failures are weather or environment dependent. These are e.g. trees falling to the lines, which causes conductor and pole damages, lightning, snow and ice, and animals.

Unknown, 32.4 %

Phase lines, 50.6 %

Isolator, 9.6 %Pole, 1.5 %

Jumper, 2.1 %

Clamp, 0.8 %

Crossarm, 0.4 %Shield line, 0.1 %

Joint, 0.2 %

Spark gap, 0.3 %

Stay wire, 0.1 %

Base, 0.1 %

Anchoring, 0.1 %

Grounding, 0.4 %

Binding, 1.5 %

Figure 5: Overhead line failure causes (2003) according to FASIT statistics [7].

7

Cables The majority of cable failures are mainly due to natural aging of insulation, cable imperfections or water treeing. Other failure modes involve corrosion or damage to the concentric neutral and metallic ground shields, and the loss of good contact between metallic shield and the semiconducting shield. Under certain conditions corrosion/breaks in the metallic shield or poor contact between metallic shield and semiconducting shield will lead to arcing damage at metallic shield/semiconducting shield interface which progresses into the insulation until failure occurs [9]. Digging also causes cable failures. Even though the cable would not be totally broken, the insulation or the jacket may be injured. This may lead to e.g. ingress of moisture and chemicals which will lead to water treeing or insulation breakdown.

Cable terminal, 16.8 %

Cable shoe, 3.7 %


Cable, 41.9 %Unknown, 30.4 %

Trifurcating joint, 1.6 %

Joint, 5.2 %

Figure 6: PEX cable failure causes (2003) according to FASIT statistics [7].

8

Cable terminal, 13.2 %

Cable shoe, 0.8 %


Cable, 35.7 %Unknown, 38.4 %

Joint, 8.5 %

Trifurcating joint, 3.5 %

Figure 7: Paper/oil cable failure causes (2003) according to FASIT statistics [7].

NORDIC GUIDELINES FOR FAULT STATISTICS Nordic countries have long traditions in co-operation in the field of electricity transmission and distribution, and currently there is also going on a research on pan-Nordic regulation models. Probably in the future there will be even more co-operation between Nordic countries. One possibility for this the development of common Nordic fault and interruption statistics. Opal In order to give the network companies (and others) a better decision basis for investments, operation, maintenance and renewal of the networks a common Scandinavian project called OPAL (Optimization of reliability in power networks) was launched in 2002. The objectives of the project are:

• Development of common Scandinavian guidelines for collecting and reporting fault data • Contribute to a better utilization of fault and interruption data • Development of methods for calculation and analysis of reliability of supply in power

networks One of the activities in the project has been to specify a common database for faults and interruptions. The content of the future database is presented in Table 1.

9

Table 1: The content of a future Scandinavian fault database [10]. Identification of the disturbance Event identification Id number Time of event Date and time Information about the fault(s) Fault number Within the disturbance (1, 2, 3, etc) Reference to disturbance Reference to event id number Network company Chosen from predefined list of companies Network area Location of the fault picked from predefined list of areas Component id Unambiguous component id, picked from company specific list of components Geographical location Chosen from list defined by each company Network type Cable network (> 90 % cable), overhead network (> 90 % overhead line), mixed network (remaining) Faulty component Breaker, transformer, overhead line, cable, protection equipment, etc. Misc. information about component Placing, function, type, manufacturer, capacity, year of installation, etc. Faulty sub-component Specific choices for each component System voltage kV Earthing system Impedance, direct, isolated Fault type Earth fault, short circuit, open circuit, missing operation, unwanted operation, etc. Primary or secondary fault Secondary fault includes succeeding and latent fault Fault character Permanent, temporary, intermittent Main fault cause Specific choices grouped under lightning, other environmental causes, external influence, operation and

maintenance, technical equipment, other, unknown Underlying/contributing fault cause Same choices as for the main fault cause Repair time Including fault localization Repair type Component replaced, permanently repaired, provisionally repaired, no repair Information about the consequences Interrupted power kW Energy not supplied kWh Number of affected customers Integer Number of affected delivery points Integer Customer interruption duration Aggregated time for all affected customers Delivery point interruption duration Aggregated time for all affected delivery points Total interruption duration Time from first customer is interrupted to the supply is restored to the last customer Disconnection type Automatic, automatic with unsuccessful automatic reconnection, manual, none Reconnection type Automatic, manual, none

NorStat NorStat working group was founded in April 2005 to continue the development of common Nordic guidelines for fault and interruption statistics and to take care of the implementation of these statistics. The main assignment is further developing and maintenance of the guidelines for registration of faults for network between 1 and 100 kV. The group is responsible for collecting fault data and composing an annual publication of Nordic fault and interruption statistics. The long-term goal is to collect all data in a common database, where Nordic network companies could get the statistics they want through a web-application. REFERENCES [1] Lakervi, E., Holmes, E. J. Electricity distribution network design. IEE Power Series 21.

Peter Peregrinus Ltd. 1998. [2] IEEE Recommended Practice for the Design of Reliable Industrial and Commercial

Power Systems. IEEE Std 493-1997. ISBN 1-55937-969-3.

10

[3] Aro, M., Elovaara, J., Karttunen, M., Nousiainen, K., Palva, V. Suurjännitetekniikka. Espoo 1996. 483 p. (in Finnish)

[4] Keskeytystilasto 2003. Sähköenergialiitto Sener ry. 21 p. (in Finnish) [5] IEEE Guide to the Collection and Presentation of Electrical, Electronic, Sensing

Component, and Mechanical Equipment Reliability Data for Nuclear-Power Generating Stations. IEEE Std 500-1984.

[6] Paoletti, G., Baier, M. Failure Contributors of MV Electrical Equipment and Condition

Assessment Program Development. IEEE Transactions on Industry Applications, Vol. 38, No. 6, November/December 2002.

[7] www.fasit.no [8] Lapworth, J., McGrail, T. Transformer Failure Modes and Planned Replacement. IEE

Colloquium on Transformer Life Management (Ref. No. 1998/510). [9] Abdolall, K., Halldorson, G., Green, D. Condition Assessment and Failure Modes of

Solid Dielectric Cables in Perspective. IEEE Transactions on Power Delivery, Vol. 17, No. 1, January 2002.

[10] Heggset, J., Christensen, J., Jansson, S., Kivikko, K., Heieren, A., Mork, R. Common

Guidelines for Reliability Data Collection in Scandinavia. Proceedings of Cired 2005.

11

http://www.fasit.no/

EFFECTS OF CORRELATION BETWEEN FAILURES AND POWER CONSUMPTION ON CUSTOMER INTERRUPTION COST

a paper for the course:

Asset Management in Power Systems

Patrik Hilber

Royal Institute of Technology [email protected]

Abstract The concept of this paper is to study the correlation between failures and power consumption for the Kristinehamn network. This is done in order to scrutinize whether the commonly used assumptions of constant failure rate and constant power consumption is reasonable to use for reliability calculations. The studied entity is energy not delivered, which is assumed to be a good estimate of how customer interruption costs are affected (by the assumptions).

• The effect of the seasonal variations is that the “constant case” underestimates the energy not delivered by approximately 2 %.

• The effect of the daily variations is that the “constant case” underestimates the

energy not delivered by approximately 6 %.

• The effect of repair time variations is that the “constant case” overestimates the energy not delivered by approximately 1 %.

The conclusion of this paper is that by using constant failure rates, repair rates and power consumption the approximation of customer costs becomes somewhat low, i.e. by 7 % for the studied case. This result indicates that the assumptions of constant failure rates, repair rates and power consumption are quite sufficient for at least the actual case study. This since this error probably is significantly smaller than other types of errors, for example customer outage costs estimates. Nevertheless, having performed these calculations the current results should be applied to further modeling of the Kristinehamn network.

1 Introduction Customer interruption cost due to loss of supply is of crucial importance for electrical network owners. Crucial since this cost can be used as a measure of the reliability worth of a network [1]. One example of this focus on customer interruption cost is the fact that the Swedish Energy Agency, the government body that regulates network tariffs, will apply a newly developed “network performance assessment model” for determining the maximum tariffs. In this model, one of the most important factors is customer interruption cost. The concept of this paper is to study the correlation between failures and power consumption for the Kristinehamn network. This is done in order to scrutinize whether the commonly used assumption of constant failure rate and average power consumption is reasonable for the calculation of customer interruption costs. Similar studies have been performed, for example presented in [2] and [3]. In [2], one major conclusion is that by using time varying interruption costs, the expected interruption cost for the industrial sector becomes significantly lower. However, the generally used customer damage function [2] undervalues the interruption cost compared to a method based on normal distributed cost factors. In [3] the interruption cost for customers is studied with respect to varying repair rates, failure rates and loads. It is noted that such data can be used for example for decision when maintenance shall be performed. It is concluded that using constant interruption cost rates significantly underestimates the average interruption cost. This paper perform a similar study but on an additional network (Kristinehamn) and with different data and with a somewhat different approach and hence complement the earlier work within this area. This study also validates and scrutinizes the assumption of average power and constant failure and repair rate for the calculation of customer interruption cost, which is an assumption made in [4].

2 The network The Kristinehamn 11kV-network for which this study has been performed is located in the western parts of Sweden. The network is depicted in Fig. 1. The urban network consists of underground cables of approximately 170 km and the rural network comprises seven separate overhead line systems with a total line length of 110 km [5]. Although the overhead line systems are operated as radial, it is possible to close cross connections between some of them during disturbances in order to shorten customer outage times. The urban part of the network allows for many opportunities for reconnection in the case of failures of the normal feeding. In table I some additional data for the network can be found.

Figure 1. The Kristinehamn network [5]. Table I. Data for the Kristinehamn network [5].

Number of customers 10 900Energy, GWh/yr 230

Investments 2004, kkr 6 364 Operation and Maintenance 2004, kkr 6 810

Interruptions per customer and year1 0.82 Average interruption time per customer and year1, min 42

Underground cable, km (incl. 0.4 kV) 684 Insulated overhead line, km 108 Uninsulated overhead line, km 106 Total line length, km 898 Uninsulated to insulated conductor for year 2004, m 7200 1 Planed and unplaned interruptions for 2004.

3 Approach/Method A common assumption in reliability calculations is that failure rate and consumption are evenly distributed over time. This has an effect on the estimates of customer interruption cost. The approach of this paper is to study the effect on customer interruption cost of that consumption and failure rate to a certain degree coincide. This is performed in order to evaluate the above-mentioned assumption. The data used in this paper are based on an extensive study of the Kristinehamn network. presented in [5]. The data for failure and repair rates have been collected over a period of 10 years, while data for 3 years of power consumption has been used. Failure rate, repair rate and consumption has been divided up into time intervals. Based on these intervalls expected undelivered energy is calculated and compared with the corresponding values calculated from the assumed constant values of load, failure rate and repair rate. Three different aspects are studied:

1. Effects of seasonal variations. 2. Effects of daily variations with respect to load and failure rate. 3. Effects of daily variations with respect to load and repair rate.

For each aspect a percentage is established which indicates how much the “constant case” ought to be modified with in order to reach a better approximation of actual customer interruption cost (or actually accommodate the specific aspect). The percentage is established by identfying the expected averages from the constant case and the case based on distributions for load and failure and repair rates. The studied entity in this report is energy not delivered. It is assumed that the relative difference for this entity is a good approximation of the differences for customer interruption costs.

4 Results

4.1 Effects of seasonal correlated power consumption and failure rate

By dividing the power consumption and failures per month the effect of seasonal variations can be studied. In Fig. 2 the total number of failures over the studied 10-year period is allocated to the month where the failures occurred. In Fig. 3 the average energy consumption per month is shown. The effect of the seasonal variations is that the “constant case” underestimates the energy not delivered with 2 %.

0

10

20

30

40

50

60

70

80

90

1 2 3 4 5 6 7 8 9 10 11 12

Month

Num

ber o

f fai

llure

s

Figure 2. Number of failures per month.

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12

Month

Ave

rage

ene

rgy

cons

umpt

ion

[GW

h]

Figure 3. Average energy consumption per month (2000-2002).

4.2 Effects of daily correlated power consumption and failure rate

By dividing the power consumption and failures per hour of day the effect of daily variations can be studied. In Fig. 4 the total number of failures over the studied 10-year period is allocated to the month where the failures occurred. In Fig. 5 the average power per hour of day is shown. The power data is without the power drawn by larger industries.

The effect of the daily variations is that the “constant case” underestimates the energy not delivered with 5 %. If larger industries are incorporated the percentage becomes approximately 6 %.

0

0.005

0.01

0.015

0.02

0.025

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of day

Failu

re ra

te p

er h

our [

f/h]

Figure 4. Failure rate per hour.

02000400060008000

100001200014000160001800020000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of day

kW

Figure 5. Average hourly power for Kristinehamn over three years (2000-2002) [4]. Note that the power consumption for industries not is included in the plot.

4.3 Effects of daily power consumption and repair time In this section the implications of that the repair rate varies with the hour of day is studied. The repair rate can in general be said to be higher when the most failures occur and at high demand for the Kristinehamn network. In Fig. 6 the average repair time per hour can bee seen. It is interesting to note that the longest repair times occur closely to midnight (this might be explained by that Kristinehamn is infested with ghosts that makes the repair work harder to perform). The correction caused by the repair time becomes –1 %, i.e. the constant case overestimates the costs slightly. Hence this factor has a to some extent canceling effect on the two previously presented factors. This result might be somewhat surprising, at a first glance the reader might be conceived to believe that the percentage ought to be lower, since the duration of interruption has a close to opposite look as the power consumption (i.e. low power consumption corresponds to a long interruption durations and vice versa), by looking at Fig. 5 and Fig. 6. However, since not many failures occur during nighttime, as seen in Fig. 4, the actual effect of the variation of repair time becomes relatively small (1 %).

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of day

Ave

rage

dur

atio

n of

inte

rrup

tion

(h)

Figure 6. Average duration of interruption as a function of hour of day.

4.4 Total effect of studied correlations When weighed together the total correction becomes 7 %, i.e. when considering the effects presented in 4.1, 4.2 and 4.3 simultaneously. It is interesting to note that this result concurs with the results in [2] and [3], i.e. by taking distributions into account the expected result becomes higher.

5 Conclusion The conclusion of this paper is that by using constant failure rates, repair rates and power consumption the approximation of customer costs becomes somewhat low i.e. by 7 % for the studied case. This result indicates that the assumptions of constant failure rates, repair rates and power consumption are quite sufficient for at least the actual case study. This since the actual error probably is significantly smaller than other types of errors, for example customer outage costs estimates. Nevertheless, having performed these calculations the current results should be applied to further modeling of the Kristinehamn network. Furthermore this paper points out the fact that it is important to scrutinize assumptions generally made in reliability modeling.

6 Discussion and future work Today maintenance is performed mainly during daytime, with corresponding high interruption costs, the benefits of performing maintenance during other hours of the day might be interesting to analyze. In this study three factors have been studied (hour of day for repair rate and for failure rate and seasonal variations). There might be other factors not analyzed in this paper that might prove important, for example This study has gone deeper in the study of the mechanisms causing interruption costs than what is usually done in reliability calculations. However, it is still possible to investigate further in the topic, e.g. by studying all individual failures and calculate their individual cost. By such an approach the “constant case” could be evaluated even more thoroughly. Another approach for more detailed studies is to use Monte Carlo simulation techniques. For example different types of customers and their specific load patterns could be combined with varying failure and repair rates. This in order to establish for example distribution curves for customer interruption costs and a more accurate estimate of the total expected interruption cost.

References [1] Billinton, R. and Allan R. N., (1996). Reliability evaluation of power systems. 2nd

ed. Plenum Press, New York. [2] Jonnavithula, A. and Billinton, R. Features that influence composite power system

reliability worth assessment. IEEE Trans. Power Systems vol 12, No. 4 1997. [3] Kjølle, G. H., Holen, A. T., Samdal, K. and Solum, G. Adequate interruption cost

assessment in a quality based regulation regime. Porto Power Tech Conf. 2001, Portugal.

[4] Hilber, P., (2005) “Component reliability importance indices for maintenance optimization of electrical networks”, Licentiate thesis, ETS, KTH. Usab ab. ISBN 91-7178-055-6

[5] Hällgren, B. and Hilber, P. 2004, ”An optimal plan for overhead line replacement in Kristinehamn – background material”, in Swedish, department of Electrical Engineering, KTH, Sweden.

[6] Tapper, M. et al, 2003. ”Electric power interruption costs 2003”, in Swedish. Swedenergy 2003.

1

OPTIMAL STRATEGY FOR VARIANCE REDUCTION BASED ON COMPONENT IMPORTANCE MEASURES IN ELECTRICAL DISTRIBUTION

SYSTEMS

Torbjörn Solver School of Electrical Engineering, KTH

[email protected]

INTRODUCTION

Reliability analysis conducted using Monte Carlo simulations (MCS) techniques can be effective within most areas where reliability is of the essence. One such is area is RCAM, Reliability Centred Asset Management, where the objective is to manage the assets of a system based on the system components’ lifecycle, i.e. their tendency to fail, the ability to repair and maintain them, and the system’s function. The assets of the system can basically be anything, technical, financial or human assets, however the most straight forward approach is within technical systems, such as power system where reliability analysis is an important and powerful tool to plan investments, reinvestments and maintenance, i.e. manage the assets. This assignment focuses on how to make the reliability analyses faster and more accurate.

The data processing capability of modern computers and its current development rate enables advanced systems and techniques to be evaluated and analysed by using MCS. However, even though computers are becoming faster and more effective in handling large quantities of data and more calculations, there should always be a strive to decrease simulation time by minimizing the number of samples while keeping the estimation variance low. This can be achieved first of all by making the evaluated systems as simple as possible and also to apply some kind of variance reduction technique.

In this assignment importance sampling is used as variance reduction technique. As the name states; the key for successful importance sampling is to know which components that are important for the system’s function. If the technique is performed poorly the effects can be the opposite of what was intended, i.e. the variance increases and more samples are needed for a sufficiently accurate result then would have been needed if simple sampling was used. Further, there are several indices for component importance with different focuses on the importance aspects. The goal of this assignment is to investigate the connection between some of these indicators and how to optimally weight of system components and thereby be able to identify a strategy for importance sampling.

MONTE CARLO SIMULATIONS

The general concept of MCS methods is to use random numbers to generate the system’s possible states, this procedure is then performed for a sufficient number of times, samples, in order comprehend the system’s stochastic behaviour [1]. In order to put light on the fundamental differences between stochastic methods such MC and the standard analytical method the following example can be told.

2

Example 1: Determine bull’s eye’s area percentage of a dart board.

The analytical approach to determine this would be to measure the diameter of the dartboard and the bull’s eye, then calculate their areas and thereby be able to calculate the bull’s eye percentage.

The MC approach on the other hand would be to let a computer throw a sufficient number of darts and if we assume the darts are evenly distributed over the board and only the dart board, we would be able to calculate the bull’s eye percentage simply by calculating how many out of the total number of darts that hit the bull’s eye.

The MC solving method for a simple example such as this seems quite complicated, and it is. But the fact is that main advantage with MC methods is its suitable usage in situations where the analytical methods becomes complicated, either due to the sheer size of the system or because more than the mean output value is of interest, e.g. the variance and the distribution.

In the previously mentioned dart example, it is quite obvious for those familiar with dart that it would take a significant number of darts in order to achieve an accurate result. The variance in the estimation of a stochastic variable Y and =, can be calculated as [2]:

nYVar

mVar Y][

][ = 1

Where mY is the mean value of Y, and n is the number of randomly selected samples of Y, i.e. y1,y2,…,yn. The variance of the estimation decreases if the number of samples increases, which seems quite natural. The following example will illustrate how the estimated value can depend on the number of samples.

Example 2: Consider the systems illustrated in figure 1, determine each systems’ unavailability.

P1 = 0.9 P2 = 0.9

P1 = 0.9

P2 = 0.9

P1 = 0.9 P2 = 0.9

P1 = 0.9

P2 = 0.9

System 1

System 2

Figure 1. System with components in series and in parallel, 1 and 2 respectively

Both systems contain two components, that has the availability of 0.9. Each system can consequently have four states. The probability of each system’s states and the systems’ function is presented in table 1, 1 indicates that the component/system is working and 0 the opposite.

3

Table 1. States and probability of the system illustrated in figure 1.

Component 1 Component 2 Probability System 1 System 2 1 1 0.81 1 1 1 0 0.09 0 1 0 1 0.09 0 1 0 0 0.01 0 0

Analytical solution, the systems’ unavailability can be calculated as:

Usys1 = 1 – (1×0.81 + 0×0.09 + 0×0.09 + 0×0.01) = 0.19

Usys2 = 1 – (1×0.81 + 1×0.09 + 1×0.09 + 0×0.01) = 0.01

If we perform MCS on the same systems for different number of samples we can se how the estimation of the unavailability changes. Examples of such results are illustrated in figure 2 and 3. The expected value of the unavailability fluctuates for low number of samples, but seems to be converge towards the analytically calculated values of 0.19 and 0.01 for system 1 and 2 respectively. The simulation of system 2 seems to require more samples in order to converge properly.

500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Number of samples

Sys

tem

una

vaila

bilit

y

SimulatedAnalytical

Figure 2. System 1 of example 2, simple sampling with number of samples raging from 1 to 5000

4

500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0.02

Number of samples

Sys

tem

una

vaila

bilit

y

SimulatedAnalytical

Figure 3. System 2 of example 2, simple sampling with number of samples raging from 1 to 5000

Generally, the more complex the system is the more samples are needed, also, safe systems, high availability, requires large numbers of samples to in order to get a sufficiently good representation of the system. In other words when performing MCS on large power systems, there is a need to keep the number of samples low and as mentioned earlier one way to do this is the use variance reduction techniques. The variance that is referred to is the variance of the estimated value presented in equation 1, not the variance of the variable. The variance reduction technique used in this assignment is importance sampling.

Importance Sampling

The aim of importance sampling is to improve the estimation by concentrating the samples to the interesting part of the population. This is achieved by using another probability distribution than the real.

Principle [2]: Let Y be a random variable with a known density function fY defined on the sample space ϒ. We seek E[X], X = g(Y), but instead of sampling X we introduce Z, with the density function fZ, which is called the importance sampling function. fZ should be such that fZ(ψ) > 0 ∀ ψ ∈ ϒ. For each outcome Y = ψ we have

)(ψgX = , 2

)()(

)(ψψψ

Z

Y

ff

gZ = 3

We can verify the E[Z] equals the E[X] [1]:

∈ϒ

=ψ

ψψψ dfgXE Y )()(][ 4

5

∈ϒ∈ϒ

==ψψ

ψψψψψψψψ dfgdf

ff

gZE YZZ

Y )()()()()(

)(][ 5

If we can return to the dart board example, importance sampling applied to that problem would mean that we simply enhanced the size of the bull’s eye. By doing this more darts would hit the bull’s eye and if we gave the darts not hitting the bull‘s eye higher weight than those hitting it the result would be as good as the simple sampling solution, but the number of darts would be less.

Now, by applying importance sampling onto example 2, illustrated in figure 1 we can observe how importance sampling improves the result in such way that fever samples are needed, figure 4 and 5. The components unavailability used in the importance sampling are presented in table 2.

100 200 300 400 500 600 700 800 900 10000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Number of samples

Sys

tem

una

vaila

bilit

y

Imp. samp.SimpleAnalytical

Figure 4. System unavailability of system 1 in example 2, two components in series.

6

100 200 300 400 500 600 700 800 900 10000

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

Number of samples

Sys

tem

una

vaila

bilit

y

Imp. samp.SimpleAnalytical

Figure 5. System unavailability of system 2 in example 2, two components in parallel.

Table 2. Real and sampled unavailability for example 2.

System 1 System 2

Component unavailability (real)

0.1 0.1

Component unavailability (imp. sampled)

0.5 0.9

The components’ weighted unavailability used in for the importance sampling of example 2 are chosen arbitrary, not at random and not optimally. What can be seen in figure 4 and 5 is that importance sampling seems to be very effective for parallel structures.

COMPONENT IMPORTANCE

In any system containing more than one component, some components are more important for the system’s function than the other(s). For instance, a component in series with the rest of the system, in a cut set of order 1, is generally more important then a component in parallel with others, i.e. part of cut set of higher order. There are several component importance measures, some will be used in this assignment1. Component importance measures are primary used to rank and classify the components within a system. The application of using the importance measures to indicate how to weigh the components when performing MCS with importance sampling as variance reduction is not a general application.

1 The included component importance measures were included, due to their suitability to systems structured as in this assignment and due to the fact that the author is familiar with these measures.

7

In this section the we will consider n independent components with the probabilities pi(t), i = 1…n, at the time t. The system reliability is h(p(t)).

Birnbaum’s measure

Definition [3]: Birnbaum’s measure of importance of component i at time t is:

nitpth

tiIi

B ,...,2,1for)())((

)|( =∂

∂= p 6

Birnbaum’s measure is basically a sensitivity analysis of changes in system reliability due to component i. If IB is large, a rather small change in component i’s reliability will have large consequences on the system reliability at the time t. By using tree notation and pivotal decomposition on the definition of IB, Birnbaum’s importance measure can be written as [3]:

))(,0())(,1()())((

)|( ththtpth

tiI iii

B ppp −=∂

∂= 7

Note in equation 7 that Birnbaum’s importance measure of component i only depends on the structure of the system and the reliability of the other components.

Critical importance

Definition [3]: The critical importance measure ICR(i | t) of component i at the time t is the probability that component i is critical for the system and is failed at the time t, when we know that the system is failed at the time t .

))((1

))(1()|()|(

thtptiI

tiI iB

CR

p−−×= 8

Critical importance measure suitable for prioritising maintenance measure, it is in other words the probability that component i caused the system failure.

Fussel-Vesely’s measure

Definition [3]: Fussel-Vesely’s measure of importance , IFV(i | t) is the probability that at least one minimal cut set that contains component i is failed at the time t, given that the system is failed at the time t.

A cut set is failed when all components in it are failed.

)(

)(ˆ

))(Pr())(Pr(

)|(0

1

tQ

tQ

tCtD

tiI

m

j

ij

iFV

=≈= 9

8

Where Di(t) states that at least one of the minimal cut set containing component i has failed at the time t, C(t) states that the system is failed at the time t. )(ˆ tQi

j denotes the probability that the minimal cut set j, contains component I, is failed at the time t. Q0(t) is the system unavailability, i.e. 1 - h(p(t)). How to derive equation 9 can be found in [3].

TEST SYSTEM

The test system used to analyse the optimal weighting of components based on component importance measure is shown in figure 6. It is a substation 40/10 kV, containing of primary as well as secondary side breakers (B) and bus bars (BB) and two parallel transformers.

B1

B1 B4

B3T1

T2

BB1 BB2

40 kV 10 kV

B1

B1 B4

B3T1

T2

BB1 BB2

40 kV 10 kV

Figure 6. Test system for example 3, 4 and 5

The system is analysed in three alterations, one with single bus bars but double transformers, one with double transformers and bus bars and one example with a single transformer and double bus bars. All alterations are assumed to be systems of independent components. The systems are fully automated and operations such as switching between bus bars do not cause any interruption. When there are two transformers in the system, the substation is operated as a meshed grid. Only passive fault are assumed to occur in the breakers, which means that the faults in breakers and transformers only affect the downstream network and parallel routes are not affected. This is off course a simplified way to represent a substation, more thorough description on how to represent the different substation components for system reliability studies can among others be found in [4]. Figures 7, 8 and 9 show the block diagrams of example 3, 4 and 5 of the test system, henceforth simply referred to as system 3, 4 and 5 respectively.

12

5

3

6

4

781

2

5

3

6

4

78

Figure 7. Block diagram of system 3

9

1

2

3

6

4

7

5

8

9

10

1

2

3

6

4

7

5

8

9

10 Figure 8. Block diagram of system 4

1

23 4 5

6

7

1

23 4 5

6

7 Figure 9. Block diagram of system 5

Table 3 shows the component’s type and availability, the availability is assumed to be constant, i.e. independent of time.

Table 3. Component specification and reliability data for example 3, 4 and 5.

System 3 System 4 System 5

i Type pi type pi type pi

1 Bus bar 40 kV 0.95 Bus bar 40 kV 0.95 Bus bar 40 kV 0.95

2 Breaker 40 kV 0.93 Bus bar 40 kV 0.95 Bus bar 40 kV 0.95

3 Transformer 40/10 kV 0.99 Breaker 40 kV 0.93 Breaker 40 kV 0.93

4 Breaker 10 kV 0.93 Transformer 40/10 kV 0.99 Transformer 40/10 kV 0.99

5 Breaker 40 kV 0.93 Breaker 10 kV 0.93 Breaker 10 kV 0.93

6 Transformer 40/10 kV 0.99 Breaker 40 kV 0.93 Bus bar 10 kV 0.95

7 Breaker 10 kV 0.93 Transformer 40/10 kV 0.99 Bus bar 10 kV 0.95

8 Bus bar 10 kV 0.95 Breaker 10 kV 0.93 - -

9 - - Bus bar 10 kV 0.95 - -

10 - - Bus bar 10 kV 0.95 - -

In order to analytically be able to calculate the availability for system 3,4 and 5, each examples’ structure function, φ(X(t)), must be determined. The structure function is a function of the state vector X(t) = X1(t) + X2(t) + … + Xn(t), which is a vector containing the states of each component in the system. Further details on how to derive the structure functions can be found in [3]. The structure function for system 3, 4and 5 are:

))1)(1(1())(( 7654328133 XXXXXXXXtSS −−−×=Xφ 10

))1)(1(1))(1)(1(1))(1)(1(1())(( 8765431092144 XXXXXXXXXXtSS −−−−−−−−−=Xφ 11

))1)(1(1())1)(1(1())(( 765432155 XXXXXXXtSS −−−××××−−−=Xφ 12

Since all components only appear once in each equation, the structure functions are in their basic form, hence there is at this time no need expand the functions [3]. By replacing the components’

10

sate variable Xi(t) with the components’ probability pi(t) we are able to analytically calculate the systems’ probability.

0.8839)1)(1(1())(()()1)(Pr( 76543281333 =−−−××==== ppppppppthtt SSS ppX

.97440)1)(1(1))(1)(1(1))(1)(1(1()( 876543109214 =−−−−−−−−−= pppppppppphS p

.85200))1)(1(1))(1)(1(1()( 54376215 =×××−−−−−−= ppppppphS p

RESULTS

The results from the main analysis are presented for component importance and MCS separately. This is followed by some conclusions and a short discussion.

Component importance

According to the previously described component importance measures we can calculate the each component’s importance for system 3, 4 and 5.

In order to use Birnbaum’s component importance measure one can either expand the structure function, equation 6, or differentiate the structure function for each component, equation 7, whichever is the simplest. For example 3, we will expand the structure function and differentiate it.

876543218432187651765432813 )1)(1(1()( pppppppppppppppppppppppppphS −+=−−−×=p

If we now differentiate hS3(p) with respect to each component in the system we can calculate Birnbaum’s importance measure for each component. In order to save space only the calculation IB(1) is shown here the rest are presented in table 4.

0.9304)(

)1( 8765432843287651

33 =−+=

∂∂

pppppppppppppppp

hI SB p

For example 4, expanding the structure function for system 4 is a whole other boll game. We will now use the other method, equation 7. Birnbaum’s importance measure for component 1 in system 4 is:

0489.09280097690))(,0())(,1()(

)1( 111

44 ==−=

∂∂= .-.thth

ph

I SB ppp

Birnbaum’s importance measure for component 1 in system 5, using the same method as for example 4:

11

0427.08114.08541.0))(,0())(,1()(

)1( 111

55 =−=−=

∂∂= thth

ph

I SB ppp

The critical importance measure can now be calculated, using equation 8, which based on Birnbaum’s measure, again only ICR(1) for system 3,4 and 5 are calculate here, the rest are listed in table4.

4005.0 0.88391

)95.01(0.9304)(1

)1()1()1(

3

133 =

−−×=

−−⋅

=pS

BCR

hpI

I

0956.0.974401

)95.01(0.0489)(1

)1()1()1(

4

144 =

−−×=

−−⋅=pS

BCR

hpI

I

0144.0.852001

)95.01(0.0427)(1

)1()1()1(

5

155 =

−−×=

−−⋅

=pS

BCR

hpI

I

In order to determine the Fussel-Vesely’s importance measure the minimal cut sets of system 3 and 4 must be determined. The minimal cut sets are:

System 3: 1,2,5,2,6,2,7,3,5,3,6,3,7,4,5,4,6,4,7,8

System 4: 1,2,3,6,3,7,3,8,4,6,4,7,4,8,5,6,5,7,5,8,9,10

System 5: 1,2,3,4,5,6,7

So, but using equation 9, the Fussel-Vesely’s importance measure can be calculated for component 1 in system 3, 4 and 5.

0.0510)(1

1)1(

3

1

0

13 =

−−==

pS

FV

hp

Qq

I

0.0026)(1

)1)(1()1(

3

21

0

214 =

−−−==pS

FV

hpp

Qqq

I

0.0169)(1

)1)(1()1(

5

21

0

215 =

−−−==pS

FV

hpp

Qqq

I

12

Table 4. Component importance measures and rating of the components for system 3, 4 and 5

System 3 System 4 System5

i IB(i) ICR(i) IFV(i) IB(i) ICR(i) IFV(i) IB(i) ICR(i) IFV(i)

1 0.9304 1 0.4005 1 0.4305 1 0.0489 3 0.0956 2 0.0978 2 0.0427 3 0.0144 3 0.0169 3

2 0.1194 2 0.0720 2 0.0904 2 0.0489 3 0.0956 2 0.0978 2 0.0427 3 0.0144 3 0.0169 3

3 0.1122 3 0.0097 3 0.0129 3 0.1317 1 0.3607 1 0.4109 1 0.9161 1 0.4332 1 0.4729 1

4 0.1194 2 0.0720 2 0.0904 2 0.1237 2 0.0484 3 0.0587 3 0.8606 2 0.0581 2 0.0676 2

5 0.1194 2 0.0720 2 0.0904 2 0.1317 1 0.3607 1 0.4109 1 0.9161 1 0.4332 1 0.4729 1

6 0.1122 3 0.0097 3 0.0129 3 0.1317 1 0.3607 1 0.4109 1 0.0427 3 0.0144 3 0.0169 3

7 0.1194 2 0.0720 2 0.0904 2 0.1237 2 0.0484 3 0.0587 3 0.0427 3 0.0144 3 0.0169 3

8 0.9304 1 0.4005 1 0.4304 1 0.1317 1 0.3607 1 0.4109 1 - - - - - -

9 - - - - - - 0.0489 3 0.0956 2 0.0978 2 - - - - - -

10 - - - - - - 0.0489 3 0.0956 2 0.0978 2 - - - - - -

Optimal component availability in the importance sampling

In order to determine the optimal availability in the importance sampling, system 3 – 5 were simulated with all configurations of availability for each component class, i.e. not the same as component availability configurations. These simulations were conducted with 100 sets of 1000 samples in each set. Different seeds were used for the random number generator for each set, but the same seeds for the different systems2. Since the components’ actual availability only influence the weight and not the outcome of each sample it is sufficient to determine the optimal availability for each class of components3. How the components were classified is included in table 5. Table 5 also shows the outcome of the search for optimal availability. The constraints for the optimisation of the availability configuration were a combination of variance of the estimated value, equation 1, and an error margin towards the analytically calculated value.

System 3 System 4 System 5

Component 1,8

Component 2-7

Component 1,2,9,10

Component 3-8

Component 1,2,6,7

Component 3-5

Optimal availability

0.55 0.85 0.83 0.69 0.93 0.69

2 Seed 1-100 were used for random number generator #4 in matlab. 3 If all availability configurations were to be tested it would require 998 = 9.2 × 1015 sets for an 8 component system, which would take enormously long time.

13

Interesting to note is the more important the component is the lower is the optimal availability, opposite to the indication of the importance measures. To illustrate how the importance sampling improves the simulations, one set of 1000 samples were simulated, the cumulative results were logged after each sample. The optimal availabilities where used. Figure 10 – 12 shows these results.

100 200 300 400 500 600 700 800 900 10000.75

0.8

0.85

0.9

0.95

1

Number of samples

Sys

tem

ava

ilabi

lity

Imp. samp.Simp sampAnalytical

Figure 10. System availability as a function of the number of samples, system3

100 200 300 400 500 600 700 800 900 10000.95

0.955

0.96

0.965

0.97

0.975

0.98

0.985

0.99

0.995

1

Number of samples

Sys

tem

ava

ilabi

lity

Imp. samp.Simp. sampAnalytical

Figure 11. System availability as a function of the number of samples, system 4

14

100 200 300 400 500 600 700 800 900 10000.75

0.8

0.85

0.9

0.95

1

Number of samples

Sys

tem

ava

ilabi

lity

Imp. samp.Simp. samp.Analytical

Figure 12. System availability as a function of the number of samples, system 5

CONCLUSIONS AND DISCUSSION

To conclude the assignment, based on the results presented in this assignment the following conclusions can be made:

i As the name reveals, there are connections between how to weight the system components when applying importance sampling and the component importance measures tested in the assignment.

ii High importance in the importance sampling refers to those components whose reliability characteristics should be emphasized, i.e. lowered availability when sampling them. This is the opposite of how the important components are indicated in by the component importance measures included in this assignment.

iii It is important to analyse the system using some kind of indicator on components’ importance before simulating it, since wrongly configured importance weighting of system components can increase the variance and thereby making the results worse than if simple sampling would have been used.

iv Of the importance measures tested in this assignment Birnbaum’s measure seems to be the best to indicating on how to weight the system components.

v The importance measures do not indicate how the optimal availability should be, but rather the direction of the availability should be adjustment and set relative the other components.

vi Looking at the system configuration; components in series with the rest of the system are important and should be given high weight, components in series with other

15

components but in parallel with another set of components are more important then single components in parallel with another component.

First of all, since the importance sampling is unique for each system one applies it on, the conclusions presented in this assignment are valid for this specific test system. The indication however is that the conclusions are somewhat general.

Second, the luxury of using a small system is that everything is known since it can be calculated analytically, however, MCS are normally used when the analytical value is too complicated to calculate analytically. Consequently, the importance measure used in this assignment are also too complicated to calculate. This means that other measures for importance indication would have to be used such as system configuration, historical reliability data etc.

Finally, the measure used in this assignment are such that, as most component importance measures, for more than one output the importance measure have to be calculated for each output. In distribution systems there are usually one input but several outputs and using importance measures as those used in this assignment could be very time consuming. To over come this one can however differentiate the structure function towards performance indicators, which are not tied to one single output. However, differentiating to performance indicators give no indication on how to weigh the system components since they are dependent on what is at the end of a line not in its path, i.e. the system’s component configuration.

REFERENCES

[1] R. Billington, W. Li, “Reliability Assessment of Electrical Power Systems Using Monte Carlo Methods”, Plenum Press, New York, USA, 1994.

[2] M. Amelin, “On Monte Carlo Simulations and Analysis of Electricity Markets”, Doctoral thesis, Royal Inst. of Technology (KTH), Stockholm, Sweden, 2004.

[3] M. Rausand, A. Höyland, “System Reliability Theory – Models, Statistical Methods, and Applications, 2nd edition”, Wiley & Sons, Inc., Hoboken, NJ, USA, 2004.

[4] R. Billington, R. N. Allan, “Reliability Evaluation of Power Systems”, Plenum Press, New York, NY, USA, 1986.

1

RELIABILITY ENGINEERING IN DISTRIBUTION SYSTEM PLANNING

Markku Hyvärinen Helsinki Energy

[email protected]

1 INTRODUCTION The planning of electricity distribution systems is a technical and economic optimization problem in which the long-term total cost must be minimized taking into account the technical, environmental, safety related and other constraints. The two metrics that most customers notice about power are its quality and its cost. The expectation is, that there is continual improvement in one or both. In the past, distribution system reliability was a by-product of standard design practices and reactive solutions to historical problems. Today, but even more in the future, reliability is a measured area of performance, reported to regulators and customers and having an impact on utilities’ economics. Reliability must be planned, designed, and optimized with regard to cost. Further more, reliability-related risks must be managed. Reliability engineering is a staff function whose prime responsibility is to ensure that equipment is designed and modified to improve reliability and maintainability, that maintenance techniques are effective, that ongoing reliability-related technical problems are investigated, and appropriate corrective and improvement actions are taken. Reliability engineering uses reliability assessment (analysis, evaluation) in order to obtain failure characteristics of a distribution system. These characteristics are normally frequency and duration of interruptions and voltage sags (dips). System reliability can be improved by reducing the frequency of occurrence of faults and by reducing the repair or restoration time by means of various design and maintenance strategies. Reliability can only be objectively assessed by quantifying the behaviour of the individual items of equipment and thus by monitoring, collecting and collating the associated reliability data. This data can then be used to both assess past performance and to predict likely future performance. As importantly, it can be used to judge the merits and benefits, technical and economic, of alternative planning and operational strategies and decisions. Reliability levels are interdependent with economics since increased reliability is obtained through increased investments but it also allows the consumers to reduce their outage costs. In order to carry out objective cost-benefit studies, both these economical aspects are important: the optimum reliability level is determined by minimizing the total cost. Finally, it must be recognized that management decisions of network reinforcement or expansion schemes cannot be based only on the knowledge of the reliability indices of each scheme. It is also necessary to know the economic implications of each of these schemes.

2

2 THE BASIC CONCEPTS 2.1 Quantification of failure consequencies and mitigation actions Reliability can be engineered, in the same way that other performance aspects such as voltage,loading, and power factor are engineered. This requires reliability-focused analytical methods. The basic concept is the following: the probabilistic risk of an event is the product of likelihood and severity (measured by damage in monetary terms):

Risk [€/year] = Probability [1/year] • Damage [€] (1) In simplified form, the damage caused by an power outage increases along with the duration and the extent (amount of interrupted load) of the outage:

Damage [€] = Duration [h] • Interrupted Load [kW] • Outage Cost [€/kWh] (2) where the outage cost includes both the utilities cost and the cost to the customer. Thus:

Risk [€/year] = Probability [1/year] • Duration [h] • Interrupted Load [kW] • Outage Cost [€/kWh] (3)

Although very simplified, this equation shows where the engineering potential lies: in reducing the number of events leading to an interruption, or in mitigation of consequences by reducing the duration or the extent of the interruption. In paragraph 6 below, there is an overview of various means used in distribution system planning in mitigation of interruptions and voltage sags. The total annual cost Ct is given by [7][30]:

smr

n

i jmjijieu

n

i jmjiijjt CCCPrcrCICPrC +++⎟⎟

⎠

⎞⎜⎜⎝

⎛⋅⋅+⎟⎟

⎠

⎞⎜⎜⎝

⎛⋅⋅⋅= ∑ ∑∑ ∑

= ∈= ∈ 1 )(1 )(

)( λλ (4)

where i = ith customer or load point j = jth element of upstream elements m(j) from the load to the feeding point λj = failure rate of the element j rj = average outage time of the element j Pi = average load at the load point i CIC(ri) = customer interruption cost due to a failure of duration ri ceu = loss of revenue per energy not supplied (ENS) Cr = annualized investment costs (CAPEX) Cm = increase / decrease in annualized maintenance cost (MAINTEX) Cs = increase / decrease in annualized cost of system losses (∼OPEX)

3

The values of Cm and Cs may be negative (decrease) and these, together with Cr, should be evaluated as annualized values using present worth valuation or discount cash flow. Thus the value, which combines network utility unavailability data with customers’ view on unavailability of supply can be used as reliability criterion in planning tasks. The same approach can be used in analog operational planning processes to quantify risk (or the reduction of it) of various actions. For instance, RCM maintenance policy is oriented towards the prevention of critical failures, evaluated as such on the basis of the cumulation of the seriousness and frequency of the events in question. The purpose of preventive maintenance is to reduce the probability of faults. This approach can also be used in scheduling planned outages. 2.2 Power system hierarchy The power system can be divided into three (or four, including loads) levels, each with its own specific design and operational problems and solutions:

• generation • transmission • distribution • (loads)

Reliability evaluation of a complete electric power system including all the levels is normally not conducted due to the enormity of the problem. Instead, reliability evaluation of generating facilities, of composite generation and transmission systems (“bulk power systems”, including interconnections with neighbouring systems, generally operated at voltages of 100 kV or higher) and of distribution systems are conducted separately. [30] In bulk power systems, the following aspects are usually considered: adequacy, transient stability and thermal stability. Adequacy is the ability of the electric system to supply the aggregate electrical demand and energy requirements of their customers at all times, taking into account scheduled and reasonably expected unscheduled outages of system elements. Security is the ability of the electric systems to withstand sudden disturbances such as electric short circuits or unanticipated loss of system elements [31]. Distribution design, in many systems, is almost entirely decoupled from the transmission system development process. Coupling between these two systems in reliability evaluation can be accommodated by using the load point indices evaluated for the bulk transmission system as the input reliability indices of the distribution system. [7] Distribution network is the final link between the power system and the customer, and distribution systems largely determine the service quality profile seen by end-customers and dominate overall reliability indices. Distribution system is gradually becoming an increasing share of overall power system cost. The influence of the transmission system – in a case of a developed system - is much smaller because of the high redundancy used. [35]

4

3 MEASURING RELIABILITY To provide a quantitative evaluation of the reliability of an electrical system, it is necessary to have indices which can express system failure events on a frequency and probability basis. Three basic reliability indices, expected failure rate (λ), average outage duration (r) and average annual outage time (U), are to be evaluated for each load point of any meshed or parallel system. These three basic indices permit a measure of reliability at each load point to be quantified and allow subsidiary indices such as the customer interruption indices to be found. They have three major deficiencies, however [7]: • they cannot differentiate between the interruption of large and small load; • they do not recognize the effect of load growth by existing customers or additional new loads; • they cannot be used to compare the cost-benefit ratios of alternative reinforcement schemes

nor to indicate the most suitable timing of such reinforcements. These deficiencies can be overcome by the evaluation of two additional indices, these being [7]: • the average load disconnected due to a system failure, measured in kW or MW • the average energy not supplied due to a system failure, measured in kWh or MWh Two sets of reliability indices, customer load point indices and system indices, have been established to assess the reliability performance of distribution systems. Load point indices measure the expected number of outages and their duration for individual customers. System indices such as SAIDI and SAIFI measure the overall reliability of the system. The third popular index most utilities have been benchmarking is CAIDI. These indices can be used to compare the effects of various design and maintenance strategies on system reliability. SAIFI is improved be reducing the frequency of outages (for example tree trimming and maintaining equipment). SAIFI is also improved by reducing the number of customers interrupted when outages do occur (for example, by adding reclosers and fuses). Strategies that reduce SAIFI improve SAIDI because if an outage does not happen, it does not add to duration. SAIDI is also improved by improving CAIDI through faster customer restoration. However, system improvements can make CAIDI go up or down, depending on whether the improvements have a greater effect on outage frequency (customer interruptions) or outage duration (customer minutes of interruptions). Usually, the three reliability indices are graphed independent of each other. It is though useful to remember the relationship between the indices: SAIDI = SAIFI * CAIDI. In this equation, SAIFI can be considered the independent variable – there can be no outage duration (SAIDI) without an outage frquency (SAIFI). This representation of the indices suggests graphing them together, with SAIFI on the x-axis and SAIDI on the y-axis. Lines of constant CAIDI pass through the origin. Thus, all three indices are shown at once. The graph is referred as the “reliability triangle” because the benchmark quartile lines form a triangle. The closer to the origin a point lays, the better the reliability. This method also is useful for tracking the indices over course of a year. [34]

5

Displaying the indices in a reliability triangle has several advantages: • data reporting is condensed • the fact that the indices are related is emphasized • reliability performance is clarified • cumulative tracking shows that SAIFI and SAIDI can only increase over the year, while

CAIDI can increase or decrease. Reliability indices can be calculated using historical outage data, or predicted using stochastic methods. 4 RELIABILITY ENGINEERING PROCESS 4.1 An overview of the process Picture 1 shows an overview of functional blocks relating to reliability engineering. With input data from data-analysis, staff knowledge, r&d, etc. a reliability model is composed based on the new design or existing network. After assessing failure consequencies, potential mitigation actions are development. These are analysed with predictive analysis methods. An economic evaluation is needed before final decision-making.

Picture 1. An overview of reliability engineering functional blocks (modified from [25] and [33]). 4.2 Data collection and processing Consistent collection of data is essential as it forms the input to relevant reliability models, techniques and equations. Processing of data occurs in two distict stages. Firstly, field data is obtained by documenting details of failures as they occur and the various outage durations

6

associated with these failures. Secondly, this field data is analysed to create statistical indices. These indices are updated by the entry of subsequent new data. The quality of the data and thus the confidence that can be placed in it, is clearly dependent on the accuracy and completeness of the compiled information. It is therefore essential that the future use to which the data will be put and the importance it will play in later developments is stressed. The quality of the statistical indices is also dependent on how the data is processed, how much pooling is done and the age of the data currently stored. These factors affect the relevance of the indices for the use to which they are subsequently put. [1] The reliability data which must be estimated from the collected data are outage rates and durations for forced and scheduled outages for each component type. Restoration can be perceived as being of two types: restoring supply to the customers and restoring a failed component to its working state. These are not necessarily the same. Care is therefore required to correctly identify restoration times. In order to perform predictive reliability assessments, the component restoration times are required. The service restoration times are important only to measure the quality of service to customers. For more detailed analysis, restoration may be categorized by a number of subevents such as repair, replacement, reclosure, switching. In most cases, a particular restoration process is coupled with a specific type of failure event. [1] 4.3 Reliability modelling Reliability modelling techniques are decribed in a more comprehensive manner in other presentations. These models must reflect all relevant phenomena of real networks; operating and failure states of system components, the asset ageing process, etc. In more enhanced studies, especially for transmission and subtransmission systems, network reliability has to estimated in such a way that both post-fault substation events, i.e., the protection system and circuit breaker operations, and the power system dynamics are included [33]. This means that the protection system must be modelled as a relevant subsystem. The failures suffered by protection systems are different from those experienced by other types of electrical system components. Due to different failure modes – failure to operate and incorrect operation – the fault clearing chain must be divided on dependability and security. The reliability measure dependability indicates the ability to perform the requested fault clearing. Security on the other hand indicates the security against incorrect operations. Dependability and security are contradictionary to each other, and a gain one of the quantities can result in a loss in the other. In modelling of the protection system, the event tree method is of particular interest because it can recognize the sequential logic of protection system operations, and the analysis can be extended to study power systems as well as protection systems to a pre-selected depth. After the occurrence of a fault in power systems, the normal operation or failure to operate the protection system components can be described in a detailed tree structure. The end events of the tree identifies the resulting states of circuit breakers, and hence the effect of failures in the protection system. [33]

7

4.4 Reliability assessment Reliability analysis consists of two steps, independent of one another on both function and application. Some utilities perform only one or the other.

• Historical reliability assessment, whose goal is to analyze system and historical operating data to assess the reliability “health” of the system, to identify problem and to determine what caused any problems.

• Predictive reliability assessment, whose goal is to predict expected future reliability levels on the system, both in general and at specific locations, by analyzing a specific candidate design for a part of the distribution system, and determining expected reliability.

Willis has added also a third step [37]:

• Calibration is required to adjust a predictive model so that it correctly “predicts” past events on the system.

Predictive analysis is the core of reliability planning. It must produce an estimate to

• the future performance of the plant and system • the benefits of alternative designs, refurbishments and expansion plans • the effects of alternative operational and maintenance policies • the related reliability cost/benefit/worth of the alternatives with two previous items

4.5 Cost evaluation A major attribute of planning is optimization or reduction of cost. An assessment of various planning alternatives may be based on the capital investment cost alone if the additional network capacity provided by each option is comparable and if system maintenance costs are effectively the same. If they are not the same, the supplement in the costs must be taken into account. The change on maintenance costs usually associated with the addition of new objects (for example a new substation). Besides, there are cost of equipment, land, labor, etc. These values can be estimated by the utility for each possible planning option. The investment criterion can be calculated as a sum of annuities of the investments and the corresponding supplements to the maintenance cost (see 2.1). 5 METHODS 5.1 An overview The techniques used in power system reliability evaluation can be divided into two basic categories: analytical and simulation methods. The analytical techniques are highly developed and have been used in practical applications for several decades. Analytical techniques represent the system by mathematical models and evaluate

8

the reliability indices from these models using mathematical solutions. A range of approximate techniques has been developed to simplify the required calculations. Analytical techniques for distribution system reliability assessment can be effectively used to evaluate the mean values of a wide range of system reliability indices. This approach is usually used when teaching the basic concepts of distribution system reliability evaluation. The mean or expected value, however, does not provide any information on the inherent variability of an index. [9] The principles of probabilistic design that have been applied in the aerospace and power generation industries for some time are finding new applications in the distribution power planning and engineering. Probabilistic design methodology provides a way to objectively quantify the relative value of different design arrangements and operational practices. Also, the probabilistic approach makes it possible to understand the sensitivity of the design to system variables, thereby giving the system operator knowledge about where system reinforcements are required to improve performance. This is in contrast to the deterministic approach that incorporates “factors of uncertainty” or safety factors to produce a design that may be overly conservative. A good reliability model and a firm grasp on the capital and O&M cost activities are essential to this process. [17] Qualitative methods may be required in several stages of the process, e.g. in pre-studies and in managing the risks. 5.2 Contingency-based planning methods Traditionally, electric utilities assured the reliability of their power delivery indirectly, by setting and then engineering criteria that called for margin, switching, and other characteristics that would assure secure operation. The electric utilities developed a method of engineering a system that would provide reliable performance by adhering to basic rule: design the system so that it can do without any major element and the system can still do its job even if any one unit fails. These methods were often referred to as “N-1” methods. So designed, a system would tolerate failures. The likelyhood of two nearby units failing simultaneously was usually remote enough so as to not be a consideration. In general, contingency-based methods came to be called “N-X” methods, where X is the number of units they were designed to do without on a routine basis. [37] Probabilities of different faults are traditionally not taken into account; instead all faults that may limit the transmission capacity are treated equally. This method can lead to conservative utilization of the grid. [33] 5.3 Planning standards From the contingency-based concept, utilities evolved rules (guidelines, design criteria, standards) and engineering methods that applied these rules with precision and procedure. These methods were simple to apply and understand, and effective in traditional situations. The so-called planning standards usually make a distiction between probable (likely) and extreme (unlikely) faults. In that sense they are semi-risk-based approaches. They are based on the assumption that the more rare an event is the more severe consequences are accepted. [20]

9

According to these standards, electric systems must be planned to withstand the more probable forced and maintenance outage system contingencies at projected customer demand and anticipated electricity transfer levels. Extreme but less probable contingencies measure the robustness of the electric systems and should be evaluated for risks and consequences [31]. Although it is not practical (and in some cases not possible) to construct a system to withstand all possible extreme contingencies without cascading, it is desirable to control or limit the scope of such cascading or system instability events and the significant economic and social impacts that can result. Planning stardards have been used successfully especially in bulk power systems, which involves multiple parties. Since all electric systems within an integrated network are electrically connected, whatever one system does can affect the reliability of the other systems. Therefore, to maintain the reliability of the bulk electric systems or interconnected transmission systems or networks must comply common planning standards (e.g. NERC, NORDEL). [31] 5.4 Predictive reliability analysis of distribution systems Contingency based methods have several disadvantages. Because they accomplish reliability indirectly, it is essentially impossible to use them as engineering tools in a process aimed at driving cost to a minimum. For that, one needs methods that directly address reliability – so-called reliability-based engineering methods. The planners need reliability-based analysis tools, methods that can evaluate a particular power system layout against a particular customer demand profile. These tools are used on almost the same manner that traditional planners used a load flow. Working with a “reliability load flow”, planners can engineer frequency and duration of outages (SAIDI, SAIFI) , and incidence of voltage sags, throughout the system in exactly the same manner. Reliability assessment on a routine computational basis is a relatively new feature of distribution planning. 5.5 Planning constraints Minimum level of reliability or maximum duration of interruptions may be defined by authorities or utilities recommendations. An example of such recommendations for maximum duration of interruptions is as follows: [36]:

• 24 hours with maximum load of < 2 MW • 18 hours with maximum load of 2-5 MW • 12 hours with maximum load of 5-20 MW • 2 hours with maximum load of 20-50 MW • 1 hour with maximum load of > 50 MW

Also, critical values for interruption durations for different types of loads can be defined [36]:

• 1 second hospitals or process industry • 10-15 minutes steelworks • 15-30 minutes animal feeding • 30 min elevators

10

• 2 hours water supply • 6 hours greenhouses • 8 hours caring industry • 10 hours telecommunication • 12-24 hours houses, water treatment plants and freezing plants

These constraints, which are felt to be “deterministic”, are used in the probabilistic reliability analysis as limitations. [13] More often, authorities define limits for interruption durations, and if these limits are exceeded, incentives follow. 5.6 Disaster mitigation While it is often impossible to prevent disasters – like fires, tornadoes, hurricanes, floods, industrial accidents, and even bombings – there are measures that communities can take to mitigate their impact. Decisions how to prepare for, respond to, and recover from disasters are not based on engineered data, but instead they are political by nature. These preventive actions can however have an impact on distribution system reliability level also during normal conditions. 6 IMPROVEMENT OF RELIABILITY OF A SYSTEM BY VARIOUS MEANS – MITIGATION OF INTERRUPTIONS AND VOLTAGE SAGS 6.1 Overview of mitigation or elimination methods To understand the various ways of mitigation, the mechanism all the way from the cause of a fault to an end-customer equipment or process outage and the following restoration process all need to be understood. Long interruptions are always due to component outages. Component outages are due to three different causes [10]:

• A fault occurs in the power system which leads to an intervention by the power system protection.

• A protection relay intervenes incorrectly, thus causing a component outage. • Operator actions causes a component outage. These could also be scheduled or planned

interruptions. Whether a component outage leads to an interruption, depends upon what sort of redundancy the component in question has. For different types of events, different mitigation methods are most suitable: improving the equipment for short-duration events, improving the network design for long-duration events, etc.. [10]

11

The basic categories of mitigation methods are: • reducing the number of faults • improving the fault-clearing process • changing the system design such that faults result in less severe events at the equipment

terminals or at the customer interface • connecting mitigation equipment between the sensitive equipment and the supply • improving the immunity of the end-customers’ equipment

6.2 The number of faults Reducing the number of faults in a system not only reduces the sag frequency but also the frequency of sustained interruptions. Some examples of fault mitigation are:

• better shielding of the components and plants (using cables or covered wires instead of overhead lines with bare conductors, additional shielding wires or earth wires reducing the risk of lightning fault, placing equipment inside the buildings, fences, etc.)

• increasing the insulation level (insulation coordination) and related earthing practices • increasing maintenance and inspection frequencies; preventive maintenance activities

could impact on the frequency of faults by preventing the actual cause of failure • optimal neutral earthing practice • preventing maloperations by proper design of protection and control systems • preventing human errors by designing easy-to-use systems (primary, secondary, control

centers), training, etc. One has to keep in mind, however, that these measures may be very expensive and that its costs have to be weighted against the consequencies of the equipment trips. [10] The impact of reliability of components on system reliability and the identification of those components which have the greatest impact on system reliability, is clear. For planning engineers, one important field of improvement is customer specifications. Customers’ specifications define how the component will be used, the loading regime and the system conditions envisaged. In some cases the specification fails to fully describe the operating conditions in terms of loading, switching operations, system transients or environmental conditions. The importance of specifications is often overlooked. 6.3 The fault-clearing Protection systems are designed to automatically disconnect components from the network to isolate electrical faults or protect equipment from damage due to voltage, current, or frequency excursions outside of the design capability of these facilities. Coordination of protection systems is vital to the reliability of the networks. If protection and control systems are not properly coordinated, a system disturbance or contigency event could result in the unexpected loss of multiple facilities. Through cascading events, the extent of the interruption is increased. [31] Reducing the fault-clearing time does not reduce the number of events but only their severity. The duration of an interruption is determined by the speed with which the supply is restored.

12

Faster fault-clearing does also not affect the number of voltage sags but it can significantly limit the sag duration. [10] 6.4 The power system design and supply restoration The structure of the distribution system has a big influence on both the number and the duration of the interruptions and voltage sags experienced by the customer. When a power system component fails, it needs to be repaired or its function taken over by another component before the supply can be restored. The repair or replacement process can take several hours or, especially with power transformers and GIS-plants, even days up to weeks. In most cases the supply is not restored through repair or replacement but by switching from the faulted supply to a backup supply. The speed with which this takes place depends on the type of switching used. A smooth transition without any interruption takes place when two components are operated in parallel. This will however not mitigate the voltage sag due to the fault which often precedes the interruption. [10] When a single customer is considered, additional reduncancy on the distribution system side is only justified for large industrial or commercial customers. For a larger group of customers, especially in densely populated areas, feeder level reduncy becomes easily feasible. At the substation level and the subtransmission level full redundancy is almost self-evident due to long repair-times of the main components in the reference of the large amount of interrupted load. At these levels optimization is needed to balance restoration times and system complexity and dimensioning (parallel operation i.e. meshed systems versus redundancy through switching). Table 1. Examples of various types of redundancy in power system design [10] Redundancy Duration of interruption Typical applications No redundancy hours … days low voltage in rural areas Redundancy through switching - local manual switching 1 hour and more low voltage and distribution - remote manual switching 5 … 2 0 minutes industrial systems, future public distribution - automatic switching 1 … 60 seconds industrial systems - solid state switching 1 cycle and less future industrial systems Redundancy through parallel operation voltage sag only transmission systems, industrial systems (also a combination is possible: remote manual switching plus local manual switching) There are two types of parallel operation: two feeders in parallel and a loop system. In both cases there is a single redundancy. The design of parallel and loop systems is based on so-called (n-1) criterion. It enables high reliability without the need for stochastic assessment. A thorough assessment of all “common-mode failures” is needed before one can trustfully use such high-redundancy design criterion. [10]

13

In switching schemes, the additional cost for the system are not only switching, signalling and communication equipment. The feeder has to be dimensioned such that it can handle the extra load. Also the voltage drop over the, now potentially twice as long, feeder should not exceed the margins. Roughly speaking the feeder can only feed half as much load. This will increase the number of substations and thus increase the cost. Thus, reliability engineering should always be conducted on system basis. In switching schemes, a system outage or failure event may not lead to long-term total loss of continuity but may cause violation of a network contraint (loss of quality) and / or to the event defined as partial loss of continuity. Many systems have interconnections which allow the transfer of some or all the load of a failed load point to other neighbouring load points through normally open points. [7] While the amount of distributed generation (DG) is likely to grow, it is not unambiguous that this has advantageous impact on reliability. The adaptation of traditionally designed distribution system to DG takes a while, and the impact of distributed generation on reliability is by nature a case-by-case issue. 6.5 The interface between the network and the customer Mitigation equipment in the system-equipment interface is the only place where the customer has control over the situation [10]. Both changes in the supply system as well as improvement of the equipment are often completely outside of the control of the end-user. On-site generators are used for two distictly different reasons:

• Generating electricity locally can be cheaper than buying it from the utility. • Having an on-site generator available increases the reliability of the supply as it can serve

as a backup in case the supply is interrupted. Standby generation is often used in combination with a small amount of energy storage (e.g. flywheel) supplying the essential load during the first few seconds of an interruption. All modern mitigation techniques are based on power electronic devices, with the voltage-source converter being the main building block [10]. Terminology is still very confusing in this area, terms like “compensators”, “conditioners”, “controllers” and “active filters” are in use, all referring to similar kind of devices. These device can be series or shunt connected. One of the main disadvantages of a series controller is that cannot operate during an interruption, A shunt controller operates during an interruption, but its storage requirements are much higher. Some examples of mitigation equipment are:

• The main device used to mitigate voltage sags and interruptions at the interface is the so-called uninterruptable power supply (UPS).

• Voltage source converters (VSC) generate a sinusoidal voltage with the required magnitude and phase, by switching a dc voltage an a particular way over the three phases. This voltage source can be used to mitigate voltage sags and interruptions.

14

The interface between the supply system and the end-customers’ equipment including protection coordination is controlled in the facility connection requirements. These requirements shall be documented, maintained, and published by voltage class, capacity, and other characteristics that are applicable to generation, transmission, and electricity end-user facilities and which are connected to, or being planned to be connected to, the system. 6.6 The equipment Improvement of equipment immunity is probably the most effective solution against equipment trips due to voltage sags. For interruptions, especially the longer ones, improving the equipment immunity is no longer feasible. 7 ECONOMIC CONSIDERATIONS

7.1 Decision process requirements The decision process can be seen as build of three separate levels. The first level deals with the technical information (network and reliability data); the second level uses the results of the first level and the economical information on assets (CAPEX, OPEX, MAINTEX), while the third level combines this with the economical information on business and societal information. [18] While reliability of the equipment and system itself is important, what matters to regulators and consumers alike is customer service quality. The reliability worth of a network may be defined as the benefit to society ascribed to the reliability level of a network. The best measure of reliability worth is therefore given by customer outage cost. While the load point and performance indices indicate the frequency, duration, severity and significance of outage situations, reliability worth attaches an economic value to such situations. This is a particularly attractive aspect since this means that their incremental values can be easily included in cost-benefit analyses of alternative network configurations. [22] 7.2 Customer interruption cost Outages in electricity supply can cause extensive economical damage to the customers due to lost production, spoilt raw materials, broken equipment, and for various other reasons. Outage frequency and duration can be reduced by technical means, but this usually requires capital intensive investments into the power distribution and generation system. Hence, when planning the power system, we have to compromise between reliability and outage costs. In this planning process, accurate and up to date knowledge on customers’ outage costs is of high importance. [27] The outage costs in general have two parts; that seen by the utility and that seen by society or the customer [10]. The utility outage cost include the loss of revenue from customers not served and the increased expenditure due to maintenance and repair. These costs, however, usually form only a very small part of the total outage costs. A greater part of the costs comprises those seen by the customer and most of these are extremely difficult to quantify. [7]

15

The worth assessment is based on customer surveys. The obtained survey data can be compiled and calculated for the Sector Customer Damage Function (SCDF), which presents the relationship between the sector interruption cost as a function of interruption duration. Real distribution systems supply different mixtures of commercial, industrial and residential types of customers that impose different load demands and service quality requirements. Composite customer damage function (CCDF) must be defined as the estimate of costs associated with power interruptions as a function of the interruption duration for the customer mix in a particular service area [22]. Reliability worth can then be evaluated in terms of the expected customer interruption cost by appropriately combining the CCDF with the calculated indices, i.e., expected energy not supplied, expected load loss, load point failure etc. 7.3 Asset management The utility must manage, in general, three inter-related activities [24]:

• prioritization of O&M resources, • prioritization of capital spending, to meet new expansion needs and for replacement of

equipment that is too old or costs too much to repair, and • optimum utilization of its existing and any new equipment.

These three activities should be a part of the overall utility managerial process during budget allocation. Asset management takes a holistic view on all these activities: it is a paradigm which includes a much closer integration of capital and O&M planning, and spending prioritization, than was traditionally the case, along with tighter controls on spending, all aimed at achieving a lifetime optimum business-case for the acquisition, use, maintenance and disposal of equipment and facilities. [37] 8 CONCLUSION Reliability planning of a distribution system seldom involves determining how to provide the highest possible reliability of service, but instead involves determining how to meet service reliability targets while keeping cost as low as possible. The modern method is to engineer reliability directly, i.e. to engineer frequency and duration of outages (SAIDI, SAIFI) and incidence of voltage sags. The quantitative reliability analysis forms input to the value-based planning process, where the total cost including customer outage cost is mimized.

16

9 REFERENCES [1] R.N.Allan, R.Billinton: Concepts of Data for Assessing The Reliability of Transmission and Distribution Equipment, ‘The Reliability of Transmission and Distribution Equipment’, 29-31 March 1995, Conference Publication No. 406, IEE, 1995, pp. 1-6 [2] D.J.Allan, A.White: Transformer Design for High Reliability, ‘The Reliability of Transmission and Distribution Equipment’, 29-31 March 1995, Conference Publication No. 406, IEE, 1995, pp. 66-72 [3] APM Task Force Report on Protection System Reliability: Effect of Protection Systems on Bulk Power Reliability Evaluation, Transactions on Power Systems, Vol 9., No. 1., February 1994, pp. 198-205 [4] N.Balijepalli, S.S.Venkata, R.D.Christie: Modelling and Analysis of Distribution Reliability Indices, IEEE Transactions on Power Delivery, Vol. 19, No. 4, October 2004, pp. 1950-1955 [5] C.Basille, J.Aupied, G.Sanchis: Application of RCM to High Voltage Substations, ‘The Reliability of Transmission and Distribution Equipment’, 29-31 March 1995, Conference Publication No. 406, IEE, 1995, pp. 186-190 [6] L.Bertling, R.Allan, R.Eriksson: A Reliability-Centered Asset Maintenance Method for Assessing the Impact of Maintenance in Power Systems, IEEE Transactions on Power Systems, Vol. 20, No. 1, February 2005, pp. 75-82 [7] R.Billinton, R.N.Allan: Reliability Evaluation of Power Systems, Pitman Publishing Limited 1984, 432 pages [8] R.Billinton, R.Ghajar, F.Filippelli, R. Del Bianco: The Canadian Electrical Association Approach to Transmission and Distribution Equipment Reliability Assessment, ‘The Reliability of Transmission and Distribution Equipment’, 29-31 March 1995, Conference Publication No. 406, IEE, 1995, pp. 7-12 [9] R.Billinton, P.Wang: Teaching Distribution System Reliability Evaluation Using Monte Carlo Simulation, IEEE Transactions on Power Systems, Vol. 14, No. 2, May 1999, pp. 397-403 [10] M.H.J. Bollen: Understanding power quality problems: voltage sags and interruptions, IEEE Press 2000, 543 pages [11 R.E. Brown, J.J. Burke: Managing the Risk of Performance Based Rates, IEEE Transactions on Power Systems, Vol. 15, No. 2, May 2000, pp 893-898 [12] Mo-yuen Chow, Leroy S. Taylor, Mo-Suk Chow: Time of Outage Restoration Analysis in Distribution Systems, Transactions on Power Systems, Vol 11., No. 3., August 1996, pp. 1652-1658

17

[13] CIGRE Meetings, Montreal Symposium 16-18 September 1991: Electric power systems reliability, Electra No 140, February 1992, pp. 5-35 [14] D.M.Dalabeih, Y.A.Jebril: Determination of Data for Reliability Analysis of a Transmission System, ‘The Reliability of Transmission and Distribution Equipment’, 29-31 March 1995, Conference Publication No. 406, IEE, 1995, pp. 19-23 [15] J.G.Dalton, D.L.Garrison, C.M.Fallon: Value-Based Reliability Transmission Planning, IEEE Transactions on Power Systems, Vol. 11, No. 3., August 1996, pp. 1400-1408 [16] A.O.Eggen, B.I.Langdal: Large-Scale Utility Asset Management, CIRED Conference 2005, paper 446 [17] L.A.Freeman, D.T.Van Zandt, L.J.Powell: Using a Probabilistic Design Process to Maximize Reliability and Minimize Cost in Urban Central Business Districts, CIRED Conference 2005, paper 681 [18] E.Gulski, J.J.Smit, B.Quak, E.R.S.Groot: Decision Support for Life Time Management of HV Infrastructures, CIRED Conference 2005, paper 183 [19] P.Haase: Charting Power System Security, EPRI Journal, September/October 1998, pp. 27-31 [20] D.Holmbergm T.Ostrup, M.Amorouayeche, A.Invernizzi (CIGRE Advisory Group 38.03): Reliability Standards Versus Development of Electric Power Industry, Electra No. 177, April 1998, pp. 95-104 [21] IEEE Task Force: Reporting Bulk Power System Delivery Point Reliability, IEEE Transactions on Power Systems, Vol 11., No. 3., August 1996, pp. 1262-1268 [22] K.K.Kariuki, R.N.Allan: Reliability Worth in Distribution Plant Replacement Programmes, ‘The Reliability of Transmission and Distribution Equipment’, 29-31 March 1995, Conference Publication No. 406, IEE, 1995, pp. 162-167 [23] G.H. Kjolle, H.Seljeseth, J. Heggset, F.Trengereid: Quality of Supply Management by Means of Interruption Statistics and Voltage Quality Measurement, ETEP Vol. 13, No. 6, November/December 2003, pp. 373-379 [24] T.Kostic: Asset Management in Electrical Utilities: How Many Facets It Actually Has, IEEE 2003 [25] M.Kruithof, J.Hodemaekers, R.Van Dijk: Quantitative Risk Assessment; A Key to Cost-Effective SAIFI and SAIDI Reduction, CIRED Conference 2005, paper 185 [26] L.Lamarre: When disaster strikes, EPRI Journal, September/October 1998, pp. 9-17

18

[27] B.Lemström, M.Lehtonen: Cost of Electricity Supply Outages, Report 2-94, VTT Energy, 1994 [28] V.Miranda, L.M.Proenca: Why Risk Analysis Outperforms Probabilistic Choice as the Effective Decision Support Paradigm for Power System Planning, IEEE Transactions on Power Systems, Vol. 13, No. 2, May 1998, pp. 643-648 [29] A.Mäkinen, J.Partanen, E.Lakervi: A Practical Approach for Estimating Future Outage Costs in Power Distribution Networks, IEEE Transactions on Power Delivery, Vol. 5, No. 1, January 1990, pp. 311-316 [30] V.Neimane: On Development Planning of Electricity Distribution Networks, Doctoral Dissertation, Royal Institute of Technology, Department of Electrical Engineering, Electric Power Systems, Stockholm 2001, 208 pages [31] North American Electric Reliability Council (NERC): Planning Standards, September 1997 [32] P.Pohjanheimo: A Probabilitisc Method for Comprehensive Voltage Sag Management in Power Distribution Systems, Helsinki University of Technology publications in Power Systems 7, Espoo 2003, 87+19 pages [33] L.Pottonen: A Method for the Probabilistic Security Analysis of Transmission Grids, Doctoral Dissertation, Helsinki University of Technology, Power Systems and High Voltage Engineering, 2005, 207 pages [34] J.Rothwell: The Reliability Triangle, Transmission & Distribution World, November 2004, pp. 54-56 [35] G.Strbac, J.Nahman: Reliability Aspects in Operational Structuring of Large-scale Urban Distribution Systems, ‘The Reliability of Transmission and Distribution Equipment’, 29-31 March 1995, Conference Publication No. 406, IEE, 1995, pp. 151-156 [36] Svenska Elverksföreningen: Leveranskvalitet [37] H.L.Willis: Power Distribution Planning Reference Book – Second Edition, Revised and Expanded, Marcel-Dekker Inc 2004, 1217 pages

A BAYESIAN METHOD FOR RELIABILITY MODELLING APPLIED TO HIGH-VOLTAGE CIRCUIT BREAKERS

Tommie Lindquist Royal Institute of Technology (KTH)

[email protected]

ABSTRACT This report presents a Bayesian method using rejection sampling in order to model the reliability of power equipment. The method makes use of the different calculations and tests carried out during the development process as prior information about new components that has not yet failed. This information is combined with data from failures and inspections. The aim of this report is to demonstrate the proposed method by using an example applied to high voltage circuit breakers (CB) using randomly generated test data. The results from the CB example show that it is possible to model the reliability of CBs with limited access to failure statistics. They also show that the proposed method makes it possible to model the effect of maintenance. Finally, the method can be applied when modelling sub-components with different ageing factors.

2

TABLE OF CONTENTS

1 INTRODUCTION ............................................................................................................. 4

1.1 Background............................................................................................................... 4

1.2 Theory ....................................................................................................................... 4 3.2.1 Censored data ..................................................................................................... 4 3.2.2 Bayes’ theorem................................................................................................... 5

2 METHOD .......................................................................................................................... 6

2.1 Component modelling .............................................................................................. 6

2.2 Bayesian method....................................................................................................... 7

3 CIRCUIT BREAKER EXAMPLE.................................................................................... 8

3.1 Model ......................................................................................................................... 8

3.2 Data............................................................................................................................ 9 3.2.1 Prior information ................................................................................................ 9 3.2.2 Updating information ......................................................................................... 9

4 RESULTS ........................................................................................................................ 10

5 CONCLUSIONS.............................................................................................................. 12

6 REFERENCES ............................................................................................................... 13

3

1 INTRODUCTION

1.1 Background The aim of modelling the reliability of power system components is to be able to predict failures and thus, by applying the appropriate maintenance tasks, prevent or delay these failures. Probabilistic reliability models allow the user to predict the likely future behaviour of the component. Modelling the reliability of power system components is difficult due to the lack of failure data resulting from high reliability components and high cost of life tests [1]. This problem can be overcome by the use of Bayesian methods. Bayesian methods allow the combination of any previous knowledge about the process one may have with empirical data. This knowledge may come from past experience from similar equipment, such as failure data or maintenance records, or it may come from tests carried out during the product development process. In this report a Bayesian method using rejection sampling is presented. The method makes use of the different calculations and tests carried out during the development process as prior information about new components that has not yet failed. This information is combined with data from failures and inspections. The aim of this report is to demonstrate the proposed method by using an example applied to high voltage circuit breakers (CB) using randomly generated test data.

1.2 Theory Throughout this report x will be an observation of lifetime, which is represented by the random variable X. Analogously will θ be considered to be an observation of the parameter of interest represented by the random variable Θ.

3.2.1 Censored data Censoring occurs when it is not possible to observe a components’ time* to failure exactly and is very common in reliability data analysis. Basically there are three ways in which reliability data can be censored [2], [3]. 1. Right-censored data; component i has not failed at time xi, giving X > xi. This is very often

the result from inspections where no fault has been discovered, nevertheless this information is very important when modelling reliability.

2. Left-censored data; component i has failed before time xi, giving X x≤ i. This situation may occur when a component breaks down before its first inspection and the failure time is not known exactly.

3. Interval-censored data; component i has failed between times xi-1 and xi, giving . This is the case when a unit is found to have failed between two

inspections. ii xXx ≤<−1

* Note that time, as it is used in this report, may be something different from calendar time (e.g. number of operations or number of cycles).

4

3.2.2 Bayes’ theorem Bayes’ theorem was first formulated by reverend Thomas Bayes and was presented posthumously in 1763 [5]. Bayes’ theorem provides a mechanism for combining prior information with sample data to make inferences on model parameters [2], [3]. Let B1, B2,…Bn be mutually exclusive and exhaustive events contained in a sample space S, such that:

iBP

jiBB

BP

i

ji

n

ii

each for 0)(

for 0

11

>

≠=

=

=

I

U

and let A be an event such that P(A)>0. Then for each k:

∑∞

=

=

1)()|(

)()|()|(

iii

kkk

BPBAP

BPBAPABP (1)

The basic concept of the Bayesian point of view is that, in the continuous case, θ is interpreted as a realisation of the random variable Θ with some density f(θ). This density represents the prior belief about the value of Θ, before any observations have been made. f(θ) is called the prior density of Θ. The conditional distribution of Θ, given X=x, is then:

)(),()|(

xfxfxf θθ = (2)

where ),( θxf is the joint distribution of X and Θ and is given by:

)()|(),( θθθ fxfxf ⋅= (3)

In (7) the marginal distribution of X, ) is: ,(xf

∫∞

⋅=0

)()|()( θθ fxfxf (4)

In (2), the denominator, as described in (4), is only used as a normalising constant due to the fact that when a value for X has been observed (4) is constant [3]. Hence )|( xf θ is always proportional to )()|( θθ fxf ⋅ , which can be written as:

)()|()|( θθθ fxfxf ⋅∝ (5) Furthermore, it is possible to predict future events, like the failure of a component from a specified population, using Bayesian methods. Future events can be predicted by using the Bayesian posterior predictive distribution [2].

5

If X0 represents a random variable for a new observation, then the posterior predictive probability density function (p.d.f.) of X0 is [3]:

)|()|()|(0

00 xfxfxxf θθ∫∞

⋅= (6)

2 METHOD

2.1 Component modelling In this report the definition of a sub-component is the smallest replaceable item of a power system component. All sub-components in the component reliability model are considered to be non-repairable and the different times to failure are statistically independent. Non-repairable means that the only maintenance action that can bring a failed sub-component back into a functioning state is replacement. Consider a component comprising k non-repairable sub-components. Each sub-component, i, has a lifetime Xi, where Xi is an independent random variable with a p.d.f. fi(x), where x is an observation of X. Using the proposed method, a power system component is modelled as a serial system comprising k sub-components, each with a lifetime Xi, see Figure 1.

Power system component

Sub-component k

Sub-component 2

Sub-component 1

Figure 1. A reliability model representing a power system component, from [8]. The reliability of sub-component i is measured using its failure rate λi(x), defined as:

∫−= x

i

ii

df

xfx

0

)(1

)()(ττ

λ (7)

If the repair time can be assumed to be zero then the failure rate of the entire power system component, modelled as a series structure as in figure 1, is defined as:

∑=

=k

iitotal x

1)()( λλ x (8)

Many power system components are very complex and are subject to many different failure modes. The different sub-components often have different failure mechanisms and factors that affect their condition. Because not all sub-components age with calendar time it is necessary, in order to use equation (8), to put the different sub-components on a common age-scale with respect to the different factors affecting their age. This concept of relative age is a way to put the age of the different sub-components to a common base. The relative age is typically a value between zero to one, where zero means that the sub-component is new and one means that it has reached the accumulated stress for which it was designed and built. Note that a sub-component may have a relative age, Ai(x), larger than one.

6

i

ii v

xxA =)( , v ≠ 0 (9)

where xi is the accumulated stress and vi is the set accumulated stress limit the sub-component was designed to withstand. The advantage of using relative age is that it makes it possible to compare different sub-components with respect to their relative age, even though they might have different failure mechanisms.

2.2 Bayesian method When applying Bayesian methods for reliability data analysis the integration operation for calculating the normalising constant in (4) plays a very important role. This integral is rarely possible to evaluate using analytical methods, except in simple cases [6]. A way to overcome this difficulty is to use numerical techniques such as Monte Carlo simulation. This section describes a Monte Carlo based Bayesian method to model the reliability of a power system apparatus as presented in [2]. The method makes use of the concept of relative age and is based on a rejection sampling Monte Carlo technique first introduced in [6]. The general Bayesian method for making inferences or predictions is described in Figure 2, where DATA is the data set of observations of X. nxxx ,,, 21 K=x

&

Posterior f(θ|DATA)

Prior f(θ)

DATA

Model for DATA

Predictive posterior

f(x0|DATA)

Figure 2. The Bayesian method for making predictions, from [10].

The prior information about the parameter vector θ is expressed by a p.d.f. denoted f(θ). The likelihood for the available updating data and specified model is given by L(DATA|θ). Then, according to (2), the posterior distribution, representing the updated knowledge about θ, can be expressed by:

∫∫==

θθθθθ

θθθθθθ

dfRfR

dfLfLf

)()()()(

)()|DATA()()|DATA()DATA|( (10)

where the integral is computed over the region f(θ)>0 and R(θ) is the relative likelihood, as introduced in [2], such that:

)ˆ()()(θθθ

LLR = (11)

7

As more empirical data becomes available, the model is updated using the previous posterior data as prior information. In this way the model is improved as more data becomes available.

3 CIRCUIT BREAKER EXAMPLE

3.1 Model The CB is modelled as several sub-components in series, as is shown in figure 1. In this example the series system is made up of five different sub-components A, B, C, D and E, where E is a fictive sub-component with a constant failure rate in time. This may be thought of as all failures caused by other sub-components than A, B, C, or D, i.e. if a sub-components has only failed once it will not be allotted a box in the model. The sub-components all have different ageing factors. Failures on sub-components A and B depend on time and failures on C and D depend on the number of operations the CB has performed, as can be seen in Table 1.

Table 1. Ageing factors for CB sub-components. Sub-component Ageing factor

A Time B Time C No. of operations D No. of operations E Time

The lifetimes of the CB sub-components are assumed to be Weibull distributed random variables Xi~W(bi,ci) and the Weibull parameters are in turn considered to be gamma distributed random variables Bi~Γ(αb,rb) and Ci~Γ(αc,rc). The Weibull distribution with scale parameter b and shape parameter c is defined such that the p.d.f. is:

cc

bx

ebx

bccbxf

−

−

=

1

),|( (12)

The Weibull parameters are Gamma distributed with the p.d.f.:

−−

Γ= α

θ

αθαθ e

rrf r

r

)(),|(

1

(13)

where the gamma function is:

∫∞

−−=Γ0

1)( dxexr xr (14)

8

The relationship between the different parameters in the model is described in Figure 3 as a directed acyclic graph (DAG) model. Each quantity is represented as a node, the circles represent random variables, and the rectangles represent deterministic variables.

Bi

bα br cα cr

Ci

Xi

Figure 3. A DAG-model over the relationship between the variables, from [8].

In this CB example vA=vB=30 years and vC=vD=10000 operations is used for the relative age scaling. These limits are, in the CB example, found in the IEC standards [11].

3.2 Data Due to the difficulties in obtaining high quality failure data for power system components this CB example is presented using randomly generated test data. This data was generated so that it was within reasonable limits based on previous CB failure studies, such as [8] and [9]. The Weibull parameters for sub-component E are b=5 and c=1 and are not updated.

3.2.1 Prior information The prior information can be obtained from the tests and calculations carried out by the manufacturer during the design and construction process. In this example the prior information is a set of Weibull parameters, see table 3.

3.2.2 Updating information The updating information is empirical data obtained from maintenance records and failure statistics. In this example each sub-component has failed five times giving 25 observed failures during the studied time period. Hence, every sub-component model is updated using five observed lifetimes and 20 right-censored observations. It is assumed that no previous failures have occurred. The test data used in this example can be found in table 2. No further information about the total CB population is assumed to be known. In column one of Table 2 the failing sub-component is the component that failed and the rest are considered right-censored observations of X.

9

Table 2. Updating data. Failing

sub-component Time in

operation [yrs] No. of

operations A 14 83 A 19 121 A 18 99 A 5 50 A 13 253 B 20 83 B 25 145 B 26 121 B 29 97 B 3 345 C 10 2501 C 7 1890 C 12 3251 C 9 988 C 15 5478 D 8 6265 D 11 3888 D 12 5550 D 2 1600 D 4 2101 E 3 10 E 8 130 E 21 145 E 22 6120 E 30 901

4 RESULTS The prior Weibull p.d.f. for each sub-component supplied by the manufacturer is combined with a Weibull p.d.f. fitted to the empirical data in Table 2 by using equation (2), as described in Figure 2. The predictive posterior distribution for the sub-component lifetime is then obtained using equation (6). The sub-component failure rates are subsequently found using equation (7) and for the complete CB failure rate equation (8) is used. In Figure 4 the prior, updating and predictive posterior distributions of the sub-component lifetimes for sub-components A, B, C and D are found. Common for all sub-components is the large variance for the predicted lifetimes. This variance represents the uncertainty present when making predictions into the future. In Table 3 a summary of all Weibull parameters is shown.

Table 3. Weibull parameters for the prior, updating and posterior distributions. Sub-component A Sub-component B Sub-component C Sub-component D

b c b c b c b c Prior 1.0000 2.0000 0.9000 2.7000 0.7000 2.1000 0.3500 1.3000 Updating 1.1936 2.0170 1.0245 2.9157 0.6417 1.6035 0.5846 2.4165 Posterior 1.3394 1.4084 1.1523 1.8807 0.7407 1.1827 0.6140 1.5504

10

The expected value of the prior p.d.f. is 0.8862 for sub-component A and is 1.2196 for the updating p.d.f. This is the case even though all failures of sub-component A occurred before they reached the relative age Ai(x)=1. The reason for this is that there are many right-censored observations of sub-component A lifetimes exceeding those of the sub-component A failures, see Table 2. The left graph of Figure 5 shows the failure rate of all CB sub-components as a function of their relative age and the right graph shows the total predicted failure rate for the CB. It is clear that the total failure rate is dominated by sub-component D.

Figure 4. Prior, updating and posterior distributions for four CB sub-components.

11

Figure 5. Failure rates for all sub-components and the CB total failure rate.

Figure 6 shows the result if sub-component D, which is what dominates the total failure rate, is replaced when it reaches the relative age of Ai(x)=0.5. In this example it is assumed that the CB operates approximately 333 times/year, hence the relative age of 0.5 for sub-component D equals 5000 CB operations or 15 years in operation. It is assumed that the replaced sub-component is as good as new, i.e. its relative age is zero after replacement. Note that the x-axis is time in Figure 6.

Figure 6. Failure rates for all sub-components and the CB total failure rate after replacement

of sub-component D at AD(x)=0.5.

5 CONCLUSIONS This report proposes a method to model the reliability of power system components, using development test data from the manufacturer along with empirical data such as maintenance records and failure statistics. The two data types are combined using a Bayesian method by employing rejection sampling Monte Carlo. Using the proposed method it is possible to model the reliability of power system components with limited access to failure statistics. The level of detail of the data is what limits the accuracy of the model.

12

Furthermore, it is possible to model the reliability of sub-components that is affected by different ageing factors by the use the relative age concept. The proposed method can be used for assessing the effect of component maintenance in the form of sub-component replacement.

6 REFERENCES [1] Atella, F., Chiodo, E. and Pagano, M. (2001). “Dynamic discriminant analysis for

predictive maintenance of electrical components subjected to stochastic wear”, COMPEL, 21(1), pp 98-115.

[2] Meeker, W.Q. and Escobar, L.A. Statistical methods for reliability data. John Wiley & Sons Inc., 2001, ISBN 0-471-14328-6.

[3] Rausand, M. and Hoyland, A.. System reliability theory: Models and statistic methods. John Wiley & Sons Inc. 2nd ed., 2004, ISBN 0-471-47133-X.

[4] Blom, G. Probability theory with applications (Sannolikhetsteori med tillämpningar). Studentlitteratur, Lund, Sweden, 4th ed., 1989, ISBN 91-44-03594-2. In Swedish.

[5] Bayes, T., (1763). “An essay towards solving a Problem in the Doctrine of Chances” [WWW] http://www.stat.ucla.edu/history/essay.pdf (Bayes's essay in the original notation), January 20, 2005.

[6] Smith, A.F.M. and Gelfand, A.E. (1992). “Bayesian statistics without tears: A sampling-resampling perspective”, The American Statistician, 46(2), pp.84-88.

[7] Englund, G. Computer intensive methods for applied statistics (Datorintensiva metoder för tillämpad matematisk statistik). Course material, dept. of Mathematics, KTH, Stockholm, Sweden. In Swedish.

[8] Lindquist, T., Bertling, L. and Eriksson, R. (2005). “A method for age modeling of power system components based on experiences from the design process with the purpose of maintenance optimization”. In Proc. of the 51st Reliability and Maintainability Symposium (RAMS), Alexandria, Virginia, USA.

[9] Cigré. (1994). Final report of the second international enquiry on high voltage circuit-breaker failures and defects in service. Working Group 06.13. Report 83.

[10] Lindquist, T. (2005). On reliability modelling of ageing equipment in electric power systems with regard to the effect of maintenance. Licentiate thesis, dept. of Electrical Engineering, KTH, Stockholm, ISBN: 91-7178-054-8.

[11] IEC 62271-100. High-voltage switchgear and controlgear – Part 100: High-voltage alternating-current circuit-breakers. Ed. 1.1, (2003-05).

13

MONTE CARLO SIMULATION METHODS FOR RELIABILITY MODELLING AND ASSESSMENT

Jussi Palola

Helsinki Energy - Network Investments [email protected]

1 INTRODUCTION

System behavior can be seen stochastic by nature - this has been acknowledged since the 1930´s, and there is huge amount of publications dealing with development models, techniques, and applications of reliability assessment of power systems. [13] Complex systems, such as large transmission and distribution network, are possible to simulate and observe with the stochastic behavior of the reliability model. Where analytical equations describing the model give too simplified solution, comes Monte Carlo – simulation usable. [1] By itself deterministic analytical reliability assessment can lead to insufficient or overinvestment if severity of occurrence is observed without taking stochastic likelihood in account. Appropriate evaluation of likelihood and severity together creates indices that can represent system risk for the investment planning. [10] Power system reliability assessment can be divided into the two basic aspects of system adequacy (sufficiency) and system security. The existence of sufficient facilities to satisfy consumer load demand and to meet the system operational constraints of static condition, presents system adequacy. System security relates the ability to respond dynamic or transient disturbances arising within the system. [10] It is necessary to know that most of the probabilistic techniques presently available for reliability evaluation are concentrating on adequacy assessment. The ability to assess security is therefore limited. Past system performance indices includes both effects of inadequacy and those of insecurity, thus pure theoretical evaluations would be important to link on historical data for the result’s evaluation. [10]

2 RELIABILITY MODELING

Electrical power system is highly integrated and complex. That is why reliability assessment usually concentrates on selected functional zones such as generating stations, transmission systems and distribution systems. [5] Still a majority of interruptions in developed nations result from problems occurring between customer meters and distribution substations. [6]

2.1 Failure State Modeling

Failures are events where a device suddenly does not operate as intended, and failures occur in all parts of the power system. Cables are damaged, for example, lighting strikes, breakers suddenly open, transformers burn out, digging activities, etc. [11]

1

Figure 1. Basic reliability assessment scheme [11]

Every reliability analysis must start by recognizing and modeling all relevant failures which may affect the system’s reliability. [11] In addition to equipment, animals, vegetation and weather, humans are directly responsible for many customer interruptions. There are too many ways for humans to cause interruptions to analyze it exhaustively, but digging activities is one good example of it. Most common mistakes by utility workers can generally be classified into switching errors, direct faults, and indirect faults. [6]

2.1.1 Analytical Simulation

Analytical simulation models each system contingency, computes the impact of each contingency, and weights this impact based on the expected frequency of the contingency. At first glance it appears to be very close to a form of contingency analysis, as N-1 – criterion would be, expect that probabilities are assigned for each contingency case. [15] [3]

Component 1

Component 2

Component 3

Figure 2. Reliability block diagram 3 components in a series-parallel configuration. [3]

2

Analytic calculations range from matrix manipulations to state enumeration techniques. State enumeration means that all relevant failures will be analyzed one by one, as with the deterministic fault effect analysis. [11] They generally provide expectation indices in a relatively short computing time, but also assumptions are frequently required in order to simplify the problem. This is especially the case when complex systems and complex operating procedures have to be modeled. [13]

2.1.2 Markov Modeling

The Markov equation approach is sometimes called the state space method since it is based on a state space diagram. The main advantage of this technique is clear picture of all states and transitions between them. [7] Markov modeling is based on analyzing the states that a system could be in: states can be such things as “everything operating normally”, or “component is derated”. It focuses though, on analyzing the transitions between these states: it analyzes the probability that the system can move from operational condition to failure and also how long the restoration time would be from the failure. [9] Consequences view Markov modeling is especially appropriate when system details of the transitions between states are known or important. For example if planners want to study how different repair and replacement part policies would impact the availability of the system. [9]

aλ

µ

swµ

pλ

Up Out

31

rateRepairµ

rateSwitchingµ

ratetransitionfailurePassiveλ

raten transitiofailure Activeλ:Where

sw

p

a

=

=

=

=

Down 2

Figure 3. Example of a Three State Breaker Markov Model. [5] [13]

(1) State before fault (2) State after the fault but before component isolation (3) State after the fault isolation but before repair is completed Passive event: A component failure mode that does not have impact on the remaining healthy components. Active event: A component failure mode that can cause the removal of other healthy components and branches from service. [13]

3

Figure 4. Three-state operating cycle and Markov model for the base load unit. [10] Analytic calculations on the basis of the Markov model are normally much faster than Monte Carlo simulations, and they produce exact results. Variances can be produced also, but probability distributions are normally not realizable. The disadvantage of the analytic Markov methods is that there are severe limitations to the calculation of interruption costs indices. [11]

2.2 Monte Carlo Approach

The name of the Monte Carlo method and the systematic use of it date back to Second World War. Nuclear physicists at Los Alamos were working on the difficult problem of determining how far neutrons would travel through various types of materials. [3] [10] Monte Carlo simulation is similar to analytical simulation, but model’s random contingencies are based on probabilities of occurrence, rather than expected events. This allows component parameters to be modeled with probability distribution functions instead of expected values. For applications requiring determination of expected values of reliability, analytical simulation is the best method for distribution system assessment. Monte Carlo simulation becomes necessary if statistical results other than expected values are required: analysis of such things as the distribution of expected SAIDI from year to year. [9] [15] Monte Carlo simulation is the repeated chronological simulation of the power system. During each simulation, faults will occur randomly, as in real-life, and the reactions of the system to these faults are simulated chronologically. The performance of the system is then monitored during the simulations. [11] The advantage of Monte Carlo simulation is that all aspects of the power system can be addressed, and there are no limits to the stochastic models or to the possibilities for measuring performance. The disadvantages are the often very high computational demands. Monte Carlo simulation produces stochastic results only, which are inexact, but for which it is possible to calculate variances and even probability distributions. [11]

Up

Down

Derated

Repair

Partial repair

Complete repair

Complete repair

Down

Up

Time

21λ Unit Up 1

Unit Unit Derated 2 Down 3

12λ

32λ 23λ

31λ

13λ

4

2.3 Random-Number Generation

The name of the method points to uncertainty and is implemented with random-numbers. A simple physical random-number generator would be a dice, a coin or a roulette table [8]. Monte Carlo random-numbers are formulated usually with computer in the following form, and the output is a row of integers with values between 1 and

. [1] ( )N−1

( ) (NaUU ii mod1 =+ ) , 10 =U a and have to be chosen. N )/int(mod BABABA ⋅−= Usually resulting integer is divided with to get random number between 0 and 1. A popular value for is and the result would be a random draw from the uniform distribution between 0 and 1. [1] Random values generated with mathematical method are pseudo random numbers and they should be tested statistically to assure randomness. There are three basic requirements for a random number generation:

NN 1231 −

1. Uniformity: The random numbers should be uniformly distributed between [0,1]. 2. Independence: There should be minimal correlation between random numbers. 3. Long period: The repeat period should be relevantly long. [10]

Uniform distribution between 0 and 1

0

0,5

1

1,5

0 1000 2000 3000 4000 5000Amount of Random Numbers

Ran

dom

Num

ber V

alue

s

Generated Random Number

Figure 5. Five thousand random numbers generated with Excel’s Rand() – function and

correlation between other random number generation round was -0,000319. Random numbers should represent various reliability indices and that is why different shaped distributions are needed. [10] Probability distribution functions are mathematical equations allowing a large amount of information, characteristics, and behavior to be described by a small number of parameters. A deep understanding of each equation is not mandatory, but the minimum suggestion is to have conceptual understanding of probability distribution functions and their relation to reality. [6]

5

Studies of the distributions have associated with the basic reliability indices indicate that the load point failure rate is approximately Poisson-distributed. It has been noted that if the restoration times of components can be assumed to be exponentially distributed, the load point outage duration is approximately gamma-distributed. There are, however, many distribution systems for which the gamma distribution does not describe real indices. Probability distributions for the annual load point outage time, SAIDI, SAIFI and CAIDI indices can also not be presented by common distributions. [10] It is also possible to make empirical distributions for the random numbers in order to have more case sensitive results. Empirical distributions are especially used in finance market analysis. Where, for example, oil stock investment has a characteristic risk profiles generated for the return on investment with tens of years experience. [12] It should expected therefore that SAIDI, SAIFI and CAIDI statistics obtained from actual circuit or system operating behavior will vary over time as each year is simply a snapshot of a continuum in time. The ability to generate the index distribution by Monte Carlo provides the opportunity to appreciate and quantify the deviations associated with these important customer parameters. [10]

2.4 Sequential Modeling

Sequential Monte Carlo method refers to a simulation process over a chronological time span although the length of the time sequent can be changed depending on dynamic characteristics of the system. There are different approaches to create an artificial system state transition cycle. The most popular one is the so-called state duration sampling. [6]

2.4.1 State Duration Sampling Approach

The state duration sampling approach is based on sampling the probability distribution of the component state duration. In this approach, chronological component state transition processes for all components are first simulated by sampling. The chronological system state transition process is then created by combination of the chronological component state transition processes. The advantages of the state duration sampling approach are:

+ It can be easily used to calculate the actual frequency index. + Any state duration distribution can be easily considered. + The statistical probability distributions of the reliability indices can be

calculated in addition to their expected values. [10] The disadvantages of this approach are:

6

- Compared to state sampling approach, it requires more computing time and storage because it is necessary to generate random variants following a given distribution for each component and store information on chronological component state transition processes of all components in a long time span.

- This approach requires parameters associated with all component state duration distributions. Even under a simple exponential assumption, these are all transition rates between states of each component. In some case, especially for a multistate component representation, it might be quite difficult to provide all these data in an actual system application. [10]

2.5 Continuous Monte Carlo Simulation

Continuous Monte Carlo simulation is sometimes called the state sampling approach. It is widely used in power system risk evaluation. The concept is based on the fact that a system state is a combination of all component states and each component state can be determined by sampling the probability of the component appearing in that state. [7] Let’s have a simple example about hybrid analytical - Monte Carlo state sampling approach: in the figure below is simplified substation model. Substation is connected at two high voltage transmission lines and has two main power transformers and medium voltage busbars. The aim is to simulate medium voltage feeder A state reliability with continuous Monte Carlo method. Reliability data for the components is combined from Helen Network, Nordel and Reliability Evaluation of Power Systems [13] – statistics. Table 1. Reliability statistics for the substation state sampling simulation.

Reliability data from Helen Network and NORDEL statistics Element Faults/year λ= Reliability λ−= e

110 kV overhead line 0,0218 0,97844

110 kV breaker 0,00238 0,99762

Power Transformer 0,0038 0,99621

Medium voltage breaker 0,000412 0,99959

20 kV Feeder 0,0536 0,94781

7

Figure 6. Simplified reliability model for a substation feeder A. For each component, a random number between zero and one is generated. If this random number is less than Rx , no failure will occur in the simulation round. If the number is greater, then component fails and system state is achieved by collecting all component states with summary conclusion. The essential requirement of a reliability assessment in this case is to identify whether the failure of a component or a combination of components causes the failure at the load point A. [13] [16] [15] [10] Table 2. Result from the Monte Carlo state sampling simulation with macro-program. [16]

R1 R2 R3 R4 SYSTEMReliability--> 0,999429223 0,993429208 0,94742119 0,993020473

Random number this iteration--> 0,3802 0,7190 0,3476 0,2337 Success: (Random) < (R-value)

Success = (1) this iteration--> 1 1 1 1 1Failure = (0) this iteration--> 0 0 0 0 0

Cumulative successes--> 19983 19878 18905 19851 18769

Cumulative failure--> 18 123 1096 150 1232Total Iterations--> 20001 20001 20001 20001 20001

Simulated Reliability--> 0,9991 0,9939 0,9452 0,9925 0,9384Theoretical Reliability--> 0,9994 0,9934 0,9474 0,9930 0,9407

% Error--> -0,03 % 0,04 % -0,23 % -0,05 % -0,24%

A

T1 T2 Analytically

simplified for the Monte

Carlo simulation

HV HV R1

R2 R4

MV

R3

MV

A

8

Monte Carlo simulated reliability for the whole system is 0,9384 , which differs -0,24 percent from analytical result. 1232 simulations led to system failure and 18769 to success, and it presents the result of 20001 simulation rounds altogether. In general, advantages of the state sampling approach are:

+ Sampling is relatively simple. It is only necessary to generate uniformly distributed random numbers between [0, 1], without need to sample a distribution function.

+ Required basic reliability data are relatively few. Only the component-state

probabilities are required. + The idea of state sampling not only applies to component failure events

but also can be easily generalized to sample states of other parameters in power system reliability evaluation, such as load, hydrological, and weather states, etc. [10]

The disadvantage of this approach is that it cannot be used by itself to calculate the actual frequency index. [10]

2.6 Weather Simulations

In generally weather conditions are one of the main causes of equipment failures: a worldwide survey over several years in the 1980´s indicated that about 20 percent of failures were arising from weather conditions. [14] Electrical network characteristics affect what kind of impact different environment conditions have on system functionality. For example urban underground network is more vulnerable to floods than overhead line delivery network - on the contrary bare overhead line conductors are more open to thunder and snowstorms. During normal weather, equipment failures are considered to be independent events, but in the severe weather conditions many equipment failures can occur at the same time and we are discussing about common cause failures [17]. This strains utility resources and can lead to long restoration times for many interrupted customers and also to penalty payments. [6] This we have seen recently happening in Sweden and Finland rural area networks, where over headline distribution network suffered interruptions due to storm weather conditions.

9

Figure 7. Failure rate as a function of time - normal and adverse weather. [1] Weather conditions can be modeled in long term view with Monte Carlo. For example, component which is sensitive for certain weather effects can have changing failure rate modeling in the basis of two-state weather function. [1]

2.6.1 System State Transition Sampling Approach

This approach focuses on state transition of the whole system instead of component states or component state transition processes [7] [10]. The advantages of this approach are:

+ It can be easily used to calculate the exact frequency index without the need to sample the distribution function and storing chronological information as in the state duration sampling approach.

+ In the state sampling approach, m random numbers are required to obtain a system state for an m-component system. This approach requires only a random number to produce a system state. [10]

The disadvantage of this approach is that it only applies to exponentially distributed component state durations. It should be noted, however, that the exponential distribution is the most commonly used distribution in reliability evaluation. [10]

2.7 Monte Carlo Simulation Error Evaluation

Selection of a stopping rule is an important factor in Monte Carlo simulation. The stopping criterion can be the fixed number of samples, or the coefficient of variation tolerance, or combination of both. A large number of samples or a small tolerance

10

coefficient can provide relatively high accuracy in reliability indices, of course with increased calculation time also. [10] [1]

Outcomes of five Monte Carlo Simulations

0,24

0,26

0,28

0,3

0,32

0 5000 10000 15000 20000 25000 30000Number of Samples

Ave

rage

Res

ults

Figure 8. Monte Carlo reliability simulations for two component parallel system. [16] Variance reduction techniques can be used to improve the effectiveness of Monte Carlo simulation. The variance cannot be reduced to be zero and therefore it is always necessary to utilize a reasonable and sufficiently large amount of samples. [10] [1]

2.7.1 Sensitivity Analysis

Sensitivity analyses are useful for many aspects of system analysis. It is useful when aim is to mitigate uncertainty concerning reliability data. Secondly, sensitive result can be used to calibrate systems to historical reliability data. Latest, sensitivity analyses can be used to find the most effective actions to have a significant reliability impact on the system. For example, if system SAIDI is highly sensitive to feeder failure rates, reducing cable failures will probably reduce SAIDI. By itself this analysis would not necessarily give cost effective result, but a view where is effective to invest, if any where. [6]

11

Sensitivities of reliability indices to equipment failure rates

0

5

10

15

20

25

30

35

40

Padmount Switches Substation Transformers Underground Cables

Sens

itivi

ty to

Fai

lure

Rat

e %

SAIFI

SAIDI

Figure 9. Example from southern US. military base distribution system. [6] Here SAIFI is least sensitive and SAIDI is most sensitive to the failure rate of substation transformers. Depending on reliability improvement goals, addressing substation transformer failures could be very effective for reducing SAIDI and very ineffective to reduce SAIFI. [6] In principle, system reliability becomes more predictable as system size increases. [10]

3 RELIABILITY ASSESSMENT

The main aims for the reliability assessment are to design new systems to meet explicit reliability targets and identify reliability problems on existing systems and due to system expansion. Also effectiveness of reliability improvement projects and to design systems that are best suited for the customer view performance based rates is important. [9] The assessment of the reliability of a power system means the calculation of a set of performance indicators. Two different sets of indicators can be distinguished: local indicators and system indicators. Local indicators are calculated for a specific point in the system. Example from local indicator could be the interruption costs per year for a specific customer. System indicator like, the average number of interruptions per year, per customer, express the overall system performance. [11]

3.1 Worth Assessment Using the Cost of Interruption Data

One approach which can be used to start reliability assessment is to relate it to the cost for the customer when power delivery fails. Standard loading classifications, where consumer sectors are identified, makes data processing reasonable. The next step is to create individual Customer Damage Functions (CDF) to relate the outage consequences to cost associated. It can be updated with cost of interruption to customer questionnaires, and also other option is to upgrade values with relation to generally know indexes like Gross Domestic Product, Consumer Price Index and energy consumption [4]. [2]

12

3.2 Using Monte Carlo Simulations for Analyzing Quality Costs

Stochastic methods can produce system performance rates and with the customer damage functions, reliability level can be transformed in to a cost of interruptions. And by taking reliability investments in account we are able to start working with reliability investments optimizing challenge. Operation, quality and maintenance cost functions are dependent on the investments and therefore they should be optimized altogether in the same examination in order to have the best investment reference acknowledge. This approach is needed specially to make investment planning more comprehensive and transparent. Figure 10. Simple model for the network investment cost function structure. Monte Carlo simulations can produce, for example, SAIDI distributions for the certain network in order to avoid interruption penalties or such. [9] [15] Next example figure is result of a 1000 random year simulation for real U.S. utility distribution system: where is three voltage levels, nine substations, more than 770 kilometers of feeder and approximately 8000 system components altogether. [15]

13

Figure 11. Performance based rate SAIDI for the quality cost evaluation. [15]

Monte Carlo method can be used to specify characteristics for the optimized reliability assessment management of the power system altogether with other reliability modeling methods. Monte Carlo method’s specialty is modeling events with stochastic input in order to found uncertainty distribution bounded to analytical results.

4 SOURCES

1. Math H. J. Bollen, Understanding Power Quality Problems – Voltage Sags and Interruptions, IEEE Press, New York, 2000.

2. A. Sankarakrishnan, R. Billinton, Effective Techniques for Reliability Worth Assessment in Composite Power System Networks Using Monte Carlo Simulation, IEEE Transactions on Power Systems, Vol 11, No. 3, August 1996.

3. K. E. Forry, A General Monte Carlo Simulation Model for Estimating Large Scale System Reliability and Availability, Clemson University, Ph. D., Electrical Engineering, U.S.A. 1972.

4. K. Kivikko, Extrapolating Customer Damage Functions, Presentation 16.6.2005 in Espoo Technical Research Centre of Finland (VTT).

5. R. Billinton, G. Lian, Station Reliability Evaluation Using A Monte Carlo Approach, IEEE Transactions on Power Delivery, Vol 8, No. 3, July 1993.

14

6. Richard E. Brown, Electrical Power Distribution Reliability, ABB Inc., Raleigh, North Carolina, Marcel Dekker, New York 2002.

7. Wenyuan Li, Risk Assessment of Power Systems – Models, Methods, and Applications, IEEE Press Series on Power Engineering, Wiley & Sons 2005.

8. I. M. Sobol, The Monte Carlo Method, Little Mathematics Library, Mir Publishers, Moscow 1975.

9. H. Lee Willis, Gregory V. Welch, Randall R. Schrieber, Aging Power Delivery Infrastructures, ABB Power T&D Company Inc. Raleigh, North Carolina, Marcel Dekker 2001 New York.

10. Roy. Billinton, Wenyuan Li, Reliability Assessment of Electrical Power Systems Using Monte Carlo Methods, Plenum Press, New York 1994.

11. Jasper Van Casteren, Assessment of Interruption Costs in Electric Power Systems using the Weibull-Markov Model, PhD, Department of Electric Power Engineering Chalmers University of Technology, Gothenburg, Sweden, 2003.

12. VAR, Understanding and Applying Value-at-Risk, Risk Publications, Financial Engineering Ltd 1997 London. and Monte Carlo – Methodologies and Applications for Pricing and Risk Management, Risk Publications, Financial Engineering Ltd 1998 London. Also conversation with Helen Trading portfolio manager, Jaakko Kontio.

13. Roy Billinton, Ronald N. Allan, Reliability Evaluations of Power Systems, second edition, Plenum Press, New York 1996.

14. U. G. Knight, Power Systems in Emergencies, from Contingency Planning to Crisis Management, John Wiley & Sons Ltd 2001.

15. H. Lee Willis, Power Distribution Planning Reference Book, second edition – revised and expanded, Marcel Dekker Inc., New York – Basel 2004.

16. Modified H. Paul Barringer MS Excel macros (MCSample.xls) by Jussi Palola and Petteri Haveri.

17. Liisa Pottonen, A Method for the Probabilistic Security Analysis of Transmission Grids, Doctoral Dissertation in Helsinki University of Technology, Espoo 2005.

15

LAPPEENRANNANTEKNILLINEN YLIOPISTO

LAPPEENRANNAN TEKNILLINEN YLIOPISTO

SÄHKÖTEKNIIKAN OSASTO

PL 20, 53851 LAPPEENRANTA, p. 05 62111, fax. 05 621 6799

DIFFERENT MAINTENANCE STRATEGIES AND THEIR IMPACT ON

POWER SYSTEM RELIABILITY

Anna Brådd Researcher, Lappeenranta University of Technology

[email protected]

1 Introduction

These days utilities are forced to rethink their maintenance strategies as well as new invest-ments must be carried out as effectively as possible due to the hardening competitive electric-ity market. In order to survive on a deregulated power market utilities simply have to be as cost effective as possible. Cost effectiveness today means minimizing costs and at the same time living up to the demand of reliability from customers and regulatory. This cost may be divided into cost of failure, cost of preventive maintenance and capital costs. [1] This paper deals with managing of different maintenance strategies and their impact on power system reliability. Taking a look at preventive maintenance means facing extensive renewals- buying new com-ponents or refurbishing a component into a new condition. Alas, when planning of making remarkable renewals it becomes important to find the point in time when such a renewal must be carried out. By postponing renewal, capital costs are saved. But on the other hand, the risk for a costly failure increases. In maintenance management it becomes very important to be as effective as possible when deciding about the point in time for a renewal. [1] Obviously, every manager's intention is to run the equipment as much as possible without costly breakdowns. Moreover, the fastest way to increase earning in a short-term perspective is to cut down on maintenance and postpone renewals. In many cases this kind of approach has been proven successful due to the very long operative lifetime of many components and the inherent reliability. However, in a long time perspective this might still not be so cost ef-ficient. In every case, the network manager has to manage the assets he is responsible for; which means that he can make investments just to meet the needs of today or the needs in a long-term perspective, say 25 years. The question here really is, what will the total costs be-come and what condition will the technical system be in? [1] Power system reliability is affected by strategic planning of network structure and layout, choices made regarding use of network components, increase of system quality, electrical quality requirements of the end customer, geographical constraints in the network as well as maintenance activities. Currently, when the economical situation of electrical utilities is quite constraint, utilities are forced to get the most out of the devices they already own through more effective operating policies including improved maintenance programs. All in all it is fair to say that maintenance is becoming an important part of network related asset manage-ment. [2]

http://www.ee.lut.fi


2

2 Presentation of maintenance strategies

Two fundamental maintenance strategies to keep in mind when talking about maintenance are i) replacement by a new component (or "good as new") and alternatively ii) replacement with a less costly component facing a limited improvement of the component's condition. More-over methods are divided into categories where maintenance is performed at fixed intervals and alternatively where it is carried out as needed. These methods are further divided into heuristic methods and other mathematical models, where the models can be deterministic or probabilistic. [2] Later in the paper we will take a closer look especially at a heuristic based method called reliability centered maintenance (RCM) as well as probabilistic mathematical models. In order to determine an optimal inspection and replacement policy, a particular strategy is usually pre-specified by maintenance planners using practical considerations such as limita-tion of technology, cost of equipment and simplicity of implementation. Basic maintenance strategies in this sense are; failure replacement, age replacement, sequential replacement, pe-riodic inspection and continuous inspection. [4]

2.1 Short history on the development of maintenance

Through the years electric utilities have always relied on maintenance programs to keep their equipment in good working condition for as long as it's feasible. Maintenance routines con-sisted in many cases of pre-defined activities carried out at regular intervals, which is also known as scheduled maintenance. However, such a maintenance policy may be quite ineffi-cient in the long run, being perhaps too costly and may still not extend component lifetime as much as possible. After this period and during the last ten years utilities have in many cases replaced their fixed-interval maintenance schedules with more flexible programs based on an analysis of current needs and priorities or information obtained from continuous condition monitoring. [2]

2.2 Definition of network maintenance and its central characteristics

The purpose of maintenance is to extend equipment lifetime, or at least the mean time to the next failure whose repair may be costly. Effective maintenance policies can reduce the fre-quency of service interruptions and other undesirable consequences related to such an inter-ruption. [2] Maintenance activities naturally affect network component and system reliability- if too little is done the result can be an excessive number of costly failures and poor overall system per-formance, which degrades the reliability of the system. On the other hand, if maintenance ac-tivities are done too often reliability may improve but the cost of maintenance will sharply increase. Therefore, in a cost-effective scheme both variables, maintenance and reliability, must be balanced. We should still keep in mind that maintenance in the end is only one of many factor that affects component and system reliability. Maintenance is one of the operat-ing activities in network business and it is selected to satisfy both technical and financial con-straints. [2]

3

2.3 Maintenance and its relation to reliability-centered maintenance (RCM)

In order to compare different maintenance strategies one can use a so called RCM approach, which selects the most cost-effective strategy for sustaining equipment reliability. RCM pro-grams have been taken into use in several electric utilities as a useful management tool. The implementation of RCM programs started a new era in the development of maintenance and the direction of "getting the most out" of the equipment installed. This RCM approach is how-ever heuristic meaning that its application requires experience and constant judgment. An-other fact is that it can take quite a long time before enough data are collected for making such judgments. Keeping this difficulty in mind, several mathematical methods have been proposed to aid scheduling of maintenance and the literature if maintenance models has be-come extensive. [2]

2.4 Maintenance approaches

Starting from the strategic level it is safe to conclude that maintenance is today a central part of asset management in electric utilities. To a vast extent the maintenance literature still con-cerns itself basically with replacements only, both after failures and during maintenance and doesn't take into account the kind of maintenance where less improvement is achieved at smaller cost. The oldest replacement schemes are the age replacement and bulk replacement policies. In the age replacement scheme a component is replaced at a certain age or when it fails, whichever comes first. With bulk replacement policies on the other hand, all devices in a given class are replaced at predetermined intervals, or when they fail. Because of the large scope in this lastly mentioned policy it easily understood that it can be more economical than a policy based on individual replacement, especially if the ages of the components are un-known. [2] When talking about newer replacement schemes, they are often based on probabilistic models and can thus be quite complex. The result can be seen in electric utilities where maintenance resulting in limited improvement is an established practice and replacement models have only a secondary role. [2]

2.4.1 A basic maintenance program

Generally we can say that maintenance programs range from very simple programs to quite sophisticated ones. An example of a very simple maintenance program is a case where pre-defined activities are carried out at predefined intervals- whenever a component fails it is then repaired on replaced. However, both repair and replacement are considered much more costly than a single maintenance job. The maintenance intervals are chosen on the basis of long-time experience, which is not necessarily an inferior alternative to different mathematical models. In fact, to this day this is the approach most frequently used among utilities. [2]

2.4.2 The RCM approach

Another approach, that deserves to be looked at is the RCM approach, which is based on regular assessment of equipment condition and is thus not directly in the business of applying tight maintenance schedules. Reliability Centered Maintenance is a strategy where mainte-nance of system components is related to the improvement in system reliability. The approach is not however always based on condition monitoring, but on other features like failure modes

4

and effects analysis and an investigation of operating needs and priorities. Normally the ap-proach is always empirical. A general RCM process could include the following stages:

1. Listing of critical components and their functions 2. Failure mode and effects analysis for each chosen component, determination of

failure history and calculation of mean time between failures. 3. Categorization of failure effects and determination of possible maintenance tasks. 4. Maintenance task assignment. 5. Program evaluation, including cost analysis. [2]

A slightly different way of presenting the RCM process has been done in Sweden [3], and here the steps are:

1. Choosing and defining of a system to analyze 2. Defining and collecting necessary input data 3. Performing of a reliability analysis of the chosen system 4. Identifying of critical components 5. Identifying of the benefit of maintenance of the critical components from an

knowledge of either its functions (for example transfer of energy), its failure modes (for example short circuit), its failure events (for example insulation fail-ure), its failure causes (for example material and method), the %-contribution of each failure cause to total failures using statistics or the knowledge of which causes can be affected by preventive maintenance. [3]

In itself the RCM procedure does not actually bring anything new to the complexity of the question regarding cost-efficient maintenance. However, it provides a formal framework to address this multi-dimensioned question. One concrete output we get from the RCM proce-dures are RCM plans. Regarding the RCM method we can identify three main procedures, which are; sensitivity analysis, deduction of relationship between system reliability and main-tenance and finally cost optimization. In the following table the procedures mentioned before, required input data and the need for interaction between system and component level is pre-sented. [3] Table 1: Presentation of RCM methodology and its main procedures Procedure Level Required data Result Reliability analysis System Component data Reliability indices Sensitivity analysis System Component data Critical components Analysis of critical com-ponents

Component Failure modes Critical components af-fected by maintenance

Analysis of failure mod-els

Component Failure modes, causes of failures

Frequency of mainte-nance

Estimation of composite failure rate

Component Maintenance frequency Composite failure rate

Sensitivity analysis System Maintenance frequency Relation reliability indi-ces and PM schedules

Cost/Benefit analysis System Costs RCM plan

5

2.4.3 The PREMO approach

A third suggested maintenance approach, claimed to be more efficient than the RCM ap-proach, is called Preventive Maintenance Optimization (PREMO). PREMO is based on ex-tensive task analysis rather than system analysis, with a capability of radically reducing the required number of maintenance tasks in a plant. Both the RCM and PREMO program have proven themselves useful in ensuring the economic operation of power stations. Still, none of them will provide the full benefits and flexibility of programs based on mathematical models. [2] In order to get a thorough evaluation of the effects of a maintenance policy, one has to know by how much its application would extend the lifetime of a selected component, for example measured in mean time to failure. This kind of answer is only obtained using a mathematical model of the component deterioration process, which is then combined with a model describ-ing the effects of maintenance. Several such models have been proposed during the last ten years. Finally, they provide the missing link from earlier mentioned approaches, that is- a quantitative connection between reliability and maintenance. [2]

2.4.4 The use of mathematical approaches

Starting from the simpler mathematical models it is safe to say, that they are essentially still based on fixed maintenance intervals, and optimization will result in identifying the least costly maintenance frequency. More complex models include the idea of condition monitor-ing, where decisions related to the timing and amount of maintenance are dependent on the actual condition, or stage of deterioration, of the device. From this we can conclude that some kind of monitoring must be part of the model. Examples of other desirable features which should be built into maintenance models are i) to insert inspection states before proceeding to maintenance ( in the case of predictive maintenance where maintenance is performed only when needed- during inspections decisions are made as to the necessity of carrying out main-tenance at that time) and ii) the effect of the break-in period of the beginning of a device's life (when failure rate is possibly quite high). [2] Once a mathematical model is constructed, the process itself can be optimized with changes in one or more of the variables. [2]

2.5 Linking power system reliability and maintenance - use of mathematical models

Commencing from the simplest maintenance policies we are facing i) a set of instructions taken from equipment manuals or ii) a long-standing experience. In both cases there are no quantitative relationships involved and the possibilities are quite limited for making predic-tions about the effectiveness of the policy or carrying out any type of optimization. It is now we come concretely in touch with mathematical models- models that make numerical predic-tions and carry out optimization, and thus show us the effects of maintenance on power sys-tem reliability. [2] Mathematical models are either deterministic or probabilistic. Both models are useful in ap-propriate maintenance studies. In this paper we will however closer inspect the probabilistic

6

model, since it is the only one that can properly handle the future uncertainties associated with quantities involved whose values vary randomly. [2]

2.5.1 Failure models

Generally speaking, component failures can be divided into two categories- random failures and those arising as a consequence of deterioration. Failure-repair processes are shown in the following figure 1.

Figure 1: State diagrams for i) random failure and ii) deterioration process The deterioration process is represented by a sequence of stages of increasing wear, finally leading to equipment failure. Naturally, the number of deterioration stages may vary and also we should keep in mind that in reality deterioration is a continuous process in time and not consisting of discrete steps used in this model. Deterioration stages can be defined either by duration ( second stage normally at three years, then the third in six etc.) or physical signs (corrosion, wear). This second approach is often in use in practical applications, which makes periodic inspections necessary to determine the stage of deterioration the device has reached. Continuing with this case, the mean times between the stages are usually uneven, and are se-lected from performance data or by judgment based on experience. [2] The process presented in figure 1 can also be represented by a mathematical model, called the Markov model, assuming that the transitions between the states occur at constant rates. Fur-thermore, well-know techniques exist for the solution of such models.

2.5.2 The effect of maintenance on the failure models

The purpose of maintenance is to increase the mean time to failure. Keeping this in mind, one way to put maintenance states to the models is shown in figure 2. In this figure it is assumed that maintenance will bring about an improvement to the conditions in the previous stage of deterioration. Frankly, this contrasts with many strategies described in the literature, where maintenance involves replacement - meaning a return to the "new" conditions. [2]

Work Failure D1 D2 Dk Failure

i) ii)

7

ies

nimal costs. [2]

]

Figure 2: Maintenance states for random failure and deterioration process This model represents only one of the several ways of accounting for the effect of maintenance on reliability. Many other approaches have been developed and of these at least two have been specifically concerned with power system applications. One of these two maintenance models is derived from parallel branches of components in series for example in transformer stations and the other model is concerned with the maintenance and reliability of standby devices. [2]

2.6 Present maintenance policies - results from the Task Force's questionnaire to utilit

According to the report "The present status of maintenance strategies and the impact of maintenance on reliability" maintenance at fixed intervals is the most often used approach, though often augmented by additional corrections. On the other hand newer so called "as needed"-type methods like reliability-centered maintenance (RCM) are increasing their favor in North America, but methods based on mathematical models are hardly ever used or even considered. In a way this is silly practice since mathematical models offer the only way to quantitatively link component deterioration and condition improvement by maintenance. Regarding these mathematical models we can state that even though probabilistic models are more complex they have clear advantages over the deterministic ones: they are capable of describing actual processes more realistically and at the same time they also facilitate optimization for maximal reliability or mi The task force had prepared a questionnaire to utilities in order to form a overview of the present maintenance practices. Replies were received from 6 countries including Austria, Canada, Germany, Italy, Saudi Arabia and the United States. Generally the answers to many questions displayed a considerable spread. Many utilities do scheduled maintenance only, or a modified form of it where additional corrective actions are taken if required by inspection results. Only a few utilities reported of exclusive use of predictive maintenance. [2

Work Failure D1 D2 Dk Failure

i) ii)

M M M M

8

2.6.1 Scheduled and predictive (as needed) maintenance

The intervals and durations reported for scheduled maintenance show considerable spread and for example cyclic routine is rare among the answers for the questionnaire. Furthermore the most frequent maintenance values for critical components are given in table 2. [2] Table 2: The reported most frequent maintenance intervals and durations Generators Transform-

ers Breakers

Interval Duration Interval Duration Interval Duration Minor main-tenance

1 year 1-2 weeks 1 year 1 day 1 year 1 day

Minor over-haul

5 years 4-5 weeks 5 years 3 days 5 years 3 days

Major over-haul

8-10 years 6-8 weeks 7 years 4-8 weeks 8-10 years 2 weeks

Regarding predictive maintenance, the most popular devices to detect maintenance needs were found to be periodic inspection and continuous monitoring. Here, looking for example at transformers, a big variety in periodic inspection intervals was found ranging from 1 week to 5 years. Continuous monitoring was widely used especially for generators, regarding for ex-ample oil leakage, vibration and bearing temperature, but also for smaller equipment regard-ing corrosion and discharge voltage. [2]

2.6.2 RCM maintenance

From the answers to the task force's questionnaire we can see that the RCM-procedure is not yet generally used and even more rarely outside North America. Luckily however, about half of the correspondents were at that moment considering the introduction of RCM. The RCM-procedure was reported to be most used with transformers. Furthermore, the expected benefits for those considering the use of RCM in the future were; longer up-times, lower costs, better use of labor and better control and decisions. [2]

2.6.3 Probabilistic models

Generally it can be seen from the answers that probabilistic approaches are not used in main-tenance planning at all. However, some of the utilities reported on pilot applications, while some had hired external consultants who may use these probabilistic models. Finally, many of the respondents wished to compute indices as failure frequency and duration as well as un-availability. [2]

2.6.4 Maintenance data requirements

Maintenance data requirements will now shortly be viewed separately for generators, trans-formers and breakers. Regarding generators, current maintenance strategies are primarily based on historical records like performance indices, inspection records and maintenance data. On top of this generator manuals and thorough experience were listed as important data sources. [2]

9

If we look at transformers on the other hand, the most frequently mentioned maintenance re-lated data were test reports, data on windings, failure data, maintenance history and mainte-nance protocols. Moving further to the breakers, often used data were among others mainte-nance history, operation logs, failure statistics and faulty operation counts versus total number of operations. [2]

3 Conclusions

The relationship between power system reliability and maintenance strategies is not unambi-guous. One accepted way of understanding the relationship between maintenance and net-work reliability is to study the causes of network failures. Striving for a cost-effective plan-ning of a network system in the changing competitive electrical market environment means maintenance of the right components, at the right time and with the right maintenance activ-ity. Continuing from this statement, it is safe to say that, maintenance should be focused on the critical components- those that have a significant impact on system reliability- and to fur-thermore reduce the dominant causes. [3] Mathematical models are useful tools to represent the effects of maintenance on power system reliability. Algorithms are used for deriving optimal maintenance policies to minimize the mean long-run costs for continuously deteriorating systems (Markov). [4] In a RCM-application to an urban distribution system in the Stockholm city area, the follow-ing results were concluded:

• There is a need to identify the critical network components • A comprehensive understanding of failure causes has to be obtained • Preventive maintenance programs should not be created by considering components in

isolation, but always as a part of the whole system. [3] According to a present study regarding the relationship between preventive maintenance and reliability of a distribution system the results show that benefits are obtained by focusing maintenance both on critical network components as well as the dominant causes of failures for which maintenance can impact. Finally, it can thus be concluded that preventive mainte-nance can have a significant impact on the network system reliability and the relationship be-tween failure rate and maintenance should be analyzed on a case-by-case basis.[3]

References

10

[1] Cired, 18 th international conference and exhibition on electricity distribution, special reports, session 6, Maintenance [2] A report of the IEEE/PES Task Force on Impact of maintenance Strategy on reliability of the net work, Risk and Probability Applications Subcommittee, J.Endrenyi et al., "The Present Status of Maintenance Strategies and the Impact of Maintenance on Reli ability", IEEE transactions on Power System, VOL.16, NO.4, November 2001 [3] L.Bertling, R.Eriksson, R.N. Allan, "Relation between preventive maintenance and reliability for a cost- effective distribution system", IEEE Porto Power tech Conference, Portugal, 2001 [4] C.Teresa Lam, R.H.Yeh, "Optimal Maintenance-Policies for deteriorating Systems Under various Maintenance Strategies", IEEE Transactions on reliability, VOL. 43, NO.3, 1994

1

MAINTENANCE OPTIMIZATION TECHNIQUES FOR DISTRIBUTION

SYSTEMS

Sirpa Repo

Vattenfall Distribution Finland

[email protected]

INTRODUCTION

Maintenance, in general, can be defined as the combination of technical and associated

administrative actions intended to retain an item or a system in, or restore it to a state in which it

can perform its required function. Objectives can be classified in four categories: ensuring system

function (availability, efficiency and product quality), ensuring system life (asset management),

ensuring safety and ensuring human well-being. [1]

Target of maintenance is to extend equipment lifetime and/or reduce the probability of failure.

That is to increase reliability. Other objectives could be to reduce risks or appearance and worth

of existing equipment. Objective is to find right level for maintenance targets. This isn’t usually

the maximum.

Maintenance is especially important in areas where large amount of capital is invested in

equipment and technical systems, and this is the case in distribution systems.

MAINTENANCE OPTIMIZATION

Maintenance optimization is basically a mathematical model. In the model costs and benefits of

maintenance are quantified and optimal balance between both is obtained. The model is objective

and quantitative way of decision-making. With maintenance optimization models different

policies can be evaluated and compared. As a result of maintenance optimization maintenance

plans or timetables can be created. [1]

Maintenance optimization consists of four parts, first is a description of the system, its function

and importance. Secondly a description of the deterioration and what kind of affects it has on the

system. Thirdly a maintenance optimization covers information about system and actions that are

possible to implement and finally an objective function and an optimization technique to find

balance between costs and reliability. [1]

There are many problems in creating maintenance optimization methods in general. First of all

models for maintenance optimizations aren’t simple and easily transformable into mathematical

calculations. Therefore software programs are needed. Developing these programs isn’t easy, for

example because of lack of specialized staff or unclear formulation of problem. Programs may

not be user-friendly and are left not used. [1]

2

One of the biggest problems in maintenance optimization is lack of data. Key point of

maintenance optimization is to know how deterioration can be modeled and what the occurrences

of equipment failures are. Mechanisms behind failures have to be known before making

maintenance decisions, relying on average failure rates etc. isn’t cost-effective. Collecting this

data requires lot of effort. [1]

Besides getting technical information about components and their condition, cost data is also

needed in order to create an optimization model. Costs of maintenance are usually easily

calculated, but benefits of maintenance are more difficult to quantify. In optimization you also

have to think of the costs that are realized after equipment failure. [1]

In electricity distribution systems equipment failure usually leads into a power failure. How to

value costs that result from power failure? Distribution system operators can have many way of

defining these costs and customer and society probably have a different opinion of the value of

electricity. From distribution system operator’s point of view costs of power failure are at least

electricity not delivered and cost of restoration, in addition to that reduced power quality and

customer dissatisfaction.

Dekker [1] has analyzed use of maintenance optimization models in general. There are some gaps

between theory and practice in this field. Most of the models are difficult to understand and

interpret, maintenance optimization uses in many cases stochastic processes and most of the

people are used to deterministic approach. Also many of the research papers in the field are

written only for mathematic purposes and companies in industry aren’t enthusiastic about

publishing their optimization methods.

MAINTENANCE IN DISTRIBUTION SYSTEMS

Performance of distribution system is related to performance of its components. Equipment type,

age, condition and exposure of events effect on equipment performance and have to be taken into

account in maintenance management. Objective is to optimize maintenance costs and efforts in

order to ensure competitive electricity price and quality of service. Distribution system operator

has power quality and safety requirements that need to be fulfilled.

Maintenance is one of the tools that can be used for ensuring system reliability, other are for

example increasing system capacity, reinforcing redundancy and using more reliable

components. Usually utilities don’t have many possibilities for these actions and have to

concentrate on improving status of existing equipment.

Objective of maintenance is to find a balance between maintenance and condition of the system.

If too many resources are spent, costs will be too high and if not enough, it will lead into failures,

and outage and restoration costs. Deregulation of electricity market makes companies to work

more efficiently in order to ensure competitive energy and distribution price. Electricity system

has some features that make its maintenance a challenging task. First of all electricity system is

highly important to society and breakdowns could have high indirect costs. Operative lifetime of

equipment used in electrical system is long, often even 50-100 years. Also equipment is highly

dispersed geographically. [3]

3

Maintenance methods can be divided into two categories: preventive and corrective maintenance.

Preventive maintenance is carried out in selected intervals to ensure that equipment works.

Intervals could be decided according to time or condition. Objective is to prevent equipment

failures. Corrective maintenance is carried out after equipment failure. Corrective maintenance is

carried out only in locations where equipment failure doesn’t cause significant problems. [2]

Methods that are used in preventive maintenance can be divided into three categories: time-based

maintenance, condition-based maintenance and reliability-centered maintenance. These are

shortly presented here.

Time-based maintenance (TBM)

In TBM maintenance is carried out by predefined time intervals. This approach has been

especially popular in past because it’s considered to be reliable. Problem of this approach is “too

late or too early” maintenance which leads into inefficiencies. [2]

Condition-based maintenance (CBM)

Condition-based maintenance optimizes intervals when maintenance is done. This maintenance

method is based on the actual condition of equipment. The most critical components of

distribution system could be monitored on-line and real time. Monitoring of other components

would be too expensive. Collected data about equipment’s condition has to be processed so that

decisions of the maintenance can be made. This method gives up to date information and greater

confidence about the condition of equipment. Thus maintenance can be planned more carefully

than in time-based maintenance. If maintenance intervals defined according to condition are

longer than in time-based maintenance, then savings can be achieved. If maintenance intervals

this way are shorter, equipment failures are likely prevented. [2]

Reliability-centered maintenance (RCM)

Reliability-centered maintenance differs from the other two approaches because it distinguishes

difference between failure causes and failure effects analysis. RCM consists of reliability

assessment, life expectancy and risk calculations. In order to make RCM work, expertise is

needed for inspections, failure analysis and decision-making. [2]

MAINTENANCE OPTIMIZATION TECHNIQUES

Literature shows only few applications of maintenance optimization techniques for distribution

systems. It’s more common to have developed optimization model for maintenance of individual

component or substation. Optimization models are usually made for important and expensive

components, like generators or transformers.

Basis of maintenance optimization is defining an objective. It could be maximizing reliability

under given constraints or minimizing costs under given constraints or minimizing total costs of

4

interruptions and maintenance. Other factors that have to be taken into account in maintenance

optimization are for example optimal maintenance and inspection intervals, allocation and

number of spare parts, manpower and redundancy. Besides a clear objective, data from

components and their relationships is needed. For system analysis also a model of the network is

required. Lastly some optimization method or approach is needed in order to reach the objective.

[5]

In this chapter few methods are explained in more detail. There are variety of other approaches

for maintenance optimization, such as Total Test Time and Weibull methodologies, Petri nets,

multi state systems and Life Cycle Costs, but these are mainly for designed for component

maintenance optimization [5].

Variety reduction model

What managers of distribution companies want from tools or methods that assist on asset

management decisions are that tool presents results also in monetary terms, model is able to cope

with uncertainty, decision is based on actual condition of the system, result shows times when

renewals should be carried out, tool should be easy and cheap to use and model should also be

able to describe long-term condition of the system. To deal with this problem a variety reduction

model is presented in [3].

Target of this model is to provide support for decision making in maintenance and renewal

strategies. Model contains four steps: condition based index, dynamic lifetime analysis,

economical analysis and maintenance analysis.

The method starts with condition based index (CBI), where condition of the system and

equipment is defined. Method uses weighted sum of measured values as inputs. These can

temperatures, vibrations, hours spent on maintenance etc. These values are transformed into

linear function and condition is presented as a value between 0 and 100. If system under study

consists of more components, like distribution system does, input values of different components

are weighted so that sum of weight factors is 1. This means that each component gets weight

factor between 0 and 1.

Dynamic lifetime analysis (DLA) needs as input residual lifetime, economic consequence of

equipment failure and costs of planned maintenance. In this way method tries to estimate risks

involved. Residual lifetime is difficult to estimate because of the stochastic nature of equipment

failures, but statistical methods or various methods dealing with distribution can be used. Also

expert opinion can be utilized. By comparing costs of planned maintenance with risk and

consequences of equipment failure financial risk is described.

Economical analysis gives understanding of dependencies between costs and maintenance.

Results would be total cost for maintenance, renewals and residual value. Costs can be calculated

by determining point of renewal, either based on condition or risk If analysis shows too much

resources spent on maintenance, renewals are postponed. It has to be considered whether to spend

more money on maintenance or accept higher risk. Method would give better understanding

maintenance in future and when renewals should be carried out when regarding risk and

condition and what are the financial consequences.

5

Maintenance analysis formulates maintenance strategy based on previous three steps. It shows

how different maintenance strategies affect costs, financial risk and condition of the system. For

the analysis time span and extent of analysis should be decided. As a result of analysis

maintenance time can be decided and condition of network calculated. Different strategies could

be compared.

Method should be used in iterative way because maintenance, costs, risks and reliability affect

each other. Workflow is presented in figure 1.

Figure 1. Work flow of the model [3]

Advantage of this method is that it helps manager to make maintenance decision without taking

too much information into account. In decision making situations not all technical or financial

information can be considered so carefully. For example condition monitoring gives detailed

information about condition of one component only. But system perspective has to be considered,

for example how increased maintenance for one component affect reliability of entire system.

Proposed methodology requires relatively few inputs, on the other hand it requires expert

opinions. Of course when entire distribution system is considered amount of data is significant.

WASRI

Maintenance optimization can be done based on parameters or key figures. One of the goals of

the optimization could be minimizing the weighted average system reliability index (WASRI)

subject to cost constraints as in [4]. This approach is based on component or system reliability

and method ranks maintenance tasks based on their impact on system reliability, it is designed to

find most cost-effective maintenance task.

WASRI is defined as )()()( 321 EMAIFISAIDISAIFIWASRI ωωω ++= (1)

where

CBI

DLA

Financial

analysis

Maintenance

analysis

Basic data for

decision making

6

SAIFI system average interruption frequency index;

SAIDI system average interruption duration index;

MAIFI momentary event average interruption frequency;

ω weight assigned to different indices.

Cost-efficiency of maintenance action E can be defined as a change of WASRI divided by the

cost associated with the maintenance task C. SAIFI, SAIDI and MAIFI indexes are sum of

contributions of each component’s corresponding indexes.

Contributions of individual components can be written as

N

SSAIFI i

i

C

i ⋅= λ (2)

N

d

N

DSAIDI

ij

ii

i

C

i

∑⋅=⋅= λλ (3)

N

TMAIFI i

i

C

iE ⋅= λ (4)

where

iλ failure rate of component i

iS number of customers experiencing sustained interruptions due to failure of component i

iD sustained interruption durations for all customers due to failure of component i

ijd sustained interruption duration for customer j due to a failure of component i, j=1,2,…, iS

iT number of customers experiencing temporary interruption event due to failure of component i

N total number of customers

Model considers only first-order contingencies, meaning that only one component fails at a time

and assumes that maintenance action changes failure rate λ, but not for example repair or

switching times. Factors iS , iD and iT remain thus constant before and after maintenance.

Approach assumes that change of failure rate after maintenance is known.

Maintenance reduces failure rate by ∆ iλ and thus

N

SSAIFI i

i

C

i ⋅∆=∆ )( λ (5)

N

DSAIDI i

i

C

i ⋅∆=∆ )( λ (6)

N

TMAIFI i

i

C

iE ⋅∆=∆ )( λ (7)

Changes in these indices are directly related to change of failure rate of associated component.

From previous equations (1, 5-7) cost-effectiveness E of maintenance action can be written

)(

)( 321

CN

TDS

C

WASRIE

iji

i⋅

⋅+⋅+⋅⋅∆=

∆=

ωωωλ (8)

7

Ranking procedure of this approach is as follows:

1. Evaluate condition of each component and cost of maintenance

2. List all possible maintenance tasks

3. Run a basic reliability assessment to get iS , iD and iT for each component

4. Rank the cost-effectiveness of each maintenance task

5. For maintenance tasks operating on the same component, select one with highest ranking

and eliminate other tasks from list

6. Select maintenance tasks from the top of the ranking list until cost limit is reached or

reliability target is reached.

Approach was applied into testing utility system whit 4000 components and five substations.

Maintenance was recommended to components in substation. With given budget limits and

maintenance actions, approach reduced WASRI by 28 %. As a comparison another approach was

tested. There maintenance was based on component-level cost-efficiency, not system-level

reliability. This CBM-based approach reduced WASRI by 16 %.

Reliability importance indices

Another approach presented in [5] is based on measuring interruption costs for network

performance. Component’s reliability and costs from failure are presented with following three

indices.

Interruption cost index i

SH

i

CI

λ∂

∂= [SEK/f] (9)

where SC is [SEK/year] total year interruption cost and iλ [f/year] component i’s failure rate.

With this index the most critical components in system can be identified, because it studies

interruption costs in relation to component reliability.

Maintenance potential i

H

i

MP

i II λ= [SEK/yr] (10)

where HI is interruption cost index [SEK/f] defined in equation (9) and iλ [f/year] component i’s

failure rate. Maintenance potential express total expected yearly cost reduction if no failures

would occur or said in other way, what are the expected costs that studied component’s fault is

going to cause.

Simulation based index T

KI iM

i = [SEK/yr] (11)

where iK is total accumulated interruption cost over the simulation time T for component i.

Customer interruption cost iK is simulated so that all the costs incurred by the component’s fault

are taken into account. This index shows components that are likely to cause most interruption

costs and maybe what components should be prioritized for maintenance actions.

Even though these three indices may look similar, they are different. HI = expected costs if studied component fails MPI = total expected yearly cost reduction that would occur in the case of a perfect component

8

MI = total expected yearly interruption cost caused by the component (finally causing)

Hilber [5] has used these indices in two actual cases studies, other being urban and other rural

system in Sweden. Case studies showed that these indices were possible to use and gave results,

which could be further analyzed for maintenance actions and economical effect. From results of

these case studies it could be noted that maintenance potential and simulation based indices give

for many components approximately the same value even though they are calculated differently.

Results of the case studies show the most important components of the distribution system. Issues

that effect component’s importance for system are e.g. how far, what kind and how important

loads are behind, how long are repair times, what are fault isolation possibilities and how long

does it take to isolate fault, is there redundancy etc.

Interruption cost index HI and maintenance potential MPI can be combined and figure 2 can be

constructed. These components that are important for system in reliability and in maintenance

potential can be identified.

Figure 2. Example of components’ importance index and maintenance potential in rural case,

radial system [5]

Profitable actions can be determined by

j

H

iijij qIP −⋅∆−= )( λ (12)

where

ijP profit for action j on component i

HI importance index for actual component

9

ijλ∆ change of failure rate

q cost for action j

After every maintenance decision reliability calculations would have to be done again to gain

accuracy because changing status of one equipment changes the state of the system.

As an evaluation of the method, indices demonstrated above were possible to apply to existing

networks. These indices enable evaluation of components from system perspective by assigning

monetary value for interruptions and thus give a starting point for maintenance optimization. For

larger system this method requires lot of data and calculation.

Linear programming

This method described in [6] optimizes maintenance resources for reliability. It is done by

minimizing System Average Interruption Frequency Index (SAIFI). First framework of the work

assumes constant failure rates and second when no accurate information about failure rates and

impact to the reliability is available. Linear programming and approximate reasoning using fuzzy

sets solve this dilemma. Usually there isn’t enough accurate information about components’

failure rates.

Problem is defined by describing SAIFI by number of components, failure rates, levels of

maintenance and maintenance costs. Limiting factors for the optimization are maintenance

resources, maintenance levels and crew constraints. Expected failure rates and the impact on

maintenance are modeled by allowing a range of values for each statistic (fuzzy number).

Statistical distributions aren’t used because suitable distributions aren’t available and

computation would be more complex. Optimization model is determined by arithmetic operations

on fuzzy numbers. Failure rates and failure rate multipliers for maintenance levels are given as a

range of expected values expressed by fuzzy sets.

Method was introduced by two examples of same radial distribution system. First example was

with complete information and the other one with incomplete information. System was the same

in both cases. In case of incomplete data failure rates, fixing times for different types of failures

(fixed, tree or recloser failure) and extent of the maintenance (extensive, minimal, no

maintenance) were given as trapezoidal and triangular fuzzy numbers instead of fixed values as

in first case. Maintenance actions were considered only for reclosers and sections (tree trimming).

With incomplete information maintenance optimization was calculated based on expected values

and based on fuzzy numbers. Expected value -solution recommends to almost all equipment

minimal maintenance, fuzzy number calculation give almost the same result. Example with

complete information (and larger maintenance budget) recommends extensive maintenance for

almost all reclosers and some sections.

Besides it was studied how additional information about failure rates, in this case visual

inspection of tree growth, would affect results. This would reduce the range of variation in outage

statistics. More accurate field information lead to lower maintenance costs with about same level

of SAIFI, so in decision making it was worthwhile.

10

CONCLUSIONS

Maintenance optimization in distribution system is a new subject in research and hasn’t been

much studied even though maintenance is important and money consuming part of electricity

distribution business. This paper pointed out some aspects on studies in this area.

Distribution system consists of many components. Optimizing maintenance for individual

component doesn’t result optimizing maintenance for system. Idea of maintenance optimization

in distribution systems is to evaluate effect of the component’s maintenance to system

performance.

However for effective maintenance optimization data about individual equipment is needed.

Providing this data might be problematic because failure mechanisms aren’t fully known and

long-term data about failures doesn’t exist in all cases. For larger system collecting this

information is demanding task, if it doesn’t exist already before. That’s why all aspects cannot be

taken into account and models have to be simplified. For maintenance optimization software

program is needed in order to transfer models into practice.

Methods introduced in this paper may seem in light of equations simple, but supplying data for

them is more complex. For example defining failure rates for equipment would need lot of back

ground information (models for deterioration, historic data etc.), which isn’t in scope of this

paper. Considering every component and its impact on reliability in system makes maintenance

optimization difficult and time-consuming task.

The four models presented in this paper give a starting point for optimization work. Variety

reduction model mainly presented comprehensive way of thinking without considering

mathematical implementation. The other three were examples of different approaches for

considering maintenance optimization as a mathematical problem.

Main point of maintenance optimization is to find an objective of what needs to be optimized. In

these models it has been minimizing system reliability index, minimizing interruption costs and

minimizing interruption frequency index.

REFERENCES

1. Dekker, Rommert. Applications of maintenance optimization models: a review and

analysis. Reliability Engineering and System Safety, 1996. Vol 51, p. 229-240.

2. Polimac, Vukan & Polimac, Jelica. Assesment of present maintenance practices and

future trends. Transmission and Distribution Conference and Exposition, 2001 IEEE/PES

Volume 2, 28 Oct.-2 Nov. 2001 p.891 - 894

3. Strömberg, M. & Björkqvist, O. Variety reduction model for maintenance and renewal

strategies. Paper in Nordic Distribution and Asset Management Conference 2004,

24.8.2004

11

4. Li, Fangxing & Brown, Richard E. A cost-effective approach of prioritizing distribution

maintenance based on system reliability. IEEE Transaction on power delivery, 2004. Vol

19, No 1.

5. Hilber, Patrik. Component reliability importance indices for maintenance optimization of

electrical networks. Licentiate thesis, Royal Institute of Technology, Stockholm, Swede

2005.

6. Sittithumwat, A., Soudi, F. & Tomsovic, K. Optimal allocation of distribution

maintenance resources with limited information. Electric Power Systems Research 68,

2004, p.208-220.

PRIORITIZATION OF MAINTENANCE METHODS (RCM, CBM, TBM, CM)

Sanna Uski [email protected]

ABBREVIATIONS RCM – Reliability Centered Maintenance CBM – Condition Based Maintenance TBM – Time Based Maintenance CM – Corrective Maintenance HBS – Hardware Breakdown Structure MSI – Maintenance Significant MTBF – Mean Time Between Failures MTTR – Mean Time To Repair OSI – Operational Significant INTRODUCTION Traditionally electric utility maintenance strategies have been based on fixed interval maintenance, and even nowadays it is quite common maintenance policy used in the world. The grounds for emerge of new maintenance methods has been to find an optimum between the costs of maintenance, and the ability of maintaining sufficient reliability of the system. The fixed interval maintenance policy, i.e. time-based maintenance (TBM) policy, may often be inefficient in the sense of the costs as well as in lengthening the lifetime of components. Due to these reasons, an approach towards flexible maintenance policies has been taken in order to be able to take advantage of the information obtained through condition monitoring done, and carry out the maintenance based on the needs and priorities. This is called predictive maintenance, of which reliability-centered maintenance (RCM) is a part. The RCM is not really a single strict maintenance method, but it allows comparison of different maintenance methods, of which the most cost-effective can be chosen without compromising the reliability. Predictive maintenance can enable better outage scheduling, flexibility of operation, better fuel use, more efficient spare part management, and improve efficiency. [1] The most primitive maintenance strategy is Corrective Maintenance (CM), which is based on restoring the operation by fixing or replacing the component in case of failure, and does not include any additional maintenance inspections. TBM is based on maintenance tasks performed after certain period of time, either executing maintenance actions regardless of, or based on, the age of the component. Condition based management (CBM) takes into consideration the condition of the component based on which, the maintenance tasks depend on. Figure 1 shows an overview diagram of maintenance methods.

1

Asset Management

Manufacturer’sSpecifications

Purchasing

Replacement

DisposalMaintenance

EmpiricalApproaches

RCM

Analysis ofNeeds andPriorities

ConditionMonitoring

Age,Bulk

ScheduledMaintenance

PredictiveMaintenance

MathematicalModels

Figure 1 Over view of maintenance approaches. [1] Use of the Reliability Centered Maintenance (RCM) method is increasing nowadays. The approach in RCM is commonly empirical, and the RCM relying maintenance policies used in recent years, are rarely employing mathematical models. Mathematical models are needed in maintenance schemes in order to be able to represent the effects of maintenance in reliability. Mathematical models can be deterministic or probabilistic. [1] Reliability, and therefore condition management, has a significant economical role in the industry. 50 % average is a rough estimation of the life-cycle costs of components that are allocated to maintenance activities. [2] There is a risk of possible outages due to failures, and on the other hand the costs of investments on (over)improved reliability in form of system upgrading and maintenance. In Figure the cost-investment relationship is shown schematically.

2

Interruption costs

Maintenance costs

Total costs

Cos

ts

Investments on reliability Figure 2 Relation between costs and investments on reliability. [3] In optimizing maintenance strategies, it is essential to know which system parts are maintenance critical. After this is known, one can define appropriate maintenance strategies for each subsystem and component. The most critical parts need to be working with higher reliability than the less critical and non-critical parts, and the maintenance strategies must be selected accordingly. The maintenance should always be optimized with respect to availability and reliability of the system. [4] FAILURE TYPES There are two failure categories; random failure, which can occur at any instant of time, and failure caused by deterioration, meaning the older the component in respect of component lifetime, the closer to failure it is. As the random failure can occur at any time, preventive maintenance does not make any improvement to that. On the other hand, maintenance actions due to deterioration, can prolong significantly the lifetime of the component. There are several different inspection-replacement strategies. Simplest one is replacement in case of failure, in which case no inspections are performed, and component is replaced only in case of a failure. This strategy is called corrective management (CM). Age replacement strategy adds a bit of reliability (e.g. in considering the failure due to aging) to the previous, when the component is replaced after a certain amount of time in case it has not failed before this. [5] It is essential to know the impact of failure of all the components in the system, to the total system functionality. The so-called maintenance significant (MSI) logic, as well as operational significant (OSI) logic, can help to narrow down the components to which corrective maintenance (CM) should be applied to, and on the other hand the components, for which preventive maintenance should be done. MSI and OSI logic are based on yes/no answers to questions on safety, production losses, cost of repair, spare part availability and need to capture information/documentation. This phase has a significant influence on maintenance efforts on the whole system, by e.g. determining the spare part reserve, maintenance work on preventing and predicting failures, and work to be put on documentation. RCM is applied only to all the MSI items (to which all the OSI items belong to as well). First, an item is selected, and hardware breakdown structure (HBS – the database into which all information will be stored) is created to the item. Then the failure mode will be investigated, and finally RCM will be applied. The result of this, will be the possible actions to be done in order to prevent failure, and the causes of failure. RMC method also reveals if condition based information is needed (instead of scheduled maintenance tasks) and if it will

3

be cost effective. Generally complex systems require more condition based information for failure prediction and prevention. If needed, condition monitoring strategy supports the top level system maintenance strategy. [4]

Assessment of deterioration Deterioration can be determined based on time, or inspection of the physical state. In order to be able to determine the physical state, periodic inspections are needed. Despite of this, the latter deterioration assessment method is more commonly used of the two methods. A common way of modeling the deterioration, is with the Markov models, with which the optimal inspection-replacement policy, enabling long-term cost-effectiveness, is defined. [1] In determining the optimal policy, there are several features that are considered, e.g. limitations of technology, equipment costs and features of implementation. [5] Besides periodic inspections, which occur periodically after equal amounts of time, there is a sequential inspection strategy, in which the inspections are spaced by sequences of time not necessarily equal to each other. Continuous inspection (condition monitoring) can also be used for determining the condition of the system. In periodic, sequential and continuous inspection strategies, the state of the system in determined, and based on predefined criteria, the system is either replaced or allowed to continue functioning (until the following inspection). In cases where periodic inspections do not help in prediction of wear-out trends accurately enough, condition monitoring is preferred, provided that the costs remain reasonable. Other arguments for using condition monitoring, may be for example the need of online inspections, or that the failure of the device is notably critical. Inspection methods may consist of e.g. visual inspection, optical inspection, neuron analysis, radiography, eddy current testing, ultrasonic testing, vibration analysis, lubricant analysis, temperature analysis, magnetic flux leakage analysis and acoustic emission monitoring [1] The mathematical models of deterioration can be either deterministic or probabilistic. Deterministic models can be strait-forward and more simple than the probabilistic ones, but may sometimes lead to false conclusions, such as resulting an infinite lifetime of a component before failure due to fixed values and assumptions that the maintenance restores the component condition to the state it had in the previous inspection or, even better state. Probabilistic methods are more realistic than the deterministic ones, but are more complex on their structure. OPERATIONAL RELIABILITY AND MAINTENANCE COSTS Optimal values of cost-rates of some different maintenance policies compared with each other are gf ≥ ga ≥ gp ≥ gs ≥ gc, where g is the cost-rate and subscripts f, a, p, s and c refer to failure replacement, age replacement, periodic inspection (inspections spaced by fixed time), sequential inspection (inspection times predetermined, but not necessarily spaced equal) and continuous inspection,

4

respectively. [5] The failure replacement is a CM strategy, age, sequential and periodic inspections belong to TBM and continuous inspection in CBM, and all may be used in RCM. Relation between reliability and maintenance effort in presented in figure 3 in three different scenarios. X represents the normal reliability of the equipment and the number of maintenance actions equal to the number of failures. Y represents the maintenance effort due to unnecessary maintenance actions caused by false alarms. Z represents the maintenance effort due to human errors and vandalism, or non-standard maintenance actions. The number of failures in each case are as follow:

X: # maintenance actions = # failures Y: # maintenance actions = # failures + # false alarms Z: # maintenance actions = # failures + # false alarms + # non-standard

maintenance actions

X

Y

Z

Reliability

Mai

nten

ance

Eff

ort

Figure 3 Relation between reliability and maintenance effort. [4] In the figure 3 can be noticed that maintenance effort decreases as reliability increases. Also can be seen that at low reliability, the maintenance effort due to Y and Z remains almost constant or even decreases slightly. Therefore, as the normal reliability X increases, elimination of Y and Z improves reliability a great deal. However, all components have their built-in reliability defined at design-stage, which can not be surpassed even with the best quality control scheme. Availability of a system is defined based on maintainability (the ability to repair a component in certain time), operability (man-machine interface), supportability (availability of spare parts, human resources etc.) and reliability (probability of adequate performance for a certain time of the component). Availability can be calculated with A = MTBF/(MTBF + MTTR). MTBF, mean time between failures is a function of reliability and operability

5

MTBF = f(R,O),

and MTTR, mean time to repair is a function of maintainability and supportability

MTTF = f(M,S), therefore availability is a function of the all four A = f(R,O,M,S). Reliability can be calculated by R = eτ, where τ = failure rate = 1/MTBF. If availability can be increased, the optimum maintenance effort is achieved, and operating costs of the system have been reduced. [4] MAINTENANCE TASKS For task analysis, when on defining the maintenance tasks to be executed, e.g. questions, “Is system shut-down necessary to when executing maintenance task?” and “Can tasks be grouped to optimise maintenance effort?”, need to be answered. The task analysis will finally define;

• which parameters need to condition monitored • optimised set of maintenance tasks • the optimum level and range of spare parts to be stored • personnel and skills required for executing the maintenance tasks.

Inputs of condition monitoring system are condition data regarding,

• performance • mechanical state • wear • physical state • other information.

In managing and optimizing predictive maintenance activities based on information received through condition monitoring, it is important, that all the correct parameters are being monitored. To be able to achieve optimal level of maintenance, documentation is required, as it important that the maintenance strategy has been agreed upon. CONCLUSION It has been stated that management costs are very significant in system costs. Despite of this, it has not been until recent years when more sophisticated maintenance strategies have been started to be implemented. CM is the most simple maintenance method, and TBM has been the prevailing method in the world. CBM is a bit more advanced method from TBM, and the most advanced method is RCM, which is actually a procedure to identify the optimal maintenance strategy regarding each item of a system. The whole maintenance task can

6

therefore be implemented by using optimally all the three other maintenance methods, CM, TBM and CBM, on different system components. REFERENCES [1] A Report of the IEEE/PES Task Force on Impact of Maintenance Strategy on Reliability of the Reliability, Risk and Probability Applications Subcommittee, J. Endrenyi, S. Aboresheid, R. N. allan, G. J. Anders, S. Asgarpoor, R. Billington, N. Chowdhury, E. N. Dialynas, M. Flipper, R. H. Fletcher, C. Grigg, J. McCalley, S. Meliopoulos, T. C. Mielnik, P. Nitu, N. Rau, N. D. Reppen, L. Salvaderi, A. Schneider, and Ch. Singh, “The Present Status of Maintenance Strategies and the Impact of Maintenance on Reliability”, IEEE Transactions on Power Systems, vol. 16, no. 4, pp. 638-646, Nov. 2001. [2] Thorbjörn Andersson, Inger eriksson, Donald L. Amoroso, “Steering the Maintenance Costs: An Explorations of the Maintenance Construct”, System Sciences, 1992. Proceedings of the Twenty-Fifth Hawaii International Conference on volume iv, 7-10 Jan. 1992. Pp. 348-358 vol. 4. [3] Olli Lehtonen, Master’s Thesis ”Development of reliability management of electricity distribution in industrial plants” (in Finnish), Tampere Universty of Technology, 2001. [4] J.H. Wichers, “Optimising Miantenance Functions by Ensuring Effective Management of your Computerised Maintenance Management System”, IEEE Africon ’96 Conference, pp. 788-794, Sept. 1996, Stellenbosch, RSA. [5] C. Teresa Lam, R.H. Yeh, “Optimal Maintenance-Policies For Deteriorating Systems Under Various Maintenance Strategies”, IEEE Transactions on Reliability, vol. 43, no. 3, pp.423-430, Sept. 1994.

7

1

RCM APPLICATIONS IN UNDERGROUND POWER SYSTEMS

Samuli Honkapuro Lappeenranta University of Technology

[email protected]

INTRODUCTION

The maintenance work of the network equipment can be divided to preventive maintenance (PM) and corrective maintenance (CM). Traditionally, most of the maintenance works of the underground cables have been corrective maintenance, where the cable is repaired after the fault has occurred. However, diagnostic methods for underground cables have developed and thereby there is better potential to implement preventive maintenance for underground cables. However, preventive maintenance could demand considerable amount of the resources, such as manpower and financial resources. Therefore preventive maintenance should be focused on those parts of the network, where it provides highest impact to reliability for lowest costs. For optimising the reliability and the costs, the maintenance method called reliability-centred maintenance (RCM) has been developed. In this seminar paper, RCM process for underground power systems is presented. The fundamental of the RCM and the basic characteristics of the cable network, such as common causes of the failures and diagnostic methods, needed for the creating of the RCM process are presented. Three methods to implement the RCM for underground power systems are presented, first the general framework and then two case examples.

RELIABILITY CENTRED MAINTENANCE (RCM)

Reliability centred maintenance was developed at 1960’s in the aircraft industry. At that time the largest civil aircraft, Boeing 747 (the Jumbo) was created. Because of the complexity of the aircraft, the preventive maintenance was expected to be expensive and the developing of the new maintenance strategy was needed. The aim of the RCM is to achieve cost effectiveness by optimising the maintenance activities in the systematic way. RCM provides a formal framework for handling the complexity of the maintenance process. In technical view, it does not add anything new. (Bertling, 2002)

General RCM framework

RCM framework can be divided to three main steps as shown in figure 1. RCM process begins with system reliability analysis, where the system is defined and components that are critical for system reliability are evaluated. Second step is to define the relation between preventive maintenance and the reliability of the component. This is usually seen as most challenging task of the process. In the third step, the system reliability and cost/benefit analysis puts the understanding of the component behaviour gained in the system perspective. (Bertling 2002)

2

Figure 1. Three main steps in RCM analysis. (Bertling 2002)

RCM process can also be formulated into seven questions, after the system items to be analysed are identified. These questions are: (Bertling 2002)

1. What are the functions and performances required? 2. In what ways can each function fail?

3. What causes each functional failure?

4. What are the effects of each failure?

5. What are the consequences of each failure?

6. How can each failure be prevented?

7. How does one proceed if no preventive activity is possible?

Data requirements

In order to create RCM strategy successfully, comprehensive knowledge about the system and its components is needed. (Bertling 2002) At the system level, following data is required: (Bertling 2002)

• System descriptions and drawings • PM and control programs

• Commitments and requirements for existing maintenance programs

3

At component level, following data is required: (Bertling 2002)

• The list of the components • Components maintenance history

Following data is required for the cost/benefit analysis: (Bertling 2002)

• The costs of reliability (investment costs and outage costs) • The costs of undertaking maintenance (cots of manpower, materials etc.)

• The costs of not undertaking maintenance (outage costs for utility and customer)

In addition to previous, also the knowledge about the relationship between maintenance and reliability is needed. That includes e.g. the failure causes of the components and the effect of the maintenance on these failures and the relationship between failures and lifetimes. Important issue is also that what are the cost factors that are needed to be balanced. (Bertling 2002)

METHOD 1; GENERAL RCM FRAMEWORK FOR UNDERGROUND POWER SYSTEMS

The increasing need for reliability and the decreasing amount of the available land in the urban areas are the main reasons for the growing use of the underground power systems. Although underground cables are more reliable than overhead lines, there are some failures that are typically for cables and that can be partially prevented by the appropriate maintenance of the cable system.

Failures in underground power systems

In the figure 2, there is shown the failure causes of the cable systems. These are based on the survey of the failures in the one substation of the Stockholm city power system. The failures can be divided to short circuit failures, which are caused by insulation failure, and open circuit failures, which are caused by conductor failure. (Bertling 2002)

4

Figure 2. Failures in cables system based on survey. Boxes shown as dashed lines are those that could be considered to be affected by preventive maintenance. (Bertling 2002)

In the figure 2, failure causes shown as dashed lines are those that could be considered to be affected by preventive maintenance. Based on these results, it can be concluded that about 35 % of the failures in the cable system could possible be avoided by appropriate maintenance. The insulation failure of the polymeric-insulated cable is usually caused by the deterioration of the insulation, which can be caused for example by the water tree phenomenon of the cable (Bertling 2002). Water tree phenomenon is quite common for the polymeric-insulated cables that are contacted with water. In water treeing the water intrudes in the polymeric insulation as the shape of a tree. Water trees can be divided to vented trees and bow-tie trees as shown in figure 3.

Figure 3. Water-trees. (Anon 2005)

5

Diagnostics in cable networks

The predictive diagnostic techniques of the underground cables have been developed during last decades and they have become powerful tools to increase the reliability of the cable networks. When cables in poor condition can be detected and repaired or replaced before the fault occur, the amount of the customer interruptions can be decreased. Diagnostic methods of underground cables can be divided to destructive and non-destructive methods. In destructive methods the measured object has to be destroyed. Electrical breakdown testing and water tree testing are two generally used destructive test methods. Electrical breakdown testing is done by using AC, DC or impulse voltage. In water tree testing, thin slices are cut of from the cable and analysed with microscope. Non-destructive methods are based on the detecting of the changes in the insulation of the cable by dielectric diagnostics. These diagnostics are based on the measurement of the current in time and frequency domain. In the figure 4 there is illustrated the water treeing process and the non-destructive diagnostics of the water tree. (Bertling 2002)

Figure 4. Water treeing process and the diagnostics of the water trees. (Bertling 2002)

Preventive maintenance of the underground cables

Traditionally underground cables have been replaced after the fault has occurred and the preventive maintenance of the cables has been minimal. With modern diagnostic methods, the condition of the cable can be detected and cable can be replaced or repaired before the fault occurs.

6

The cable deteriorated due to water tree can be repaired with rehabilitation method. In rehabilitation, the silicon-based liquid is injected between the wires of the conductor. When rehabilitation liquid comes into contact with water, chemical polymerising takes place and it consumes the dampness. (Bertling 2002) According to study of Birkner (Birkner 2004), the costs of rehabilitation method are roughly 50 % of the costs of the exchange of the cable. On the other hand, the additional lifetime of the rehabilitated cable is about 20 years, compared to 40 years lifetime expectancy of new cable. (Birkner 2004) Based on these results the rehabilitation and replacement are quite equal method in the economical point of view. However, in some other studies rehabilitation has been proved to be economically and technically beneficial method (Bertling 2002).

RCM framework

In the figure 5, the optimal focus of the RCM for underground distribution systems is presented. At first whole cable system is considered to be replaced or repaired. After that the fundamental maintenance program is created based on historic performance and customer load critically. The predictive diagnostics can then be applied to the areas with most severe needs. The results of the diagnostics will then identify the cables that need to be repaired and cables that need to be replaced. These tasks can then be prioritised by considering the severity of the defects and the number of the customers affected. Project implementation can then be performed within the limits of the human and financial resources. (Reder & Flaten 2000)

Consider entire cable and component systemIdentify failure prone cables and components

Determine dominant failure prone components

Understand critical customer load risk

Perform value added diagnostics

Analyse total repair/replace

Prioritise repair/replace

Implement to resource

limits

Figure 5. The optimal focus of the RCM for underground distribution system. (Reder & Flaten 2000)

7

RCM process for underground distribution systems can also be divided to following six steps: (Reder & Flaten 2000)

1. Establish the scope: The work limits are defined by establishing the initially boundaries. The scope of the maintenance activities could include e.g. certain geographical areas or certain feeders or critical customers.

2. Identify what is not in the scope: The elements that do not affect on the goals of the

program are defined. Items that will not be included could be for example: dig-ins, animal related outages and ground settling.

3. Specify performance goals: The performance level that is needed to meet company

reliability objectives is defined.

4. Identify the problem: The historic problems and their causes are defined.

5. Identify resources available: Resource constraints, such as field resources, computer systems, data gathering tasks and financial limitations, are defined.

6. Create necessary procedures: Necessary procedures for predictive diagnostics and

corresponding repairs or replacements are created.

METHOD 2; CASE EXAMPLE – LECHWERKE AG

Lechwerke AG is a German electricity distribution company, which operates a radial 20 kV grid. Lechwerke AG has introduced RCM process for its underground cable network. The total length of cable network is 3156 km, but only 8,3 % (262 km) of all cables are responsible of 74 % of all outages. These cables were categorised and the condition of the important cables were tested with IRC-analysis (Isothermal Relaxation Current), which is a non-destructive method that allows the classification of the XLPE and PE cables in the aging classes “Perfect”, “Mid Life”, “Old” and “Critical”. These classes correlate to the residual dielectric strength of the cable insulation. (Birkner 2004) In RCM process, underground cables are categorised into required and not required ones depending on whether they are required or not for system operation. Required cables are categorised further to important and not important ones. Evaluation process is shown in figure 6 and it is based on practical experience. (Birkner 2004)

8

Figure 6. The categorisation of the underground cables. (Birkner 2004)

RCM process for cable network is shown in figure 7. After the cables have been categorised as shown in figure 6, the diagnostic and maintenance process can be created. If cable is not required for system operation and it has increased outage rate, it can be decommissioned or dismantled. If cable is required but unimportant, no activities are performed and the risk for outage is accepted. Only cables that are required and important are tested with IRC-analysis. (Birkner 2004)

Figure 7. RCM program for underground cable network. (Birkner 2004)

If the result of the IRC-analysis is “Perfect” or “Mid Life”, the cable is submitted for further testing and is repaired if testing indicates some deficiencies. If the result of the IRC-testing is “Old” or “Critical”, the cable is replaced or repaired. (Birkner 2004) The results of the analysis of the cables with increased outage rate show that 45% (120 km) of the cables are considered as “required” and “important”, these are the cables that are IRC tested. 79 % (95 km) of IRC tested cables have been classified to “Old” or “Critical”. After the analysis

9

it has been possible to concentrate the replacements to these 95 km of cables. Investment costs for that operation are 5,7 million Euros. Another plan was the replacement of all the cables with increased outage rate (262 km of cable), which would have cost 15,8 million Euros. Thereby the savings in the investment costs due to successful RCM process were 10,1 million Euros. Also the number of the outages has been reduced since RCM has been implemented systematically. (Birkner 2004)

METHOD 3; CASE EXAMPLE – NORTHERN STATES POWER COMPANY

Northern States Power Company (NSP) implemented RCM for underground distribution network on feeders in Minneapolis/St. Paul area. Predictive cable testing was performed on 241 NSP’s worst performing feeders; the methodology is shown in upper part of figure 8. These feeders represented one-third of total metro area feeders. Control group, which was not tested, contained 554 feeders. (Reder & Flaten 2000)

Figure 8. Methodology utilised to show benefits of predictive cable testing and repair. (Reder & Flaten 2000)

After repairs were performed on the 40 % of the recommended locations, clear reliability benefits were achieved. The System Average Interruption Frequency Index (SAIFI) of tested feeders and control group of not tested feeders was compared. The results of this comparison are illustrated in the figure 9.

10

Figure 9. The reliability improvement of the tested cables. (Reder & Flaten 2000)

As can be seen from the figure 9, SAIFI of the tested group improved by 40 %. In practice this means that approximately 45 000 customer outages were avoided. The SAIFI of the remaining feeders that were not tested got worse, partly due to hot summer during testing time. (Reder & Flaten 2000)

CONCLUSIONS

Reliability Centred Maintenance has been used in many areas of industrial during several decades. Recently RCM applications for the electricity distribution systems have been created. There are some special challenges in the creating the RCM application for underground power system; condition monitoring and repairing of the cable need special methods. With new diagnostic methods, the improvement in the reliability and cost-effectiveness can both be achieved. It is stated that predictive diagnostics can reveal 2/3 of cables, which are scheduled for replacement, can actually be kept in service reliably in the near future (Reder & Flaten 2000). Developed diagnostic methods have generated the need for new methods to optimise the preventive maintenance process of the underground power system. RCM is an appropriate framework for planning the optimal maintenance program for underground cables. Theoretical studies have been made to create appropriate RCM process for underground power systems. Case studies of RCM applications have shown out that increase in reliability and decrease in costs could both be achieved by applying RCM process in the underground power system.

11

REFERENCES

Anon 2005 http://www.eupen.com/cable/power/medium_voltage/power0212.html Bertling 2002 Bertling, Lina. Reliability Centred Maintenance for Electric Power

Distribution Systems. Doctoral Dissertation. Royal Institute of Technology (KTH). 2002

Birkner 2004 Birkner, Peter. Field Experience With a Condition-Based

Maintenance Program of 20-kV XLPE Distribution System Using IRC-Analysis. IEEE Transactions on Power Delivery, Vol. 19, No. 1., January 2004

Reder & Flaten 2000 Reder, Wanda & Flaten, Dave. Reliability Centred Maintenance for

Distribution Underground Systems. Power Engineering Society Summer Meeting 2000. IEEE

1

RCM applications for switchgear and high voltage breakers

Richard THOMAS Chalmers Tekniska Högskolan, Göteborg & ABB Power Technologies AB1, Ludvika, SWEDEN

[email protected]

1. AUTHOR NOTE – REFERENCES, DEFINITIONS & ABBREVIATIONS

This report refers to specific terms for which formal or normative definitions exist (IEC / ANSI / IEEE / CIGRÉ) and these have been used wherever possible. They have been collated into a table (including abbreviations and/or symbols) contained in Section 9 of the report and are referenced by a superscripted 9.# reference corresponding to the table numbering where terms are introduced. Definitions might be repeated in the body text where considered relevant to the flow of the discussion. Other references to cited texts are in referenced in square brackets [#], according to numbering in the Bibliography in Section 10 at the end of the report. 2. INTRODUCTION

Power system operation involves maintaining a balance between supply and demand, usually with a demand driven focus. As large scale storage of electrical energy has practical limitations, the power system operator needs to maintain a suitably robust and flexible system in order to respond to demand fluctuations instanteously, allowing for the probability of faults and disturbances on the system that can reduce the supply capability. There are many sources of possible disturbance to a power system that can result in the loss of a component. These can be catergorized according to their cause and frequency as indicated in Table R1 below. Disturbance Category Primary Causes Occurrence Frequency

Externally Caused Faults Lightning, falling trees, weather extremes, human error etc

Sporadic or “random”. Weather related faults can show seasonal tendancies.

Equipment Failure Ageing, operational wear, inherent defects, incorrect settings etc

Ideally “rare”. Varies according to equipment, insulation, wear and failure mechanism types. Studies of failure statistics can reveal specific failure trends for specific equipment.

Equipment Maintenance Prevention and / or rectification of defects in equipment due to operational wear or age.

Preventative maintenance is planned. Planning criteria can vary, usually either on a time, operations history or condition basis.

Table R1: Categorization of Major Power System Operational Disturbances “External” faults, refers to faults caused by events “external” to the power system itself i.e. not caused by the power systems own equipment or operation. Equipment faults are those caused by the failure of equipment within the power system. Equipment maintenance refers to the need to

1 Please note that the views expressed in this report do not necessarily reflect the official policies of ABB.

2

take equipment out of service for safety and practical reasons in order to conduct maintenance. Equipment failures might also be considered “random”, but for well understood failure mechanisms, the expected frequency and probability of such failures can to some extent be quantified, including allowance for changes to the risk of failure due to specific equipment age or condition. Maintenance and diagnosis of equipment on the other hand can be planned and therefore has additional possibilities for optimization. The focus of this report is on the application of Reliability Centred Maintenance (RCM) 9.13 on high voltage (HV) breakers. This report is structured in the following main parts:

• General principles of RCM as applied to HV breakers in power systems • Functional description of an HV breaker • Failure modes of HV breakers • Application of RCM to HV breakers – including reported utilty experience • Impact of RCM on breaker standards and designs

High voltage breakers are one of the most critical primary elements of a power system. They are the main devices relied upon for interruption of load and fault currents in a controlled manner. Failure of a breaker to fulfill its primary functions will normally have a direct impact on the relaibility9.11,9.12 and availability9.2 of the power system. Even if such a failure does not result in an immediate loss of supply to a consumer, it will alter the operational state of the power system so as to increase the risk due of result in loss of supply to any further failures in the network. The relationships between one of a breaker’s primary functions and maintenance, to the above listed power system disturbance catergories is illustrated in Figure R1 below:

Figure R1: Relationships between Breaker Function, Maintenance

and Power System Disturbances

Equipment maintenance

Equipment faults

External faults

Disturbance category

Equipment maintenance

Equipment faults

External faults

Disturbance category

Corrective

Maintenance

Preventative

Maintenance

Maintenance

optimization through

Reliability

Centred

Maintenance (RCM)

Corrective

Maintenance

Preventative

Maintenance

Maintenance

optimization through

Reliability

Centred

Maintenance (RCM)

Primary breaker

function:

Interrupt faults

Primary breaker

function:

Interrupt faults

Focus areas of this report

3

The primary functions of a breaker will be presented in more detail later, though in Figure R1, interruption of faults is indicated as the most well recognized of these functions. Figure R1 indicates two main categories of maintenance activity: preventative and corrective. There are important differences between these activities. Preventative maintenance9.10 is aimed at restoring the condition of equipment so as to achieve a minimum acceptable performance and reliability. Such maintenance is normally planned. Corrective maintenance9.17 is undertaken as repair or replacement of failed equipment and is typically unplanned. Both activities are linked to equipment reliability. Preventative maintenance seeks to maintain or improve reliability. Corrective maintenance occurs as a consequence of imperfect reliability; meaning that the risk of failures can only be minimized but never eradicated. Too little (effective) preventative maintenance can lead to an increase in corrective maintenance. However, there will also be a level of diminishing returns in attempting too much preventative maintenance. First, the activity itself normally requires removing equipment from service, thus making it a disturbance to overal power system availability, as indicated above. Second, there is a realistic limit to the level of reliability that can be achieved through maintenance; it cannot eliminate all failure risks – especially so called “hidden failures”. This can be expressed Pareto terms; e.g. 80% of failures can be attributed to 20% of components; alternatively 80% of failures can be minimized by 20% effort. Minimizing the remaining 20% will required a drammatically increasing effort. RCM is a process to optimize the maintenance activities. The mix of objectives and constraints governing the optimization process can be complex, but in general RCM aims to achieve an acceptable level of reliability for a minimum of effort. 3. RELIABILITY CENTRED MAINTENANCE APPLIED TO POWER SYSTEMS AND

HV BREAKERS – GENERAL PRINCIPLES

RCM was first developed in the 1960’s within the aircraft industry in response to the advent of larger and more complex aircraft designs [3 ], for which traditional maintenance methods were considered to be impractical for. In the power system context RCM is defined in IEC 60300-11-3 [3 ]as follows: Reliability Centred Maintenance9.13: Systematic approach for identifying effective and efficient

preventative maintenance tasks for items in accordance with a set of specific procedures and for establishing intervals between maintenance tasks

In other words RCM can be considered as the application of a structured optimization process to existing preventative maintenance schemes, aimed at meeting the availability requirements of the power system for the lowest total maintenance cost within a given time frame. Optimization is generally understood to be seeking to maximize output while minimizing input. In this respect, optimization of power system maintenance can be considered as seeking to maximize system availability while minimizing maintenance effort (or cost). It is important to recognize that both system availability and maintenance effort can be measured in monetary terms. Availability can be measured in monetary terms such as costs for loss of supply or

4

providing alternative supply, in addition to penalties that might be imposed either by a network regulator or by customer supply contract due to loss of supply. Maintenance measurement in monetary terms is made most typically in terms of the budgeted costs of personnel, parts and equipment required to carry out the maintenance, but may also include penalty costs from a network regulator associated with taking an item of plant out-of-service. RCM also adds in reliability considerations to the optimization task i.e. achieving a specified reliability through optimum maintenance effort. In the power system context reliability can be considered not only in repect of the probability of a failure, but the probability of a failure impacting on either the direct instanteous availability or supply capacity of the system, but also impact on the redundancy level of the system. Factors in RCM RCM Goals Failure rates, λ Minimize Mean time between failures, MTBF9.7 = 1 / λ Maximize Mean time to maintain / repair, MTTM/R9.8 Minimize Cost of maintenance Minimize Cost of unavailability Minimize One must distinguish between system reliability and component reliability. In applying RCM to HV breakers it will be come clear that the breaker itself can be considered as a “system”, due to its complexity and different functions that involve the correct interaction of many parts. RCM at

• system level = Select which breakers to maintain • component level = Determine maintenance tasks providing the maximum return on

effort Figure R2 below illustrates the above considerations in the context of HV breakers on a (fictious) power system. Several important points are made in Figure R2:

• Different types of circuit breakers – according to interrupter types: Air-blast “A”, Oil “O” and SF6 “S” are present on this example system. This reflects the use of different breaker technologies and designs available at the time each part of the system has been built. These different breaker types will have different failure modes, failure rates and maintenance requirements.

• Breakers have different impacts on overall system availability depending on their location. Breakers near the system generators have a greater overall impact than breakers near the radial ends of the system (in this example).

It is important to recognise the relationships between overall power system HV circuit design and associated breaker reliability / availability on the overall system reliability and availability. Breaker availability and system availability are seperate quantities. Normally transmission systems are designed with the “N-1” redundancy criteria in mind i.e. the system’s performance should not be placed in immediate jeopardy by the loss of a single line (or component)[7 ]. On more critical power circuits an “N-2” or higher redundancy might be applied.

5

Figure R2: Illustration of Breaker Maintenance and System Risk Mitigation Factors

There are two important aspects of breaker availability to be considered with respect to overall power system level “N-1” redundancy:

1. Failure of one breaker can shift the system from the “N” secure state to “N-1” secure state. Failure of another breaker may then place the system in a potentially insecure or unstable state.

2. Maintenance that requires removal of a breaker from service can affect the “N-1” system

status. The longer such maintenance takes, the longer the system can be required to operate in a “N” secure state and thus is operating at higher risk.

Considering the above two aspects of the impact of breaker availability on system performance, RCM on breakers will be aimed not only at reducing direct maintenance costs, but also reducing

MAIN

TENANCE

COSTS

MTBF (MAXIMIZE)

MTTM (MINIMIZE)

PBM (MAXIMIZE)

LEGEND:

PBI: PERIOD BETWEEN INSPECTIONS

MTTI: MEAN TIME TO INSPECT

PBM: PERIOD BETWEEN MAINTENANCE

MTTM(/R): MEAN TIME TO MAINTAIN(/REPAIR)

MTBF: MEAN TIME BETWEEN FAILURE

TIME

TIME

A O S

O O O S S S

SS

S SSOOO

SOOO

SSSOOO

A A

A A

A A

A A

A

O

S

PNEUMATIC; AIR-BLAST

HYDRAULIC; OIL

SPRING, SF6

BREAKER TYPES

PBI (MAXIMIZE)

MTTI (MINIMIZE)

FAILURE

COSTS

CRITICAL

SYSTEM

IMPACT

HIGH

SYSTEM

IMPACT

MODERATE

SYSTEM

IMPACT

INSPECTION COST

(MINIMIZE)

MAINTENANCE COST

(MINIMIZE)

FAILURE COST

(MINIMIZE)

MAINTENANCE

INVESTMENT

FAILURE

RISK

BUDGETFAILURE

COSTS

MAIN

TENANCE

COSTS

MTBF (MAXIMIZE)

MTTM (MINIMIZE)

PBM (MAXIMIZE)

LEGEND:

PBI: PERIOD BETWEEN INSPECTIONS

MTTI: MEAN TIME TO INSPECT

PBM: PERIOD BETWEEN MAINTENANCE

MTTM(/R): MEAN TIME TO MAINTAIN(/REPAIR)

MTBF: MEAN TIME BETWEEN FAILURE

TIME

TIME

A O S

O O O S S S

SS

S SSOOO

SOOO

SSSOOO

A A

A A

A A

A A

A O S

O O O S S S

SS

S SSOOO

SOOO

SSSOOO

A A

A A

A A

A A

A

O

S


HYDRAULIC; OIL

SPRING, SF6

BREAKER TYPES

A

O

S


HYDRAULIC; OIL

SPRING, SF6

BREAKER TYPES

PBI (MAXIMIZE)

MTTI (MINIMIZE)

FAILURE

COSTS

CRITICAL

SYSTEM

IMPACT

HIGH

SYSTEM

IMPACT

MODERATE

SYSTEM

IMPACT

INSPECTION COST

(MINIMIZE)

MAINTENANCE COST

(MINIMIZE)

FAILURE COST

(MINIMIZE)

MAINTENANCE

INVESTMENT

FAILURE

RISK

BUDGETFAILURE

COSTS

6

total maintenance time (i.e. “breaker out-of-service” time) while aiming at an acceptable (if not as low as possible) breaker failure probability. ANSI/IEEE standard 1366™-2003 [2 ] defines quantities to be considered in setting the availability requirements of a power system. Figure A.3 in this standard reports that the following four indicies were the most commonly used according to a survey conducted between 1995 and 1997:

• System Average Interruption Frequency Index, SAIFI 9.15 • System Average Interruption Duration Index, SAIDI 9.14 • Customer Average Interruption Frequency Index, CAIFI 9.3 • Average System Avaliability Index, ASAI 9.1

These indicies provide various ways to quantify the level of interruption experienced by electricity consumers and thus provide reference indicators for utilities and regulators to monitor and maintain minimum levels of service or performance. The application of RCM to HV breakers is a relatively new concept for many utilities; CIGRÉ Brochure 165 [5 ]2 cites the introduction of RCM in transmission and distribution companies as starting from 1991. Traditionally HV breaker preventative maintenance has been dictated according to manufacturer’s recommendations which are still normally based on a combination of Time Based Maintenance (TBM9.16) together with some Condition Based Maintenance (CBM9.17) guidelines relating to the usage of the breakers. CIGRE Brochure 165 (Parts II & III), refers to a survey of 45 utilities conducted in 1998 indicating a planned shift from TBM and CBM dominated strateiges towards more CBM + RCM dominated strategies (CIGRÉ Figs III.2 & III.4). TBM is most commonly where the item to be maintained has well-defined and dominant time-dependent degradation of key functions. An example of such a dependence could be the elasticity in a rubber seal or the drying out of lubricant. CIGRÉ Brochure 167 on monitoring and diagnostics for HV switchgear [6 ] clarifies the difference between CBM and PM. While both methods aim to predict when maintenance will be required, PM normally will base the prediction on the last, most recent inspection or maintenance in combination with a knowledge of the equipments operating duty and environment. In contrast, CBM is normally based on use of continuous or routine monitoring of the equipment to predict maintenance “based on the present needs of the equipment condition”. In short, CBM is viewed as PM applied with more up-to-date information on the equipment condition. There are a number of normative and guidance publications existing to support utilities in devising and implementing an RCM program for HV breakers. Some relevant examples include: IEC: IEC 60300-3-11 Dependability management – Part 3-11: Application guide –

Reliability centred maintenance[3 ]

2 Numerous references are made to CIGRÉ Brochure 167 in the remainder of this report, all traced to ref [5 ]. Further references will focus on the specific parts of this CIGRÉ document.

7

IEEE: IEEE Std C37.10-1995 IEEE Guide for Diagnostics and Failure Investigation of

Power Circuit Breakers[4 ] CIGRÉ: CIGRÉ Brochure 165 Life Management of Circuit-Breakers[5 ] IEC 60300-3-11 [3 ] provides a general process description for RCM implementation:

Figure R3: IEC flowchart for a general RCM programme. HV breakers differ from most other HV power system equipment in that they are subject both to high electrical and mechanical stresses during service. In addition their primary function in regard to circuit switching involves high dynamic stresses. The CIGRÉ definitions for HV breaker failure modes are dominated by cases relating to the failure of a breaker to perform its dynamic operations as intended.

8

The next sections of this report will now address functional description of an HV breaker as a prelude to reviewing types of functional failures. These two subjects provide pre-requisite information for then assessing the application of RCM specifically to HV breakers in more detail. 4. FUNCTIONAL HIGH VOLTAGE BREAKER DESCRIPTION

A functional device description is essential when applying RCM, since as stated by Bergman [8 ] “RCM is a method of preserving functional integrity” and later “Defining function is perhaps the most difficult RCM analysis task”. From a “black box” perspective, a breaker has several main functions:

• Conduct rated currents when closed • Maintain rated insulation withstand across its contacts when open • Close its circuit always and only on command • Open or interrupt its circuit always and only on command, according to its ratings

There is a wide variety of HV circuit breaker designs and configurations in existence. They can be classified in various ways, but the most common classifications (for 72-800kV breakers) are described in Figure R4 below – based on information from [9 ],[10 ],[11 ].

Figure R4: Summary of General HV Breaker Classification by Design There are even more detailed sub-classifications and design differences within those shown above, however those shown here are adequate for a general review of RCM application to HV breakers. From a historical perspective oil and air-blast interrupters are the oldest designs and essentially have been superceded by SF6 interrupters since the mid-1980’s. There has also been an increasing trend towards the use of spring operating mechanisms from the early 1990’s, following from the results of the second CIGRÉ enquiry into the reliability of HV breakers [12 ].

HV Breakers (72-800kV)

Metal Enclosed

(”Dead Tank”)

Non-Metal Enclosed

(”Live Tank”)

Interrupter Type:

Air-blast

Oil

SF6

Operating Mechanism:

Pneumatic

Hydraulic

Spring

Operational Configuration:

Three Pole Operated Single Pole Operated

HV Breakers (72-800kV)

Metal Enclosed

(”Dead Tank”)

Non-Metal Enclosed

(”Live Tank”)

Interrupter Type:

Air-blast

Oil

SF6

Operating Mechanism:

Pneumatic

Hydraulic

Spring





9

At the most basic level a breaker can be considered as an interrupter or set of contacts that is switched to a closed on open state on command. Control of the circuit breaker is normally via a combination of local controls at the breaker, substation protection system and the network control system. The main functions of a circuit breaker described above can be divided into “static” (conducting current when closed; maintaining insulation across open gap when open) and “dynamic” (closing a circuit; opening or interrupting a circuit) functions. The dynamic functions must be carefully controlled and there are certain conditions that must be fulfilled for closing or opening to be made successfully. For a breaker to close successfully the following minimum conditions must be met:

• The breaker should be in fully open position • Sufficient stored energy for a closing (or close-open) operation should be available • Sufficient dielectric strength in the breaker (to manage immediate opening) • A valid closing command issued (with necessary operating latch release energy available)

For a successful opening or trip operation, the required pre-conditions are similar, but with some additional measures:

• The breaker shall be in the fully closed position • Sufficient stored energy for an opening operation (with current interruption) should be

available • Sufficient dielectric strength in the breaker (to manage current interruption and transient

recovery voltage withstand) • Sufficient commutation margin between arcing and main contacts • Sufficient dielectric flow control into arc interruption region (between arcing contacts) • A valid opening command issued (with necessary operating latch release energy

available) The current interruption process for breakers becomes considerably more complex when considering the broad range of interruption cases a breaker must perform, ranging from near resistive load or highly reactive (capacitive or inductive) load to fault currents. It is easiest to explain the interruption process of a HV breaker by considering an example of a specific design. Figure R5 below (based on Garzon Fig5.39 [9 ] and van der Sluis Fig 4.5 [10 ]) illustrates a generic SF6 puffer interrupter. SF6 interrupters are the most common design in production today for breakers above 36kV.

10

Figure R5: Illustration of a Generic SF6 Puffer Interrupter Because of the different stresses on breaker contacts conducting load current when closed compared to interrupting arc currents, breakers are normally equipped with arcing and main contacts of different materials and specially co-ordinated operating times. Arc contacts are typically made of a tungsten-based alloy that is highly resistant to erosion from the higher current densities and temperatures associated with arcs (especially fault arcs, which may reach 20000K). Main contacts are typically silver plated, copper, designed to conduct load currents with minimal contact resistance and therefore low losses and contact tempertures. During opening, the arcing and main contact opening times are normally co-ordinated so that the main contact open first and the current is commutated to the arcing contacts. Once the arcing contacts open, an arc is formed, which should then be interrupted at a current zero. During closing, the contact operating sequence is reversed. On HV breakers normally a small pre-arc occurs as the gap between the arcing contacts becomes small and then as the arcing contacts close they carry the current until the main contacts close and they largely takeover the current conduction duty. Normally HV breakers are also equipped with some form of arc control and dielectric flow control device, built around and between the arcing contacts (the “nozzle” in an SF6 breaker). These devices help both restrict the arc to being between the arcing contacts and away from the main contacts at the same time directing dielectric medium into the arc region to cool the arc and establish a vey rapid dielectric withstand at arc current zero. Figures R6a & R6b below (copied from own Licentiate Thesis [11 ]) illustrate the basic operating principle of an HV (SF6 puffer) interrupter for fault interruption, described in terms of certain time intervals or instants (T1 to T7):

FIXED ARCING

CONTACT

FIXED MAIN

CONTACT

”NOZZLE”

”PUFFER”

VOLUME

MOVING

CONTACT

CYLINDER

FIXED

PISTON

SINGLE

PRESSURE

SF6

VOLUME

HV TERMINAL

HV TERMINAL

MOVING ARCING

CONTACTMOVING MAIN

CONTACT

ARCING CONTACT

GAP

OPERATING

ROD

FIXED ARCING

CONTACT

FIXED MAIN

CONTACT

”NOZZLE”

”PUFFER”

VOLUME

MOVING

CONTACT

CYLINDER

FIXED

PISTON

SINGLE

PRESSURE

SF6

VOLUME

HV TERMINAL

HV TERMINAL

MOVING ARCING

CONTACTMOVING MAIN

CONTACT

ARCING CONTACT

GAP

OPERATING

ROD

11

Figure R6a: Fault Interruption Process of a HV (SF6 Puffer) Breaker

T1: Fault starts. T2: Protection system has detected fault and sends trip / open command to breaker T3: Breaker has responded to trip command and its contact have moved to point where the

main contacts separate. Now current is commutated to the arcing contacts. T4: Contacts have now moved to the point where the arcing contacts separate. An arc is

formed between the arcing contacts. T5: First fault current zero is encountered, but contact gap and SF6 gas flow is too small to

succeed in interruption and current re-ignites after current zero. T6: Current interrupted at next current zero. T7: Peak Transient Recovery Voltage (TRV) withstood by opening contacts.

Time (sec)

Current

0.02 0.036 0.052 0.068 0.084 0.1

Voltage Across Circuit Breaker (TRV)

0.02 0.036 0.052 0.068 0.084 0.1

TRIP

SIGNAL

MAIN

CONTACTS

ARCING

CONTACTS

CO

NT

AC

T T

RA

VE

L

T1 T2 T3 T4

T6

T5

T7

Time (sec)

Current

0.02 0.036 0.052 0.068 0.084 0.1


0.02 0.036 0.052 0.068 0.084 0.1


0.02 0.036 0.052 0.068 0.084 0.1

TRIP

SIGNAL

MAIN

CONTACTS

ARCING

CONTACTS

CO

NT

AC

T T

RA

VE

L

T1 T2 T3 T4

T6

T5

T7

12

It must be stressed that the above figure is only an example. Different (fault) currents will exhibit different current zero times with respect to contact parting and so the breaker may interrupt at the first or second current zero after contact parting, depending on the arcing time and the current type and magnitude.

Figure R6b: Fault Interruption Process of a HV (SF6 Puffer) Breaker

This description can be enhanced by considering additional details to the basic open and close functions. For example, HV breakers are required by international standards (e.g. IEC 62271-100 Cl 4.104 [15 ]) to be able to perform auto-reclosing duties, i.e. operating sequences like: Open – 0.3 s – Close-Open-3min-Open, or Close-Open – 15 s – Close-Open

T1-T2 BREAKER

CLOSED

T3 MAIN CONTACTS

OPEN

COMMUTATION

TO ARCING

CONTACTS

T4 ARCING CONTACTS

OPEN

ARC FORMS

PRESSURE RISE

T5 1st CURRENT ZERO

TOO SHORT ARCING

TIME LEADS TO

RE-IGNITION

T6 2nd CURRENT ZERO

SUCCESSFUL

THERMAL

INTERRUPTION

T7 TRV PEAK

SUCCESSFUL

DIELECTRIC

WITHSTAND

T1-T2 BREAKER

CLOSED

T3 MAIN CONTACTS

OPEN

COMMUTATION

TO ARCING

CONTACTS

T4 ARCING CONTACTS

OPEN

ARC FORMS

PRESSURE RISE

T5 1st CURRENT ZERO

TOO SHORT ARCING

TIME LEADS TO

RE-IGNITION

T6 2nd CURRENT ZERO

SUCCESSFUL

THERMAL

INTERRUPTION

T7 TRV PEAK

SUCCESSFUL

DIELECTRIC

WITHSTAND

13

In order to perform the above operating sequences the breaker is normally required to have some form of operating energy storage. Due to the strenuous duties required by HV breakers they are typically very large items of equipment, requiring large operating energies (in the order of 2 to 30kJ per opening operation, depending on interrupter rating and design). There must be a system for tranferring the mechanical operating energy from to the moving contact system in the interrupter. In addition it is required in international standards (e.g. IEC 62271-100, Cl 5.4 [15 ]) that circuit-breakers be equipped with so-called “anti-pumping” control. This requires that if the breaker receives a close command, the close circuit shall respond and then be disabled until the same close command is removed. This is to avoid the case where a breaker closes onto a fault and receives a near instanteous trip signal on closing. If the original close command is still present after the trip has occurred, the breaker should not attempt to re-close until a second or new close command is sent. This stops a breaker performing repeated CO operations (referred to as “pumping”) in the presence of a “long” close command with a persistant trip condition. For single pole operated breakers, there can be the additional control requirement that if not all poles of the circuit breaker close within a set time window (ususally set between 0,5 to 3 s) a three phase trip command is sent to the breaker to bring all three phases back to the open position. This is to avoid “single phasing” of the three phase system, which can cause major imbalances in load flow and potentially major damage to large motors or generators. The main functional modules of a generic HV breaker and its main interfaces to the remainder of the power system as described in Figure R7 below.

Figure R7: Functional Description of HV Breaker and its System Interfaces

Energy

Storage

Energy

Charging

Energy

Release

Energy

Transmission

Contr

ol &

Sig

nalin

g

Interrupter

Arcing Contacts

Main Contacts

Arc Control Device

Dielectric Monitor

Dielectric (Interrupting Medium)

HV Insulators / Housing

Protection

Relays

Auxiliary

Supplies

Network

Control

HV Primary

Connections

High Voltage Circuit-Breaker

Energy

Storage

Energy

Charging

Energy

Release

Energy

Transmission

Contr

ol &

Sig

nalin

g

Interrupter

Arcing Contacts

Main Contacts

Arc Control Device

Dielectric Monitor

Dielectric (Interrupting Medium)

HV Insulators / Housing

Protection

Relays

Auxiliary

Supplies

Network

Control

HV Primary

Connections

High Voltage Circuit-Breaker

14

5. FAILURE MODES OF HIGH VOLTAGE BREAKERS

The functional view of a breaker is reinforced by the methods applied in assessing breaker reliability. CIGRÉ has conducted two (2) major international studies on circuit breaker reliability to date (a 3rd is now in progress)[12 ]. In these studies, failures have been classified in terms of their functional impact. The two main CIGRÉ [12 ] breaker failure classifications are: Major Failure (MF) : Complete failure of a circuit-breaker which causes lack of one or more of

its fundamental functions. Note: A major failure will result in an immediate change in the system operating condition…(intervention required within 30 minutes).

minor failure (mf) : Failure of circuit-breaker other than major failure; or any failure, even

complete, of a constructional element or a sub-assembly which does not cause a major failure of a circuit-breaker.

It should be noted that even though a minor failure does not result in the immediate loss of a breaker’s primary function(s), it may either develop into a major failure, or in any event require user intervention / maintenance which usually requires the breaker to be taken out-of-service i.e. a forced outage. The CIGRÉ breaker reliability surveys have been an important source of information to the industry (both utilities and manufacturers), and provide a useful reference in consideration of RCM planning and assessment on HV breakers. However some important points should be noted in respect of the first and second surveys:

• Both survey results are now somewhat “old”. The first covering HV breakers of “all technologies” from 1974 to 1977. The second focussing on 18,000 single pressure SF6 breakers from 1988 to 1991. CIGRÉ Working Group A3.06 is now conducting a third and even more comprehensive survey. (The figures quoted from the surveys in this report are from CIGRÉ paper 13-202, published in 1994 [12 ]).

• While reasonably detailed, considering the scope and complexity of such an international survey task, there is an acknowledged risk that the data is subject to variance in interpretation by the respondents in terms of classifying cause of failures.

• Some differences in definitions between the surveys make comparison of results difficult, in addition to fact that the second enquiry focussed on SF6 single pressure interrupter breakers and the first enquiry focussed on all types of interrupter.

Types of circuit breaker failure investigated by the surveys and their failure rates are summarised in CIGRÉ Table 6, reproduced below:

15

CIGRÉ Table 4, reproduced below, summarizes the reported failure types, causes and frequencies from the two surveys on HV breaker reliability:

The above two tables provide some insight into areas of most concern regarding breaker reliability:

• Failures to operate-on-command or operating-without-command accounted for between 40-50% of reported failures in both surveys

Characteristic MF per

100

CByears

(% total) MF per

100

CByears

(% total)

Does not close on command 0,33 33,3% 0,164 24,6%Does not open on command 0,14 14,1% 0,055 8,2%Closes without command 0,02 2,0% 0,007 1,0%

Opens without command 0,05 5,1% 0,047 7,0%Does not make the current 0,02 2,0% 0,011 1,6%

Does not break the current 0,02 2,0% 0,020 3,0%Fails to carry current 0,02 2,0% 0,010 1,5%

Breakdown to earth 0,03 3,0% 0,021 3,1%Breakdown between poles 0,00 0,0% 0,010 1,5%

Breakdown across open pole (internal) 0,04 4,0% 0,024 3,6%Breakdown across open pole (external) 0,01 1,0% 0,010 1,5%

Locking in open or closed position 0,00 0,0% 0,190 28,5%Others 0,31 31,3% 0,098 14,7%

No answer 0,59 0,006

Totals 1,58 100,0% 0,673 100,0%

First enquiry Second Enquiry

Table 6 - CIGRÉ Paper 13-202, August 1994 - 2nd International CB Reliability StudyMajor Failure rate by characteristic for

first and second enquiry

CB years

Components

at service

voltage

0,76 48% 0,14 21% 0,92 26% 1,44 31%

El. control and

aux. circuits

0,3 19% 0,19 29% 0,57 16% 0,92 20%

Operating

Mechanism

0,52 33% 0,29 43% 2,06 58% 2,05 44%

Others 0,05 7% 0,25 5%

Totals 1,58 100% 0,67 100% 3,55 100% 4,66 100%

Failures per 100 CByearsTable 4 - CIGRÉ Paper 13-202, August 1994 - 2nd International CB Reliability Study

Subassembly

responsible

Major Failure (MF) Rate minor failure (mf) Rate

1st Survey 2nd Survey 1st Survey 2nd Survey

77892 70708 77892 70708

16

• The high percentage of locked-in-postion faults in the second survey is in part attributed to the second survey’s focus on SF6 breakers, where the monitoring of SF6 in the breaker is often arranged to “block” breaker operations once the density level falls to a specified critical level.

• There was a distinct reduction in the number of failures arising from “components at service voltage” i.e. HV components between the first and second survey. This could be attributed to improvements in interrupter design, often also attributed to the reduction in number of series interrupters required per phase for SF6 versus air-blast or oil breakers.

• Breaker operating mechanisms consistently accounted for over 40% of failures in both surveys. It should be noted that while the second survey focussed on SF6 interrupter breakers, it reported use of three main types of operating mechanism; pneumatic, hydraulic and spring. It has been inferred by this CIGRÉ report that spring operating mechanisms exhibited the least number of failures and this has been a significant factor affecting further SF6 breaker development (see later).

More specific insight into the attributed relationships between faults and components for SF6 breakers is provided in CIGRÉ report Table 5 reproduced below:

CIGRÉ report 13-202 draws some important general conclusions, including:

• Operating mechanisms are the sub-assembly with the most failures – further work was suggested to “simplify the electrical and mechanical control process”

• While only 7% of major failures in SF6 breakers were attributed to loss of gas, approximately 40% of minor failures were due to this problem, leading to the conclusion that additional effort was needed (in the early 1990’s) to address SF6 gas pole sealing and monitoring

• Additional type testing for mechanical endurance, climate testing and life cycle assessment were recommended (see later discussion on impacts on revisions to IEC breaker standards)

(% total) (% total)

1. Components at service voltage 99 21,0% 1019 30,9%1.1 interrupting unit 66 14,0% 310 9,4%

1.2 auxilliary interrupter / resistor 6 1,3% 20 0,6%

1.3 main insulation to earth 27 5,7% 689 20,9%

2. Electrical control and auxiliary circuits 137 29,0% 650 19,7%2.1 trip / close circuits 47 10,0% 49 1,5%

2.2 auxiliary switches 35 7,4% 69 2,1%

2.3 contactors, heaters etc 36 7,6% 178 5,4%

2.4 gas density monitor 19 4,0% 354 10,7%

3. Operating mechanism 204 43,2% 1449 44,0%3.1 compressors, pumps etc 64 13,6% 615 18,7%

3.2 energy storage 36 7,6% 238 7,2%

3.3 control elements 44 9,3% 383 11,6%

3.4 actuators, dampers 42 8,9% 168 5,1%

3.5 mechanical transmission 18 3,8% 45 1,4%

4. Others 32 6,8% 178 5,4%Totals 472 100,0% 3296 100,0%

Subassembly responsibleFailures per subassembly or component

Table 5 - CIGRÉ Paper 13-202, August 1994 - 2nd International CB Reliability Study

Major Failures minor failuresNo. of failures No. of failures

17

• Encouragement was made for the use of “common definitions for reliability studies and maintenance techniques”.

One further aspect of the CIGRÉ reliability surveys is of relevance to consideration of RCM on HV breakers. Table 8 from the CIGRÉ 13-202 report is shown below:

It is of interest to note the the change in the percentage of failure attributed to “Incorrect maintenance” from the first to the second survey. The CIGRÉ report comments that only a small percentage of failures (6-13%) were detected during maintenance, which suggested that it maintenance practices (at least at the time of these surveys) was not very effective in determining potential problems in a breaker that may lead to failure. While the CIGRÉ surveys were heavily focussed on the breaker as a system in itself, they were also conducted with the intention to provide input for system relaibility studies. There are other surveys e.g. Nordel [13 ] (see translated extract below), that provide some more insight into the potential impact of breaker relaibility on overall system reliability.

It should be noted that the Nordel statistics only relate to the reported information from the Scandinavian countries and that there may exist “special” local conditions (e.g. climatic extremes) that effect these statistics. Nevertheless such data can be useful in providing guidance

Cause First

enquiry

Second

enquiry

First

enquiry

Second

enquiry

Design 25,4% 24,7%Manufacture 28,7% 39,1%Inadequate instructions 0,7% 1,1% 0,3% 1,7%

Incorrect erection 9,3% 8,2% 10,7% 7,1%Incorrect operation 1,2% 6,0% 0,2% 4,5%Incorrect maintenance 8,1% 2,8% 4,5% 2,6%Stresses beyond specification 4,8% 3,4% 0,7% 1,8%

Other external causes 2,3% 5,4% 1,7% 6,6%

Other 28,3% 19,0% 29,4% 11,9%

Totals 100,0% 100,0% 100,0% 100,0%

45,3% 52,5%

Table 8 - CIGRÉ Paper 13-202, August 1994 - 2nd International CB Reliability Study

Cause of the Major and minor failuresMajor Failure minor failure

Extract from Table 4.3 Nordel Fault Statistics Report 2003

Apparatus attributed to fault

Percent impact on Non-

delivered energy (average

2000-2003)

Overhead line 15,6%

Power cable 6,6%

Power transformer 5,8%

Circuit breaker 2,1%

Disconnector 25,3%

Substation control equipment 12,1%

18

to utilities on which power system apparatus requires the most focus for RCM at the overall power system level. 6. RCM APPLIED TO HIGH VOLTAGE BREAKERS

As described earlier, RCM is a process that can be applied at the overall power system level and at the power system component (i.e. breaker) level. The first part of this section will concentrate on the breaker as a component; specifically the discussion will centre on SF6 breakers. Later, some references to the application of breaker RCM applied at the power system level will be presented, with reference to a broader range of breaker technologies. CIGRÉ Brochure 165 [5 ] provides a good summary of the reported experience of utilities and manufacturer’s in the formulation of breaker maintenance principles, leading in part to recommendations on application of RCM. CIGRÉ paper 500-06 within Brochure 165 describes a seven step RCM process, summarized as follows:

1. System selection 2. System boundary definition 3. System description 4. System function and functional failures (as opposed to equipment failures) 5. Failure Modes, Effects and Criticality Analysis (FMECA), including development of

appropriate preventative maintenance tasks 6. Logic tree analysis (leading to prioritization of failure modes and related maintenance

tasks) 7. Maintenance task selection

This CIGRÉ paper 500-06 is at pains to point out that the RCM process depends heavily on considerable detailed knowledge of the system or equipment to be addressed, including not only statistical performance data, but the proper analysis work to develop appropriate maintenance activities directed towards meeting reliability goals. In short RCM implementation requires considerable effort, and is a continuos process with feedback from ongoing service and maintenance experience leading (ideally) to continuos improvement and optimization. As can be seen from the previous section on HV breaker failures, potential problems can arise in every subsystem of a breaker. It is not necessarily so that maintenance can prevent all such failures. Nevertheless it is possible to plan maintenance of breaker that can at least account for the reduction in performance security that occurs due to “wear and tear” of the breaker arising from its normal operational duties. This relates to key issues for RCM on HV breakers as components:

• What items to maintain? • When should they (best) be maintained? • How should they (best) be maintained?

19

List I.1, p8 of CIGRÉ provides a convenient summary relating wear factors to specific subsystems in a HV breaker. The basic relationships described in this list are presented in Table R2 below with some additional inference on the degree of wear influence per case. The main message of this table is that the “wear” effects on breakers can be a combination of accumulated mechanical operation stress, current interruptions and in some cases time. The dominant wear inducing factor is accumulated mechanical operations, bearing in mind that this is a number greater than the number of current interruptions, since when a breaker is operationally tested either in a factory or at site, it is normally disconnected from the power system and so not interrupting currents. Considering the dynamic nature of a breaker and the high mechanical energies required for HV breaker operation, it is not surprising that accumulated operations and thus accumulated mechanical stress forms a dominant role in determining breaker wear, thus impacting on breaker reliability.

Table R2: Summary of Breaker Subsystem Wear Factors (Based on CIGRÉ Brochure 165 List I.1)

CIGRÉ Brochure 167 [6 ] describing monitoring and diagnositic techniques for HV switchgear also includes much valuable guidance on relationships between breaker functions and the component subsystems normally providing those functions within the breaker e.g. see Tables 2.1-2.4 in same reference. The most “time affected” parts of a circuit-breaker are normally seals (static and dynamic) and “open” lubricated parts (e.g. latches or bearings in the mechanical transmission system). NGC UK reports in CIGRÉ paper 13-302 [22 ] of the time-under-pressure exposure life limits seen with air-blast breakers – compounded by the exposure to high oxygen contents in such breakers.

Breaker Component or Subsystem

Subject to Operational Wear

Number of

operations

(mechanical)

Number of

interruptions

(current)

Number of

Energy Storage

Recharging

Cycles

Hours

Exposure to

Environmental

Extremes

Time

Arcing Contacts L H

Main Contacts M L

Nozzles (SF6 breakers) H

Operating Mechanism H H L-M

Auxiliary Contacts H L-M

Gas Sealing System L-M L-M M

L = Low influence

M = Moderate Influence

H = High Influence

Wear Inducing Factors

20

It is important to also note that Table R2 above is only a qualitative guide to breaker wear behavior. The individual subsystem dependencies on number of operations or time are not necessarily linear. There can be a complex combination of relationships existing, differing substantially between breaker designs and technologies. Classical, generic wear behaviors (not necessarily breaker specific) are described in CIGRÉ Brochure 165 Figures I.3 and I.4 as reproduced below:

The above figures highlight the important difference between wear patterns that can exist for “predictable” and “non-predictable” failures. The non-predictable failures can only be rectified by corrective maintenance or possibly design modification. In the case that some generic problem or weakness is discovered on a specific breaker type or technology, then the utility may be able to implement “planned corrective maintenance” on the other other breakers of this type to minimize the risk of the problem developing to failure. More typically with maintenance, one thinks of the application of preventative maintenance, addressing those components that can have “predictable” wear behaviors. A specific example of predictive wear maintenance is on the interrupters of a breaker. As implied in Table R2 there is usually a well known relationship between accumulated current interruptions and interrupter wear. In SF6 breakers such wear is normally focussed on the arcing contacts and the nozzle. Figures R8 and R9 below (copied from own Licentiate Thesis [11 ]) illustrate this wear relationship:

21

Figure R8 – Influence of Current Arc Raditiation on Arc Contact & Nozzle Erosion

Figure R9 – Illustration of Typical Arcing Contact and Nozzle Erosion Effects If the commutation “margin” between the arcing and main contacts becomes too small there is the risk that proper commutation may not occur and an arc might be formed between the main contacts and outside the arc control device. Alternatively, if the arc and dielectric flow control devices become too worn with successive interruptions the effectiveness of interruption at arc current zero can be reduced. In either of these cases there is the risk of the breaker failing to interrupt once its contacts have fully opened, which in the worst case could lead to a catastrophic (i.e. explosive) breaker failure. Three important questions exist on interrupter wear:

• What are the wear limits? • How can the wear be measured, monitored or assessed for in-service breakers? • How often will the wear limits be approached?

Arc Energy released

Radiation &

convection

Dissociation

& ionization

Erosion

Input Energy:

∫ugap(t) . ifault(t) dt

Arc contact erosion

Nozzle erosion

ifault(t)

ugap(t)

”Nozzle”

Arc Energy released

Radiation &

convection

Dissociation

& ionization

Erosion

Input Energy:


Arc contact erosion

Nozzle erosion

ifault(t)

ugap(t)

”Nozzle”

Arc Energy released

Radiation &

convection

Dissociation

& ionization

Erosion

Input Energy:


Arc contact erosion

Nozzle erosion

ifault(t)

ugap(t)

”Nozzle”

Arc Energy released

Radiation &

convection

Dissociation

& ionization

Erosion

Input Energy:


Arc contact erosion

Nozzle erosion

ifault(t)

ugap(t)

”Nozzle”

Arc Energy released

Radiation &

convection

Dissociation

& ionization

Erosion

Input Energy:


Arc contact erosion

Nozzle erosion

ifault(t)

ugap(t)

”Nozzle”

ARCING

CONTACTS NOZZLE

”NEW” INTERRUPTER

(No arcing wear)

”WORN” INTERRUPTER

(Arcing wear at maintenance limit)

NOZZLE

EROSION

ARCING

CONTACT

EROSIONARCING

CONTACTS NOZZLE


(No arcing wear)

ARCING

CONTACTS NOZZLE


(No arcing wear)



NOZZLE

EROSION

ARCING

CONTACT

EROSION



NOZZLE

EROSION

ARCING

CONTACT

EROSION

22

Guidance on wear limits is most often sought or given by the breaker manufacturer. This information will normally be derived from the experience of type tests of the breaker design, together with some extrapolation (bearing in mind the high cost of type tests and the range of possible current switching combinations to which a breaker can be subjected). Figures I.1 and I.2 from CIGRÉ Brochure 165 provide examples of interrupter wear behaviors for different current switching duties:

These examples are important to consider in the context of breaker application. Daily switched reactor or capacitor bank breakers may reach their interrupter wear limits much faster than a breaker of the same type installed for line or transformer switching duty. Such a difference is underscored by other studies. For example CIGRÉ paper 13-304 [18 ] reports on a survey conducted by German utilities on the current interruption stresses seen by breakers on the 123, 220 and 420kV German networks. This study found that within a ten year period nearly 70% of the surveyed breakers (2000 in total) did not interrupt a fault. Furthermore it was predicted that between 92-98% of the breakers would not experience wear out of their interrupters within a service life of 35 years. Though only the experience of one specific network survey, similar results are cited in the CIGRÉ Brochure 165, which tends to suggest that most HV breakers can be expected to survive a reasonably long service life e.g. >30years without need of interusive interrupter maintenance. A further consideration in SF6 interrupter wear is that the wear rates on the nozzle and arcing contacts may not necessarily follow the same trend with respect to accumulated interruptions.

23

Lehmann et al [17 ] describe work undertaken to show that the wear rates of arcing contacts and nozzles in SF6 breakers can vary, due to a number of factors including differences in geometries, materials and relative arc exposure times. This can be important as the ability to non-intrusively assess nozzle condition is very difficult compared to assessment of arcing contact wear. It is important to understand that such conclusions on interrupter wear must be related to the specific operational behavior of the network in question and the performance of the breaker types on such a network. However information such as interrupter wear curves shown in CIGRÉ Figures I1 and I2 above are important guides to maintenance planners that can permit focussing breaker maintenance activities on potentially more relevant parts of their breakers that affect system reliability and availability. Avoiding intrusive interrupter maintenance has become even more critical with the advent of SF6 breakers. Increased environmental requirements strictly limiting the release of SF6 to atmosphere mean that strict procedures in the recovery of SF6 must be followed (as would be required for interrupter maintenance). In addition, arcing in SF6 generates a number of by-products (e.g. H2S, HF, CF4 and other compounds – ref Jones et al. [14 ]) which requires strict health and safety procedures for the cleaning and disposal of arc exposed SF6 interrupter components. As indicated in Table R2 and from the earlier cited CIGRÉ breaker reliability survey figures, operating mechanism failures are a major potential source of problem and thus target for preventative maintenance. Addressing operating mechanism failure mitigation through maintenance is difficult, as the more critical failures can often occur suddenly, with little prior indication – as inferred from the CIGRÉ finding that only between 6-13% of failures were detected during maintenance. It is further complicated from the utility perspective that the detailed critical knowledge of the mechanism design resides with the manufacturer and so the manufacturer must provide adequate recommendations on what items of the mechanical system need to be maintained, when and how. Conversely there is the problem for the manufacturers that they must develop competitive breakers and even for a certain rating “class” eg 145kV, 40kA, 50 / 60Hz the breakers can end up in a variety of operational duties even within a single power system e.g. seldom switched line, transformer or busbar breakers to frequently switched capacitor or reactor bank breakers. The CIGRÉ breaker reliability surveys and summary reports like Brochure 165, highlight the need for exchange of information between utilities and manufacturers in order to achieve a practical basis for implementing RCM. Some results of such feedback is evident in the trends in breaker designs and reflected in the changes to international breaker standards (as is summarized later in this report). There are general guidelines that can be referenced to the type of maintenance activities that can be undertaken, with respect to number of operations and/or time, cited both in the CIGRÉ Borchure 165 and by manufacturers (e.g. Bosma, Thomas [20 ]). Such guidelines describe three (3) levels or categories of breaker maintenance activity, summarized in Table R3 below. It should be noted that the “typical frequency” and the service time indications for maintenance category below can vary considerably depending on both the circuit breaker type / design and it operational duty (e.g. switching frequency, environmental conditions). It should also be borne in mind that even where a utility has breakers of similar technology (e.g. SF6 interrupter with spring

24

operating mechanism), there can be a mix of breakers from different manufacturers with slightly different maintenance wear limit or activity recommendations.

Table R3: Summary of Typical HV Breaker Maintenance Task Categories An interesting example of the implementation of such a categorized maintenance, directed in general RCM process terms is described in CIGRÉ paper 13-303 by ESB International and ESB National Grid [23 ]. This paper outlines a process of evaluating the when specific categories of maintenance should be performed on breakers, including the prioritization of breakers for maintenance according to system availability risk. The ESB maintenance taks categories are not identical to the A, B, C types described above, but similar:

• Operational Testing (OPT) – made annually (comparable to part Category B above) • Operational Servicing (OS) – made every 3-4 years, includes and OPT (comparable to

Category B above) • Detailed Servicing (DS) – made every 12-24 years, depending on design of breaker

(comparable to Category C above) Interestingly, ESB state that in their experience “developing faults” in breakers are detected roughly 1:100 OPT or OS activities and 10-20:100 DS activity, which seems consistent with the overall CIGRÉ reported experience of the difficulty in detecting faults during maintenance. ESB describe a quantities called “Expected Regret” and “Prevention Ratio” which are used for this RCM-oriented approach. “Expected Regret” (ER) is a measure of the risk incurred for deferring a planned (preventative) maintenance task. ESB evaluate this by a formula:

Maintenance

Category

Details Frequency

(typical)

CB Service Status

A

Visual Inspection:

Record operations count,

check no obvious signs of

damage or leakages, check

insulating medium levels (e.g.

SF6 density monitor reading)

Every 1-2 years In-service (mostly);

Alternatively out-of-

service 0.5 day

B

Non-intrusive condition

assessment (e.g. CB contact

times, speeds, damping

curves, contact resistance),

cleaning insulators, minor

lubrication (latches / gears)

5-10 years,

depending on CB

type / design

Out-of-service; 1 day

C

Fully detailed (intrusive)

overhaul. More dismantling of

mechanism and/or poles,

replacement of worn contacts,

seals, linkages

10-20 years; 2-

10000 operations or

at interrupter wear

limit depending on

CB type & design

Out-of-service; 3-5

days

25

ER = S x C ER = Expected regret incurred from deferring a maintenance task S = Severity of consequences of breaker failure in service C = Change expected in the risk of breaker failure due to maintenance task deferral “Prevention Ratio” (PR) is ESB’s measure of the effectiveness of planned maintenance tasks and is defined by a simple ratio formula:

No. of prevented failures PR = ------------------------------------------------------------ No. of prevented failures + No. of Failures

= e-t/τ

PR = Prevention ratio t = Maintenance task interval (years) τ = Process time constant (years) ESB summarize the calculated prevention ratios expected from different tasks, according to the planned time schedule for the task resulting in Table II in the paper (reproduced below):

The paper also describes a ranking system applied to types of functional failure and their impact at a system (substation) level e.g. specific CB failure causing tripping of “neighboring” breakers or not. The paper describes how the “Expected Grief” is calculated from doing no maintenance at all for one year. This figure is then used together with the PR figures to assess impact on “Expected Grief” of deferring different maintenance activitied per breaker for a defined period e.g. 1 year. The “Expected Regret” for maintenance deferral is then evaluated as the change in “Expected Grief” times the deferral period. This is made for a set population of breakers to derive a priorty list of breakers for the different maintenance activities. The proposed ESB system seems to have the advantage for being fairly straightforward, and provides a means of both assessing the impact (for their system) of re-scheduling maintenance, in addition to prioritization of maintenance activities for the coming year. EdF (France) describe a somewhat detailed application of RCM to SF6 breakers in Appendix D1 of CIGRÉ Brochure 165 [5 ], which contains similar steps-in-principle to the ESB approach. EdF

Planned

Maintenance

Task

Prevention

Ratio

Process

Time

Constant

(Years)

OPT 0,90 9,5

OS 0,82 15,0

DS 0,81 85,4

26

formally apply an FMECA to set rankings for failure consequences (seriousness) and frequency and using these to calculate and overall “criticality” number per fault type. However the EdF approach goes into further equipment detail in also assessing the “applicability” of specific maintenance tasks in terms of their effectiveness (degree to which a task can detect or rectify a fault) and facility (in short, “how easy” or practical is the task). EdF stated the following relations were applied: CRITICALITY = FREQUENCY x SERIOUSNESS (of fault / failure) ACCPLICABILITY = EFFECTIVENESS x FACILITY (of maintenance task) EdF go on to describe the use of a maintenance task selection process using a simple decision tree as shown below (Figure 1.6.1.1 from Appendix D1 of CIGRÉ Brochure 165):

Figure R10: EdF Task Selection Decision Tree

(Based on Fig 1.6.1.1. Appendix D1, CIGRÈ Brochure 165) The level of effort and complexity required for a detailed equipment-based RCM approach can be somewhat of a deterrent to most utilities, especially in regard to populations of breakers of older technologies, where the detailed knowledge of the equipment is diminished or possibly even lost. National Grid (UK) describe such problems in their CIGRÉ paper 13-302 [22 ]. They provide the example of an old 420kV air-blast breaker that has 36-interrupters containing over 20000 components, approximately 1500 of which are moving parts and 2500 are various types of seals. The paper describes a higher power system level approach to breaker RCM, directed towards

FMECA & TASK ANALYSIS

CRITICALITY ≥≥≥≥ 6

of the failure?

APPLICABILTY ≥≥≥≥ 6

of the task?

FAILURE

ACCEPTED?

OTHER TASK

PREVENTIVE

MAINTENANCE

NOMINAL

MAINTENANCE

CORRECTIVE

MAINTENANCE

NO YES

NO

NO

YES

YES

FMECA & TASK ANALYSIS

CRITICALITY ≥≥≥≥ 6

of the failure?

APPLICABILTY ≥≥≥≥ 6

of the task?

FAILURE

ACCEPTED?

OTHER TASK

PREVENTIVE

MAINTENANCE

NOMINAL

MAINTENANCE

CORRECTIVE

MAINTENANCE

NO YES

NO

NO

YES

YES

27

determining a reasonable program for replacement of older technology breakers for which both the risk of failure and the cost of preventative maintenance is deemed too high. The CIGRÉ surveys on HV breaker reliability also contain interesting data on HV breaker maintenance and its relation to reliability. Summarized from Tables 10a and 10b in the CIGRÉ 13-202 1994 survey report [12 ], show the reported average maintenance intervals and effort per breaker voltage level:

The above table shows a distinct trend towards increased maintenance intervals for SF6 breakers compared to the overall averages for the mix of technologies in the first survey. In addition the “cost” of maintenance (in manhours per year) is reported to be overall lower for the SF6 population. This provides some indication of the impact of improved reliability and lower maintenance cost achieved through the introduction of new technologies that can lead to less complex devices offer the same or even higher functions / performance. This aspect is discussed further in the next section. 7. IMPACT OF RCM ON BREAKER DESIGNS AND STANDARDS

Even if the application of RCM at the equipment level on breakers is possibly limited to date, there are indications that the reliability focussed concerns associated with RCM have impacted on circuit breaker design and on international breaker standards. Both the CIGRÉ breaker reliability surveys and Brochure 165 highlight the desire for more reliable breakers or at least breakers requiring as little intrusive detailed maintenance as possible in order to maintain a minimum acceptable system reliability. This desire is reflected in recommendations for more extensive mechanical and electrical endurance testing of new breakers, both of which are now included in the most recent (2003) edition of the main IEC breaker standard, IEC 62271-100 [15 ]. This standard includes new “classes” for breakers relating to overall mechanical endurance: Class “M1”: Mechanical operations type test to 2000 CO operations (without maintenance) Class “M2”: Mechanical operations type test to 10000 CO operations (without maintenance) The previous IEC 60056 required only the 2000 CO operation endurance test. The addition of the 10000 CO operation test is mainly aimed at frequently operated breaker applications, such as

Voltage class (kV) First enquiry

(all CB types)

Second

enquiry

(SF6 CBs)

First enquiry (all

CB types)

Second enquiry

(SF6 CBs)

First enquiry (all

CB types)

Second enquiry

(SF6 CBs)

63 ≤ kV < 100 2,30 7,60 19,60 15,30 55,00 25,40

100 ≤ kV < 200 2,00 8,80 34,00 17,40 38,20 20,70

200 ≤ kV < 300 2,00 8,20 47,40 24,80 87,50 31,60

300 ≤ kV < 500 1,40 8,20 48,50 31,00 72,70 17,70

Average spare parts "cost" (manhours per CB-year)

From Tables 10a and 10b - CIGRÉ Paper 13-202, August 1994 - 2nd International CB Reliability Study

Average interval between scheduled servicing (years)

Average labour effort (manhours per CB-year)

28

shunt capacitor or reactor banks. However given that most manufacturers develop breaker product lines on the basis of “general application” most major suppliers now aim to provide class M2 for all their breakers – thus providing a very high level indication of the breaker capabilities to have little or no maintenance need based on number of mechanical operations (given that daily switched capacitor and reactor banks probably only represent less than 5% of the breaker population overall in most systems). In addition the standard has requirements for electrical endurance testing, initially aimed at “distribution” breakers upto 52kV. This reflects the utility desire to avoid intrusive interrupter maintenance and at the same time have a quantitative reference to the cummulative current switching capability of a new breaker which would assist in future power system level RCM decision making on overhaul or replacement of breakers. A further important requirement in IEC 62271-100 [15 ] and IEC 60694 [19 ] standard type testing is the measurement of SF6 leak rates during high and low temperature tests. The results of such tests (conducted without maintenance) provide important guidelines for utility maintenance planning and reporting in regard to SF6 loss control. One direct example of how RCM concepts can feedback into breaker design is provided in Figure R11 below:

Figure R11: Example of Optimization in HV Breaker Interrupter Design (from CIGRE paper 13-101 Paris 2000, Bosma, Schreurs (ABB) [16 ]) The figure shows two variants of fixed contact design from an HV SF6 breaker. Contact set “2” on the right is the later design, based on a much higher integration of contact design into much fewer parts. Such a design step can be seen to contribute both to higher reliability and easier maintenance by reduction in the number of individual components performing the same basic

29

functions (i.e. conducting and commuting currents). Reduction in the number of components, without resulting in lower individual component reliability, will direct improve the overall reliability of the subsystem, and thus improve the reliability of the breaker as a whole. Integrated modular parts also assist in reducing the time and complexity required during the maintenance process. 8. CONCLUSIONS

Reliability Centred Maintenance is a process, that permits the user to structure the collection, analysis and use of information to optimize preventative maintenance. RCM can be applied at different levels e.g. at the power system network level or at the component (HV breaker level). The use of RCM appears to be growing, based on both the results of surveys, publications by utilities and international standard organizations. It can be seen that the RCM drivers of increased relaibility and lower or more effective maintenance are also reflected in the ammendments to equipment standards and new breaker designs. It is also clear that the application of RCM can require considerable investment in time and resources. It requires detailed knowledge and understanding of the “system” (network or component) for which maintenance and reliability are to be optmized. Such investment and knowledge demands can limit a utility’s approach to a basic “breaker replacement decision management” scheme, though there are cases of its application at a detailed component level also. There are a number of comprehensive industry reference documents (e.g. CIGRÉ Brochure 165 and IEC 60300-3-11) to provide guidance on the detailed implementation of RCM for HV breakers. Important aspects of RCM for breaker applications, given the complexity of the equipment itself include:

• RCM is a structured process • RCM promotes quantification of function and reliability performance • RCM should be executed with continuos feedback – allowing ongoing knowledge and

experience to be utilized in a structured way to maintain optimal product and system performance

30

9. DEFINITIONS AND SYMBOLS The following table summarizes the definitions and symbols used in this report with reference to the source of the defintion (where relevant). The majority of the definitions have been taken from IEC and ANSI/IEEE standards or CIGRÉ guidelines. For reference details, please refer to the corresponding numbered reference in the bibliography at the end of this report. No. Symbol

(Abbrev)

Term Definition Source

[Reference]

9.1 ASAI Average service availability index

Customer Hours Service Availability ASAI = ---------------------------------------------

Customer Hours Service Demands NT x (No. hours / year) - Σ ri Ni = ----------------------------------------------

NT x (No. hours / year)

ANSI/IEEE 1366™2003 [2 ]

9.2 Availability The ability of an item to be in a state to perform a required function under given conditions at a given instant in time or over a given time interval, assuming that the required external resources are provided. Notes 1 – This ability depends on the combined aspects of the reliability performance, the maintainability performance and the maintenance support performance. 2 – Required external resources, other than maintenance resources do not affect the availability performance of an item

IEC 60050-191 [21 ]

9.3 CAIDI Customer average interruption duration index

Σ Customer Interruption Durations CAIDI = ------------------------------------------ Σ Customers interrupted Σ ri Ni = --------- Σ Ni

SAIDI = --------- SAIFI

ANSI/IEEE 1366™2003 [2 ]

9.4 CBM Condition Based Maintenance

Similar to preventative maintenance, but the maintenance done is based on information from monitoring equipment which predicts when maintenance is necessary rather than on the time in service or number of operations

CIGRÉ (Brochure 167) [6 ]

9.5 MF Major Failure Complete failure of a circuit-breaker which causes lack of one or more of its fundamental functions. Note: A major failure will result in an immediate change in the system operating condition…(intervention required within 30 minutes).

CIGRÉ [12 ]

31

No. Symbol

(Abbrev)

Term Definition Source

[Reference]

9.6 mf minor failure Failure of circuit-breaker other than major failure; or any failure, even complete, of a constructional element or a sub-assembly which does not cause a major failure of a circuit-breaker.

CIGRÉ [12 ]

9.7 MTBF Mean time between failure

The expectation of the operating time between failures

IEC 60050-191 [21 ]

9.8 MTTR Mean time to repair

The expectation of the time to restoration IEC 60050-191 [21 ]

9.9 PBM Period between maintenance

Time period between maintenance on any specific breaker


9.10 PM Preventative Maintenance

Maintenance carried out at predetermined intervals or according to prescribed criteria and intended to reduce the probability of failure or the degradation of the functioning of an item

IEC 60300-3-14 [1 ]

9.11 Reliability(1) The ability of an item to perform a required function under given conditions for a given time interval.

IEC 60050-191 [21 ]

9.12 R(t1,t2) Reliability(2) The probability that an item can perform a given function under given conditions for a given time interval (t1, t2).

IEC 60050-191 [21 ]

9.13 RCM Reliability Centred Maintenance

Systematic approach for identifying effective and efficient preventative maintenance tasks for items in accordance with a set of specific procedures and for establishing intervals between maintenance tasks

IEC 60300-11-3 [3 ]

9.14 SAIDI System average interruption duration index

Σ Customer Interruption Durations SAIDI = ----------------------------------------- Total No. of Customers Σ ri Ni = --------- NT

ANSI/IEEE 1366™2003 [2 ]

9.15 SAIFI System average interruption frequency index

Σ Customers interrupted Σ Ni SAIFI = ------------------------------ = -------- Total No. of Customers NT

ANSI/IEEE 1366™2003 [2 ]

9.16 TBM Time Based Maintenance

The preventative maintenance carried out in accordance with an established time schedule


9.17 CM Corrective Maintenance

Maintenance carried out after fault recognition and intended to put an item into a state in which it can perform a required function

IEC 60300-3-14 [1 ]

32

10. REFERENCES AND BIBLIOGRAPHY

[1 ] IEC 60300-3-14 Dependability management – Part 3-14: Application guide – Maintenance and maintenance support, First Edition, IEC, Geneva, March 2004.

[2 ] ANSI / IEEE 1366™-2003 IEEE Guide for Electric Power Distribution Reliability Indicies, IEEE, New York, 2004.

[3 ] IEC 60300-3-11 Dependability management – Part 3-11: Application guide – Reliability centred maintenance, First Edition, IEC, Geneva, March 1999.

[4 ] IEEE Std C37.10-1995 IEEE Guide for Diagnostics and Failure Investigation of Power Circuit Breakers, IEEE, New York, 1996.

[5 ] Life Management of Circuit-Breakers, CIGRÉ Brochure No. 165, CIGRÉ Working Group 13.08, CIGRÉ, Paris, August 2000.

[6 ] User Guide for the Application of Moniotring and Diagnostic Techniques for Switching Equipment for Rated Voltages of 72.5kV and Above , CIGRÉ Brochure No. 167, CIGRÉ Working Group 13.09, CIGRÉ, Paris, August 2000.

[7 ] Electric Power Systems 4th Edition, Weedy B.M., Cory B.J., John Wiley & Sons Ltd, Chichester, 1998.

[8 ] Reliability centred maintenance (RCM) applied to electrical switchgear, Bergman W.J.,

Proceedings of IEEE Power Engineering Society Summer Meeting, vol.2 pp1164-1167,1999.

[9 ] High Voltage Circuit Breakers – Designs and Applications, Garzon R. D., Marcel

Dekker, Inc., New York, 1997.

[10 ] Transients in Power Systems, van der Sluis, L., John Wiley & Sons Ltd, Chichester, 2001.

[11 ] Controlled Switching of High Voltage SF6 Circuit Breakers for Fault Interruption,

Thomas R., Technical Report No 514L, Chalmers University of Technology, Göteborg,

2004.

[12 ] Final Report on High-Voltage Circuit Breaker Reliability Data for Use in Substation and

System Studies, Paper 13-201, Heising C.R., Colombo E., Janssen A.L.J., Maaskola J.E., Dialynas E., CIGRÉ, Paris, Aug 28 - Sept 3, 1994.

[13 ] Driftströningsstatistik – Fault Statistics 2003, Nordel (from www.nordel.org), 2003

(original in Swedish).

[14 ] SF6 Switchgear, Ryan H. M., Jones G. R., ISBN 0 86341 123 1, Peter Peregrinus Ltd.,

London, 1989.

[15 ] IEC 62271-100 high-voltage switchgear and controlgear – Part 100: High-voltage

alternating-current circuit-breakers, IEC, Geneva, May 2001.

33

[16 ] Cost Optimisation Versus Function and Reliability of HV AC Circuit-Breakers, Paper

13-101, Bosma A., Schreurs E., CIGRE, Paris, Aug 2000.

[17 ] A Novel Arcing Monitoring System for SF6 Circuit Breakers, Lehmann K., Zehnder L.,

Chapman M., Paper 13-301, CIGRÉ, 2002, Paris aug. 2002.

[18 ] Stress of HV Circuit-Breakers During Operation in the Networks - German Utilities

Experience, Neumann C., Balzer G., Becker J., Meister R., Rees V., Sölver C-E., Paper

13-304, CIGRÉ, Paris, Aug. 2002.

[19 ] IEC 60694 – Common specifications for high-voltage switchgear and controlgear

standards, Edition 2.2, IEC, Geneva, January, 2002.

[20 ] Condition Monitoring and Maintenance Strategies for High-Voltage Circuit-Breakers,

Bosma A., Thomas R., Proceedings of 6th International Conference on Advances in Power

System Control, Operation and Management, APSCOM 2003, Hong Kong, November,

2003.

[21 ] IEC 60050-191 International Electrotechnical Vocabulary – Part 191, IEC, IEC,

website: std.iec.ch

[22 ] Measurement of life and life extension: A Utility View by National Grid UK. CIGRÉ

Preferrential Subject 3 – Paper 13-302, Reid J., Bryan, U., CIGRÉ, Paris, 2002.

[23 ] A procedure for allocating limited resources to circuit breaker planned maintenance,

Higgins A., Corbett ., Kelleher C (ESB Ireland)., CIGRÉ Paper 13-303, CIGRÉ, Paris, 2002.

End of Report.

RCM Applications for Generator Systems

Nathaniel TaylorKTH, Stockholm

[email protected]

2005 08 22

Introduction

This report considers high-voltage (HV) rotating machines, i.e. large generators and mo-tors, and in particular their insulation systems. In relation to the application of AssetManagement (AM), a description is given of the past and modern construction of suchmachines and of current methods of condition assessment.

Most electricity is, at present, supplied from large generators of tens, hundreds andeven over a thousand MegaWatts (MW) each. When dealing with such large powers fromsingle machines it is clear that failure of a generator is highly undesirable to the powersystem controllers as well as being very expensive in outage time as well as repairs to thegenerator’s owner. Exactly how important the reliability is does of course vary to someextent other than just by machine power rating, since countries and contracts vary intheir penalisation of lost supply.

Motors of such high power ratings as the large generators are not used, but motorsrated in the tens of MW up to rather more than 100MW exist in some industries. Suchhigh-power motors are generally machines of the same type as generators—synchronousmachines—while lower power (several MW and less) motors are usually of the inductiontype. Motors can be claimed to have a greater variation of importance than generatorshave, from those whose failure has no worse effect than the need, eventually, to repair orreplace the motor, to those whose failure implies the halting of a far larger process systemor the creation of a dangerous state. Motors also have a wide range of duties, from near-constant applications with only occasional starting and stopping, to applications withfrequent hard stopping and starting and possibly rapid changes in load. The justificationfor on-line and off-line condition monitoring and maintenance therefore varies vastly, notso obviously dependent on the power rating of the machine.

In order to limit the scope of this short report to the area of interest to the author’swork, only synchronous machines of power-ratings of MW and more are considered here,and the stator insulation is the main focus.

HV machine construction

Overview of construction

Large synchronous AC machines consist of a cylindrical iron rotor moving within the boreof an iron stator. Both of these parts have electrical windings of insulated conductorsarranged to produce a magnetic field linking the rotor and stator.

The stator winding is formed by conductors within slots in the surface that faces therotor. In these windings the working power of the machine is generated or dissipated.

The rotor winding is of similar slotted construction in high-speed machines, or haswindings around protruding pole-pieces on lower speed machines. The rotor conductorsare excited by a DC supply, and have a maximum voltage to ground of only a few kilo-Volts (kV) even on large machines.

The number of magnetic pole-pairs around the stator or rotor is an important featureof a machine: this determines the ratio of the (fixed) electrical supply frequency to the

1

frequency of mechanical rotation. Gearing of the huge powers involved in electrical gen-eration is hugely impractical compared to adapting the electrical machine, by adjustingthe number of pole-pairs, to interface different angular speeds of electrical system andmechanical power-source.

Hydro-turbines move at quite low speeds and their attached generators therefore havemany pairs of poles and rotate at low speeds of a few revolutions per second. To allowsuch a large number of pole-pairs to be accommodated the diameter of the machine isvery large. The rotors of such machines are of the “salient pole” type, with mushroom-shaped iron pole-pieces protruding from a central rotor core. The rotor windings are thenaround the sides of the pole-pieces.

Steam-turbines and gas-turbines operate at higher speeds, often sufficiently fast—3000rpm on a 50Hz system—for their attached generators to have just one pair of poles.The rotors are then of modest diameter and considerable length, and are of slotted cylin-drical construction, often called “round rotors”.

Through the power-range of interest here, a few MW to over 1000MW, there arevarious relevant differences in the design of windings, depending on power-rating, ageand the mechanical power source or load: the winding construction and cooling methodsare of particular interest.

Cooling

To remove heat from the windings and iron, several methods may be used. These fallinto two main groups: indirect cooling, which removes heat from conductors after it haspassed through the insulation, and direct cooling, which removes heat from by circulationof a fluid within the windings.

Indirect and Direct indirect cooling is used in three variants: open-ventilated indirect-cooled machines have air from the environment passed through the machine; recirculatedair or recirculated hydrogen cooling uses enclosed air or hydrogen circulated within themachine and cooled by a heat-exchanger with air or water on the other side. Whenusing recirculated gas, it is possible to pressurise the machine’s containment, improvingthe thermal (volumetric) capacity of the gas. Hydrogen has a particularly good thermalcapacity.

Direct cooling uses either purified water or hydrogen, circulated either through hollowconductors or through separate ducts alongside the conductors. This is used as a supple-ment rather than as an alternative to indirect cooling, for cases where the advantages ofbetter cooling outweigh the considerable cost and complexity of the inclusion of ducts,connection of ducts in the end-winding region, and external apparatus for treating andcooling the fluid.

Electrical consequences of cooling systems Use of hydrogen or greater than atmo-spheric pressure has further positive effects on the electrical insulation. The breakdownstrength of the higher-pressure gas is greater than for atmospheric air, resulting in re-duced risk of PD in the slots and end-windings. The absence of oxygen means thatoxidative thermal deterioration of the insulation is reduced, which is relevant both tothe deterioration due to operating temperature and to that due to PD. Ozone detection

2

systems, mentioned later, will not be of use in hydrogen-cooled machines since there isnot significant oxygen present to form the ozone.

Applications of cooling Only small machines use open-ventilated indirect cooling.Modern turbo-generators up to a few hundred MW are indirectly-cooled by recircu-lated air, and larger ones by recirculated hydrogen, while before the 1990s cooling byrecirculated hydrogen was sometimes used even at such low ratings as 50MW. Directwater-cooling is used in hydro-generators at ratings above about 500MW. Modern turbo-generators have direct hydrogen or water cooling of stator-windings at ratings aboveabout 200MW, though older designs (1950s) use direct cooling of machines as small as100MW.

Stator insulation requirements

A high-power machine must have a large product of stator-terminal current and volt-age. Making either of these quantities large is however undesirable since high voltageputs greater demands on the insulation while high currents necessitate thick conductors(bending problem, skin effect, eddy currrent losses) or many parallel connections (circu-lating currents, complex connections in the end-windings) and makes harder the matterof transferring current from the terminals (losses, magnetic forces). The compromiseposition chosen as optimal, changing little over the years, is that the voltage used in thestator is up to around 30kV line-voltage on the largest ratings, and is indeed about halfthis value even for very much lower powers of tens of MW.

A particularly strong constraint imposed by machine design is that every bit of spacearound the conductor area is very valuable: all electromagnetic machines are a com-promise between “iron” and “copper” (magnetic and electrical conductors) and to theelectromagnetic designer any insulation is just an unfortunate necessity that must be keptinto as small a cross-sectional area as possible.

The windings operate at high temperature, due to the need to use space efficientlyand therefore to economise on the area of conductors. The conductor temperature ofindirectly-cooled windings is determined by the insulation’s thermal properties, withthinner and more thermally conductive insulation being desirable. The temperature alsoaffects the insulation’s deterioration, since for an air-cooled machine much of the agingof the organic insulation material is by a reaction with oxygen.

Coil or bar stator windings

Machines of more than about 1kV (which implies a maximum practical power of hundredsof kW) always have “form-wound” stators: the windings are carefully prepared withconductors and insulation before being inserted in the stator.

Up to about 50MW a “coil-type” form-winding is used, in which a multiturn loopof conductors with insulation is prefabricated ready to be inserted so that the two longparallel sections (legs) fit into stator slots and the remainder protrudes from the ends ofthe stator. This is quite simple to construct as one end of the stator then has no explicitconnections needing to be made; the connections are just continuations of the conductors.

3

For larger ratings it is impracticable to fit so thick and rigid a coil into the stator, andthe windings are fabricated in single bars for insertion in the stator: this is a “bar-type”or “Roebels-bar” construction. It is then necessary at each end to make connectionsbetween individual conductors, which is complicated still further if channels for directcooling are present.

Stator conductors

Within a coil or bar, there are separate conductors (ten, as a very rough idea!). A high-power machine has high currents, and if a single copper wire were used for the winding itsarea would be so great as to cause unacceptable skin effect and eddy current losses andto make it insufficiently flexible. Several smaller conductors, the “strands” are thereforeconnected in parallel to form a larger conductor.

The strands in a bar-type winding are arranged to change position regularly (trans-position) in order to minimise differences in the induced voltages in spite of differentmagnetic conditions in different parts of the slot. Strands in a coil-type winding usuallydo not need this transposition as the smaller machine size results in less distance betweendifferent strands; an inverted turn at the end-winding can be used to swap inner andouter parts between the two slots that a coil occupies. “Turns” of parallel strands arethen connected in series to form whole coils and ultimately whole windings consisting ofmany coils.

The set of conductors and insulation that is put in each stator slot is conventionallyof rectangular cross-section with rounded edges, constrained by cross-section demandsof the iron between the slots and by the practicalities of making the winding. Somerecent development has been made of fundamentally different insulation systems, based oncircular-section conductors of cable-insulation design, but the conventional constructionis still far more common. The corners of conventional bars result in a non-uniform electricfield in and around the insulation and therefore in some over- or under-stressed regions.

Stator insulation geometry

A three-part insulation system used for form-wound stators has a thin strand-insulationable to withstand the high temperatures and low voltages (∼10V) between strands, thena turn-insulation able to withstand the induced voltage of a loop through the stator. Atpoints where the strands have a transposition, extra insulation is often needed to fill thegaps. Since the 1970s many manufacturers have used a strengthened strand insulationto obviate the need for turn insulation.

To insulate the turn insulation from the stator-core’s ground potential a further layer,the “groundwall” insulation, is used. Although the groundwall thickness could be var-ied along a winding from a small amount at the neutral point to the necessarily greaterthickness near the phase terminal, this is avoided for simplicity of geometry; a trick fa-cilitated by this is the reversal of the connection of a windings so that the previouslyhigher-stressed parts of groundwall are stressed less than the other end, so prolongingthe insulation’s life. For machines operating at more than 6kV the groundwall insulation

4

is covered with a semiconductive (low conductivity, 0.1-10kΩ/sq) compound, usuallywith carbon-black as the conductive part. This is sufficiently conductive to ensure thatcavities between the bar and the stator will not be exposed to high enough electric fieldsto cause partial discharges (PDs) but sufficiently resistive that the laminations of thestator iron will not be shorted out.

The end-winding region is the part of the winding outside the stator-core, where thewindings through the stator are connected to each other to form loops. With the zero-potential surrounding of the stator core removed, the outer surface of the insulation hasa potential due to the conductors inside. In the region near the stator the electric fieldin the surrounding medium (air or hydrogen) is particularly high due to the electric fieldfrom the closest part of the end-winding concentrating at the ground potential. This maylead to PDs on the surface, with damaging effect.

As the end-winding of large machines is a mass of connections and is therefore sensitivefrom an insulation perspective, it is preferred that the end-windings should be allowed tobe remote from ground potential; coating the end-windings with a quite well-conductingmaterial is therefore not desired (and induced voltage would also need to be allowed forin this case). Instead, a semi-conducting coating with high resistivity, usually includingSilicon Carbide (SiC) to give it a non-linear current/voltage-gradient relation, is appliedfor several centimetres from the end of the winding’s outer semiconducting coating ofrelatively low resistivity. The surface potential then falls off quite smoothly as the statoris approached from the end-windings, and surface discharges are avoided. The gradingand semiconductor materials are applied as paint or, in more modern designs, as bands.

Insulation materials

From early beginnings of organic cloths with oils or resins on them, stator insulation nowrelies on mica flakes surrounded by a polymeric binding. Mica is a mineral that is veryinert and is favoured for many high-temperature applications. It is also very enduringof partial discharges. Windings are coated with a paper of mica flakes and then, outsidethe stator or else all in situ, an epoxy resin filler is added to impregnate the flakes.In older machines still widely used, bitumen was used instead of epoxy, giving higherconductivities (due to water absorbtion), the risk of softening or even running out of thewindings at high temperature, and easier formation of voids that would allow PD.

Sources of faults

The parts that are most susceptible to aging and consequent failure are the insulationsystems of the two windings, and the bearings that support the rotor. The bearings oflarge or critical machines are monitored on-line for vibration and temperature, and evenquite small machines of a few MW often have regular scheduled measurements. Bearingproblems can even arise due to winding problems if an asymmetry in the magnetic fieldis caused by for example turn-shorts.

Which insulation system—stator or rotor—is more prone to problems depends on thetype of machine. A survey of hydro-generators cited in [1] in North America has suggestedaround 40% of outage-causing faults to be due to stator insulation, and fewer to the rotor

5

(the remainder are mainly mechanical). In [1] it is claimed as the (illustrious) authors’opinion that turbo-generators generally tend to the contrary, with rotor insulation beingthe greater source of outages, but it is stated that there is no wide-spread detailed surveyof such machines to the extent performed with hydro-generators. [2] gives some figuresfor North American turbo-generators, of air- or H2-cooled designs: the generator is seenas the least troublesome of the main power-plant causing around 0.5 occurences per year;the generator’s HV insulation gives more faults than other components on the furtheranalysis within the generator.

The rotor insulation has lower voltages to insulate, since 1kV (DC) is about the max-imum normal excitation voltage, but has less direct cooling in the larger machines andhas to operate with large forces on it due to the rotation. The thermal conditions underhighly excited conditions can also be very severe.

On-line electrical monitoring of rotor insulation is impractical with many designs ofexcitation system (rotating exciter or DC machine), since there is not direct electrical con-nection from the stationary part of the machine to the rotor winding. A short-circuitedturn in the rotor only results in a less effective field, in contrast to the case of the statorwhere the alternating magnetic field would cause a huge current to flow in that turn; therotor turn insulation is therefore not as critical a candidate for continual monitoring. Arotor ground-fault can also be mitigated by systems that use an excitation source that isnot solidly grounded, i.e. a single ground fault can be tolerated, although usually this istaken as a severe state requiring prompt action.

From here on, just the stator insulation will be considered.

Stator insulation failure mechanisms

Thermal deterioration of the organic component of the insulation, particularly in air-cooled machines, is a continual process, often simple modelled with an Arrhenius-ratereaction, i.e. exponential relation of rate to temperature above a minimum value. Thedeterioration leads to more brittle insulation material, which in turn can increase theeffect of vibrational aging.

Thermal cycling may cause stresses and movement of whole bars (complete withgroundwall insulation) axially relative to their slot, or for quick changes in load or expoy-mica bars the forces of bar against groundwall insulation may cause internal movementin the bar. The windings are the main heat-source when at high load, so warm upfaster than the surrounding iron and reach a much higher temperature besides havingdifferent coefficients of thermal expansion. Mechanical stress and/or movement occursduring changes of load, which should be make slow and infrequent in order to minimisethe effect of thermal-cycling.

There are large forces between conductors even with the currents of normal operation;separate conductors within a bar, separate bars within a slot, and nearby connections inthe end-winding region all experience forces that alternate at twice the power frequencyand that can damage the insulation in regions where there is looseness that allows move-

6

ment. During a short circuit the forces can be many times greater, possibly causinginternal damage that initiates longer-term degradation; the bars must be very firmlyheld in place particularly in the end-winding regions.

The semi-conducting coating of the stator bars may wear out, due to the chafingfrom vibration in the slot and to thermal expansion or to arcing due to lamination short-circuits. The end-winding stress-grading materials may also become less effective withtime, leading to surface PD that will only worsen the state of the end-windings. Band-applied grading is found to be more durable than paints. Stress between windings in theend-winding region, due to bad design or to movement, can also cause PDs. In all thesecases of PDs against the outside of the stator insulation, wear will be caused, possiblyleading to a proper breakdown.

Condition Assessment

Off-line tests and measurements

Off-line tests were used even with the earliest machines, before the technology and de-mands for availability caused on-line monitoring to be feasible. Off-line tests are still usedat maintenance times, and allow measurements to be made that give possibly better orjust complementary information to that of on-line measurements. Many popular off-linetests are very simple, and application of more sophisticated tests, while of great interestif able to improve diagnoses, have the problem of how their more detailed informationshould be interpreted to determine the relevant condition details.

Manual and visual inspection of the machine, possibly requiring disassembly of someparts, can detect looseness of bars in slots, wear and residues on surfaces due to repeatedPD, signs of overheating and more. The need for internal access rather than, for ex-ample, electrical measurements on the terminals or from the end-windings does makedirect-inspection methods less convenient in cases where the maintenance is not other-wise requiring disassembly. The risk of making worse the state of a working machine bymovement of parts or an oversight during disassembly or reassembly should be borne inmind. It is claimed in [1] that when conducted by an expert such inspection is the bestform; but this surely neglects voids causing PD well within the insulation layers.

Smooth (non-pulsed) currents When a voltage is applied across insulation, currentmay flow for various reasons. There is some current that flows during changes of voltage,charging the fundamental “free-space” capacitance of the insulation; a material insulationwill then undergo some polarisation of molecules or alignment of already polar molecules,resulting in a weakened field and consequently more current flowing while the polarisationis happening. This polarisation current is of interest for showing changes in the chemicalstructure of the insulation, or the ingress of moisture (water is highly polar).

Some current may flow due to pure conduction in the insulation material, mainly inwet, old bituminous insulation. Modern expoy-mica insulation that has not been abusedby exposure directly to water for a long time has negligible conductivity. This is a currentthat continues even with constant voltage, rather than falling away as the capacitivecurrent does. Surfaces of insulation often have enough contamination to make them far

7

more conducting than the insulation’s bulk. This adds to the conduction current.Even PDs can be measured by smooth-current methods, which are particularly suit-

able for the case of many small PDs, as often found in a stator winding.Methods of measuring smooth-current effects cover a wide range of complexity. Insu-

lation Resistance (IR) tests just measure the DC current flowing after the application of avoltage for a time that allows fast polarisation to have been completed. The PolarisationIndex (PI) is similar but uses a ratio of currents at two different times to prevent thevery high sensitivity of measured resistance to temperature from having so powerful aneffect on results wanted for comparison of machines or of a machine over time.

Measurements, e.g. by a Schering bridge, of effective resistance and capacitance of awinding at a particular voltage and frequency are widely used, with the ratio of dielectricloss to capacitance—the loss tangent or tan δ—being an important value. Taking suchmeasurements with varied voltage and observing any trend in loss or capacitance can beuseful, particularly for the detection of PD inception (the tan δ tip-up test).

More detailed information about insulation can be found from Dielectric Spectroscopy(DS): the change in current over time is measured for a stepped voltage (time-domainmethod) or the magnitude and phase of the fundamental current and possibly someharmonics too are measured at several different frequencies and amplitudes of the appliedvoltage (frequency-domain).

As opposed to the above methods, all of which can be applied at modest voltages eventhough use of voltages around the rated value may be desirable in some cases, the “hi-pot” (high potential) tests are more endurance tests for the insulation than diagnosticmethods. A DC or AC source drives the coils with a potential much higher than therated one, often increased in steps. If a certain over-voltage level (2V + 1, with V beingrated voltage in kV) is withstood than the winding passes the test. If the voltage is notwithstood, the winding may need to be repaired even though it may have survived for along time in normal operation.

Pulses (PD) Generator insulation is much more tolerant of PD than is the insulationof most other HV apparatus. Some PD activity is acceptable in long-term service, butin tests it may be useful to know some measure of the PD in order to distinguish moreand less harmful or expected locations and concentrations of PD activity.

There are several ways of detecting PD, some of them very crude. Sometimes PDactivity is perceptible by sight (the “blackout test”, very useful for localisation) or bysound. Radio-frequency detectors may be used with a probe to locate regions of PDby the signal transmitted through the air. A machine that can only be accessed at itsterminals is more suited to electrical methods, with the current pulses into the wholewinding being measured.

On-line PD measurement has an advantage of measuring PD in real operating condi-tions of voltage and temperature. If wider knowledge of the winding condition is desiredthan just this matter of “is there currently PD or not PD at working conditions”, thenmodern phase-resolved PD detection systems, possibly together with varied amplitude orfrequency of the applied voltage, may be used.

8

On-line monitoring

On-line monitoring is clearly highly desirable if its results can be used to keep a machinerunning for longer without shut-down while still detecting problems before they causesevere damage. Some widely used methods particularly relevant to stator insulation aredescribed below.

Thermal monitoring of the stator windings is used for practically all machines of thesizes under discussion here. Temperature sensors are included between bars or to measurethe temperature of a cooling fluid. A large machine may have many sensors and havemaintenance personnel make more use of trends over short and long periods than in thecase of a less important machine where temperature sensing is mainly for a short-termwarning of severe malfunction.

Condition monitoring, or Generator Condition Monitoring (GCM), is the detection ofchemical products of hot insulation material, i.e. a sophisticated smoke detection systemrelying on the removal of ions from a chamber by their binding to smoke particles. Insome machines a tracer paint may be used on critical areas, to release easily detectablechemicals at a well-defined temperature. Use of different paints in different parts of themachine allows still better location of a problem. An extreme GCM reading can be usedas a warning to operations staff, but in most cases the GCM is of interest to maintenancestaff; a long-term analysis is useful to spot problems such as the sporadic burning due toshorted laminations burning insulation in small areas.

Ozone (O3) monitoring of enclosed (recirculated cooling) machines may be acheivedby a sensor inside the machine enclosure, for continuous monitoring. An open-ventilatedmachine may instead have ozone measurement by simple manual exposure of a test chem-ical that reacts with ozone. Ozone is produced by electrical discharges, so ozone detectionmethods are sensitive only to PDs that are not internal to the insulation, i.e. they may beslot discharges between the groundwall insulation and the stator core due to deteriorationof the semiconducting layer or to loose bars, or may be discharges in the end-windingsdue to wearing of the stress-grading semiconductive layer or to excessive proximity inthe end region. Single PD sites are unlikely to produce measurable quantities of ozone,and general wear in poorly represented areas, e.g. the stress-grading of the end-windingrather than the semiconductive layer all along the slots, will show only weakly even if thelocalised activity is strong. Trends, even over a long time, are important as with GCM, ifthe measurements are to be usefully interpreted for phenomena other than severe declinesin condition.

Electrical monitoring of Partial Discharges online has the PD measurements made atrealistic (actual!) operating conditions. PD is very sensitive to gas pressure, temperatureand cavity size., so to compare PD measurements throughout a machine’s working timerequires matching of these conditions quite closely if trending is be be used.

Although not a means of measuring the current state of the insulation, on-line volt-age surge monitoring may be useful in recording times when the external network has

9

introduced a transient voltage to the machine terminals. Since the inductive coils are ahigh impedance to high frequency signals there is a large voltage drop over the first turnsin the winding, which may even cause a breakdown in the turn insulation, causing it tobe weakened.

Condition Assessment Practices

Some on-line monitoring is used on almost all machines under consideration here, and onall those over a few tens of MW; this is, for example, temperature monitoring. On-linePD monitoring has been popular, with inductive capacitive or RF pickups being used invarious systems to get more or less precise location of PD.

It is credible that deregulation, with its concomitant high demands on reliability andoptimisation of assetts, has encouraged further use of condition monitoring, but it shouldalso be noted that modern developments in data acquisition (sensitive automatic elec-trometers) and processing (modern computers and software) have made the extractionfrom the machine of meaningful reliability information much improved.

Choice and application of formalised AM methods such as RCM have grown markedlyin the power-industry owners of power-station generators since the 1990s. Although vary-ing considerably depending on the analyst, generators have been singled out as significantsources of failures of the whole power-station system.

The Swedish generating utility, Vattenfall, has implemented RCM methods in itsmaintenance program, including some . Reference [3] gives an description of variationsof RCM applied to generating plant. [4] describes briefly some RCM approaches andconsiders the RCM application by Vattenfall to generator systems.

10

References

[1] G. C. Stone, A. Boulter, I. Culbert, and H. Dhirani. Electrical insulation for rotatingmachines. IEEE Press series on Power Engineering. IEEE Press, 2004.

[2] H. J. van Breen, E. Gulski, and J. J. Smit. Several aspects of stator insulationcondition based maintenance. In Conference record of the 2004 IEEE internationsymposium on electrical insulation, Indianapolis, IN, USA, pages 446–449.

[3] Fredrik Backlund. Managing the Introduction of Reliability-Centred Maintenance,RCM. RCM as a Method of Working within Hydropower Organisations. PhD thesis,Lulea University of Technology, Sweden, 2003.

[4] Otto Wilhelmsson. Evaluation of the introduction of RCM for hydro power generatorat Vattenfall Vattenkraft. Master’s thesis, Electrical Engineering, KTH, Stockholm,2005.

[5] G. C. Stone. Advancements during the past quarter century in on-line monitoring ofmotor and generator winding insulation. Dielectrics and Electrical Insulation, IEEETransactions on, 9(5):746–751, October 2002.

[6] G. C. Stone. Recent important changes in IEEE motor and generator winding insu-lation diagnostic testing standards. Industry Applications, IEEE Transactions on,41(1):91–100, January-February 2005.

[7] T. Bertheau, M. Hoof, and T. Laird. Permanent on-line partial discharge monitoringas strategic concept to condition based diagnosis and maintenance. In ElectricalInsulation Conference and Electrical Manufacturing and Coil Winding Conference,1999. Proceedings, pages 201–203, October 1999.

[8] A. Kheirmand, M. Leijon, and S. M. Gubanski. Advances in online monitoring andlocalization of partial discharges in large rotating machines. Energy Conversion,IEEE Transactions on, 19(1):53–59, March 2004.

[9] V. Warren. Using on line insulation testing to implement generator predictive main-tenance. Electrical Insulation Magazine, IEEE, 13(2):17–21, March-April. 1997.

[10] IEEE Trial-use guide to the measurement of partial discharges in rotating machinery,August 2000. IEEE Std 1434.

[11] G. C. Stone and J. Kapler. Condition-based maintenance for the electrical windingsof large motors and generators. In Pulp and Paper Industry Technical Conference,1997, pages 57–63, June 1997.

11

[12] D. G. Edwards. Planned maintenance of high voltage rotating machine insulationbased upon information derived from on-line discharge measurements. In Interna-tional Conference on Life Management of Power Plants, 1994, pages 12–14, Dec1994.

[13] T. Laird. An economic strategy for turbine generator condition based maintenance.In IEEE International Symposium on Electrical Insulation, 2004, pages 440–445,2004.

[14] J. L. Kohler, J. Sottile, and F. C. Trutt. Condition-based maintenance of electricalmachines. In Industry Applications Conference, 1999. Thirty-Fourth IAS AnnualMeeting, volume 1, pages 205–211, October 1999.

12

1

CONDITION MONITORING OF WOODEN POLES

Osmo Auvinen VTT Processes

email: [email protected] 1. INTRODUCTION The wood poles are one of the most remarkable asset in the electricity networks. The number of wood poles in Finland is about 8 million, including the transmission, distribution and telecommunication network poles. A great share of these poles is approaching the technical end of their life-time. This creates a challenge: because the number of pole to be replaced is so great, what is the optimal way to handle this problem? The first task in this optimisation is to survey the remaining life-time of the poles and what’s the present condition of the poles? The age distribution of installed poles in Finnish utility is shown in the figure 1.

nu

mb

er o

f in

stal

led

po

les

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

construction years

The age distribution of installed poles in Finland

Figure 1. Age distribution of installed poles in Finnish utility There are developed several methods for this wood pole condition monitoring, starting from very simple tools to high-tech ultrasonic detectors. The main problem with the assessment of condition of wood poles is that every single wood pole decays with individual rate, so that to clarify the whole mass, every pole has to be monitored and this needs time and money. Damaging of wood pole is caused mainly by decay fungi, insects and woodpeckers. Structural damages decrease quite quickly residual strength of a wood pole. In the Nordic countries the most serious problem in using wood poles is decay, while usually insects are not even considered as a problem. In addition, some woodpecker damages may occur.

2

2. WOOD POLE PRESERVATIVES In Finland, mainly two preservatives are used, creosote and CCA (chromated copper arsenate). Creosote is the oldest preservative in general use, and it is highly effective providing a long service life. It has also been improved to satisfy the latest demands of effectiveness and bleeding. Thus, the quality of treated poles is currently quite even, and bleeding has reduced without any loss of service life. Characteristic for the creosoted poles is that it will decay internally. This lead to the tube structure where the pole seems to be in shape, but it have been lost most of its strength. However, there could be left enough strength for climbing, but it is very hard to measure from outside. Usually, this kind of pole will break down with noise, which might warns a lineman so that it is possible for him to climb down before final break down. Most poles used in Finland are preserved with CCA (chromated copper arsenate) type C, which has considerably reduced leachability compared to type B. CCA is a highly effective wood preservative used to treat poles of pines. Nowadays about 95 % of the poles in use are CCA-treated. The external decaying (in CCA poles) leads to the situation where the diameter of the pole is reducing. This phenomenon is, in most cases, clearly visible and climbing can be forbidden if the diameter is too narrow. The machine tooling (like drilling) of the poles is better to do before the impregnation whenever it is possible. Then the treatment material fills all the places. The holes done afterward will be the weak point in the protection of poles and it can accelerate the decaying of the pole. Also, a pole top cover should always be used because it provides extra protection for topmost parts of the pole. This decaying of pole top can be dangerous if the cross arm will fall from the pole and cause damages and maybe hazardous situation due to decay. Since the preservation has been improved greatly after 1950’s, the expected average service life of CCA type C treated pole is 35 to 50 years, and 40 to 55 years with creosoted poles. 3. DECAYING OF WOOD POLE Fungi are long, microscopic, tube-like plant cells that feed off plant materials with external digestion (i.e. outside of the fungal cells). The fungus penetrates and colonizes its food, digests and absorbs the soluble fragments, and metabolizes them internally as an energy source. Fungi reproduce by cell fragmentation or the formation of spores. When favourable conditions, as follows, occur, fungi rapidly germinate and begin decay. In order to develop, fungi has certain requirements as follows:

− Oxygen − Water (moisture content (MC) of wood exceeds 20%) − Temperature (+5 .. +30°C) − Food

Lack of air, overdry wood (MC below 20%) and freezing temperatures only limits or stop fungal growth, which continues when conditions returns suitable. The most favorable conditions for fungi are MC of wood between 25 and 50% and temperature between +20 and

3

+30°C, i.e. the ideal location for decay is 50 mm to 450 mm below groundline. On the other hand, most fungi are killed at temperatures exceeding +65°C /Ng/. On of the most important procedures in the pre-treatment handling of pole is drying. The idea of drying is that fungi needs moisture in the wood cells and if this moisture is below the fibre-saturation point, fungi cannot develop. And when the moisture content increases, the amount of oxygen becomes limiting parameter: water has only few ppm free oxygen and this is not enough for fungi. That’s why wood material in the bottom of lake etc. will remain centuries. Optimal moisture content for fungi has found to be around 40 … 80 %. Fungi demands food that fills three requirements: energy from carbon compounds, metabolic storage for development and suitable vitamins, CO2 and nitrogen. Also pH-level should be around 3 …6 for active development of fungi /Zab/. There are great number of parameters which affect to the development of fungi. Main parameters are location of pole (i.e. immediate surrounding, like forest or field), type of soil (dry or wet), wood quality, preservative and preservation quality. Surrounding of a wood pole has significant impact on the rate of decay. In forest development of decay is found to be slower than in field. Also, type of soil affects on how rapidly decay develops. Other parameters are found to be very significant, but they are also more or less individual, so this makes the exact forecasting of remaining life-time impossible to a single pole. /Gray/ There are three types of wood decay: white rot, brown rot, and soft rot. All these rots affect differently. White rot is caused by fungi that consume all of the wood cell walls, leaving behind residual wood characterized by a white punky. Fungi causing brown rot attacks only a portion of the wood cell walls, leaving soft, brown residual wood characterized by shrinkage cracks in a cubical pattern. Fungi causing soft rot attacks to the inner portion of the wood cell wall, forming diamond-shaped holes. Soft rot fungi require less oxygen than other fungi types, and therefore it develops specially in wet conditions. By starting to extend from the shell of a pole, substantially reduces the pole’s strength. Brown rot is the most rapid and damaging to wood, soft rot is almost as rapid as brown rot, and white rot is intermediate in its rate of decay /Ng/. The ideal conditions for fungi are the moisture content of wood 25 … 50 % and temperature +20 … +30 ºC. Ideal location for decay is 50 mm … 450 mm below groundline where the combination of moisture and oxygen is optimum. 4. WOOD POLE INSPECTION METHODS Most of the inspection methods are based on the experience of the inspector, except some modern technologies. The methods can be divided to two groups: methods for decay inspection and methods for strength assessment. The first group consists, at least, of eight different techniques, like:

− visual inspection − sounding / hammering − drilling − excavation − sonic devices − x-ray and NMR techniques

4

− decay detecting drill − electronic resistance instruments and − Pilodyn wood tester

Visual inspection is done at first above ground zone of the pole to detect possible damages. If damage is considerable and it is located at areas critical to the strength of the pole (e.g. in the root of the pole in straight line poles and in the middle of the pole in A- and stayed poles), inspector should note that damage on the record so that proper maintenance action can be done in time. Visual inspection is adequate, if the pole has been in service less than 20 years; otherwise next phases are carried out.

Figure 2. Visual inspection of the wood pole

Sounding (or hammering) determines the in-pole damages, like inner decay. The location of decay can also be indicated quite sharply with this technique. Groundline zone inspection starts with removing the soil around a pole to a depth of 20 to 40 cm. Subsequently, a pole is hammered to detect the type of decay. Soft surface of a pole is an indication of external decay. Correspondingly, internal decay can be noticed by listening to the sound of hammering, i.e. special sound indicates internal decay. After detecting the type of defect and its location, the depth of decayed wood is measured with pick test in any case and also with increment borer if internal decay is detected.

5

Figure 3. Hammering test

Drilling gives even more exact result of possible inner damages of the pole. Drilling is done by radial boring to the pole to take a test sample. Increment cores are taken from that part which is regarded most severely decayed. The thickness of pole’s surface is determined with extracted core, or with shell-thickness indicator. Proper actions are also defined with a sound diameter and a rate of decay.

Figure 4. Increment borer and a sample

Excavation is one of the most utilised techniques. It is used to detect decay below the ground line. The test sample is taken from the pole by pick test. Pick is pushed abeam of the pole into

6

wood, and a small sliver of wood is lifted. If sliver splinters, the wood is sound, whereas abruptly breaking sliver indicates rot. Test is carried on as deep as sound wood is indicated. Same test is also done on the other side of the pole, or if needed, around the pole. After removing the defected wood, the diameter of the sound pole is measured, and proper actions are defined depending on an evaluated rate of decay and a sound diameter of the inspected pole.

Figure 5. Pick tests of the wood pole.

Left: Abruptly breaking sliver indicates decay

Right: The sliver splinter when the wood is sound

X-ray and NMR techniques are base of radiation, x-rays and nuclear magnetic resonance (NMR). These techniques give 2D- or 3D-mapping of the pole and its strength. Disadvantage for this technique is that it is for laboratory tests only, not for field services. Also gamma radiation has been utilised when assessing the deterioration (the density of the wood decreases when it decays) Decay drilling technique is based on the same idea than in the conventional drilling, but this technique utilises special equipments for this purpose. The drill penetrates to the wood and measures the drilling resistance. The drill resistance depend on the soft or hard wood. Electronic resistance instruments measures the resistance over the test sample of pole. Negative ions are released by decayed wood and this lowers the electrical resistance. The resistance is <25 % of sound wood indicates decay. Sonic devices are based on the speed of sound in a material. This technique utilises the stress wave velocity. In soft, decayed wood the stress wave propagates lower speed. Reliability if this technique is quite low to exactly detect the amount of decay. Several equipments for sonic based analysis are available nowadays and two of these are introduced briefly as follows. De-K-Tector is a measurement device, where the pole is knocked with ball-headed hammer. The sensor at the opposite side of the pole registers the wave generated by this hammer. If the sensor investigates more small-frequency (about 160 … 600 Hz) waves than high-frequency

7

(1 kHz … 5,2 kHz) waves, interprets the measuring device this part of pole as weakened. And vice versa, if there are more high-frequency than small-frequency waves, the pole is investigated to be sound /Far/. PURL (Portable Ultrasound Rot Locator) consists of ultrasonic transmitter and sensor. These equipments measure the sound volume across the pole diameter and this value is compared to the set value. If the volume of the measurement is higher than set value, the pole is sound and if the volume is lower, the pole is decayed. This PURL-technique is quite laborious; only about 20 poles can be measured with this technique in a day /Far/. With strength assessment methods, there are great variations in the fibre stress value (gap is about three times between the species and class). The ANSI O5.1 –standard defines the required pole strenghtness, but only minor percent of the poles really pass this standard. There are different techniques for the pole strength assessment, like:

− a sonic technique, where digital waveforms collected from the ground line of the pole are compared to the strength model

− pendulum impactor, where the stress wave propagates along the pole − Polux-method, where the wood density and moisture are measured by driving two

electrodes into the pole − calculating the stiffness property (MOE) against breaking strength (MOR): MOE –

MOR –relationship lowers when pole become more decayed. Other mechanic inspection equipment is so called Pilodyn wood tester, where the device is loaded with the ramrod and then pressed firmly on to the test surface. The impact pin is shot into the wood by pressing the trigger cover. The depth of penetration can be read immediately on the scale mounted on the tester. The wood pole inspection process can be introduced as a flow chart also. Example of this flow chart is shown in figure 6

8

Visual inspection

Hammeringup to 2 m

height

Poleinspection

End ofinspection

Incrementborer

Locate the softiest spot of the pole by hammering

Pole > 20years old ?

No furtherinpection

Dig the soil down to 20- 40 cm and measure the groundline diameter

Dull thudsound ?

NO

YES

YES

NO

Estimate the rate of decay and fill the inspection

record

Pick test

Figure 6. Flow chart from wood pole inspection

4. OVERHEAD LINE INSPECTION Normally, when wood poles are inspected, the whole overhead power line is also inspected. Wood poles are the main target, but also bars, insulators and conductors are inspected, in most cases visually. These other structures are inspected in a case of damages. Damaged insulators are found to cause interruptions for customers and even top pole fires in UK. Damaged or failed insulator location techniques have been considered in the past and have relied on different parameters such as partial discharge, impedance, leakage current and voltage imbalance in addition to visual inspection. Conductors are generally inspected for signs of damages stranding, evidence of clashing, bird caging or fatigue during the inspection process

9

/Hor/. Bars have to be also examined in a case of possible breakage or other damage, which can cause danger to environment or blackout. SOURCES /Zab/ Zabel, Robert, Morrel, Jaffrey. Wood microbiology: Decay and its prevention. Academic Press Inc. 1992. ISBN 0-12-775210-2. 16 p. /Hor/ Horsman, Steve. Condition and design assessment of wood pole overhead lines.

Improved Reliability of Woodpole Overhead Lines (Ref. No. 2000/031), IEE Seminar on 8 March 2000. 8 p. /Gray/ Gray, Scarlett. Effect of soil type and moisture content on soft rot testing. IRG/WP/2270. The International Research Group on Wood Preservation. 20. April 1986. 26 p. /Ng/ Harry W. Ng. et al., Wood pole technology. Electric Power Research Institute, Electrical Systems Division, Publication BR-102525, Palo Alto, USA, 1993, 42 p. /Far/ Farin, Juho et al. Puupylväiden kunnonarviointi ja arvioinnin luotettavuus. Valtion teknillinen tutkimuskeskus. Raportti SÄH 7/93. 40 p.

MAINTENANCE AND CONDITION MONITORING ON LARGE POWER TRANSFORMERS

Pramod Bhusal (56980W) Lighting Laboratory (HUT)

[email protected]

1

CONTENTS

INTRODUCTION 2

TRANSFORMER CONDITION MONITORING ARCHITECTURE AND TECHNIQUES 3

MONITORING BY OIL ANALYSIS 4

WATER CONTENT OF PAPER/OIL SYSTEM 4 ROUTINE TEST OF OIL QUALITY 4 DISSOLVED GAS ANALYSIS 5

PARTIAL DISCHARGE MONITORING 5

TEMPERATURE MONITORING 8

VIBRATIONAL TECHNIQUE FOR MONITORING 8

LOAD TAPCHANGER MONITORING 9

CONCLUSION 10

REFERENCES 11

2

INTRODUCTION Large power transformers belong to the most valuable and important assets in electrical power systems. These devices are very expensive and therefore diagnosis and monitoring systems will be valuable for preventing damage to these transformers. Also an outage due to these transformers impacts the stability of the network and the associated financial penalties for the power utilities can be considerably high. So some ways has to be found to avoid sudden breakdown, minimize downtime, reduce maintenance cost and extend the lifetime of the transformer. Condition monitoring is the way helpful to avoid those circumstances and having the capabilities to provide useful information for utilizing the transformer in an optimal fashion. Condition monitoring can be defined as a technique or a process of monitoring the operating characteristics of machine in such a way that changes and trends of the monitored characteristics can be used to predict the need for maintenance before serious deterioration or breakdown occurs, and/or to estimate the machine’s “health”. It embraces the life mechanism of individual parts of or the while equipment, the application and development of special purpose equipment, the means of acquiring the data and the analysis of that data to predict the trends. [1] Before the wide use of Condition monitoring, time-based maintenance had been mainly used maintenance strategy for a long time. Time-based maintenance strategy involves the examination and repair of the machine offline either according to the time schedule or running hours. This strategy may prevent many failures but might also involve many unnecessary shutdowns and unexpected accident in the intervals. This will cause the unnecessary waste of money and time due to the blind maintenance without having much information about the condition of the machine. On the other hand, condition-monitoring lets the operators know more about the state of the machine and indicate clearly when and what maintenance is needed so that it can reduce the manpower consumption as well as guarantee that the running will never halt accidentally. The benefits of condition monitoring can be summarized as:

• Reduced maintenance costs • The results provide quality control feature • Limiting the probability of destructive failures, this leads to improvement in operator

safety and quality of supply • Limiting the severity of any damage incurred, elimination of consequential repair

activities, and identifying the root causes of failures • Information is provided on the transformer operating life, enabling business decisions

to be made either on plant refurbishment or on asset replacement. To be successful, condition monitoring must be self-sufficient and not require manual intervention or detailed analysis. It must be capable of detecting gradual or sudden deterioration and trends and have predictive capabilities to permit alarming in sufficient time to allow appropriate action to be taken and avoid major failure. It must be reliable and not reduce the integrity of the system, it must not require undue maintenance itself and must be a cost effective solution.

3

TRANSFORMER CONDITION MONITORING ARCHITECTURE AND TECHNIQUES The integrity of power transformer depends upon the condition of its major components and a weakness in any can lead ultimately to a major breakdown. The main components are the windings, insulation oil, core, bushing and on-load tap changers [2]. The operating temperature of the transformer has a major influence on the ageing of the insulation and the lifetime of the unit. Thermal impact leads not only long-term oil/paper-insulation degradation it is also a limiting factor for the transformer operation [3]. Therefore the knowledge of temperature, especially the hot spot temperature, is of high interest. The degradation of insulation system is accompanied by phenomenon of changing physical parameters or the behaviour of insulation systems. The degradation of insulation system is a complex physical process. Many parameters act at the same time thus making the interpretation extremely difficult. The monitoring and assessment of such components is vital to achieve better reliability of the system

Condition monitoring techniques can be off-line or on-line. Offline techniques can only be carried out during outages and some require complete isolation of the transformer. Frequency response analysis, power factor and capacitance testing and measurement of winding and insulation resistance, magnetizing currents and turns ratios are applicable to large or strategically important transformers. Post fault forensic tests also include paper analysis and metallurgical tests. Online techniques maybe by discrete tests or can be applied continuously and avoid the need of outages. Online, computer based, integrated, multisensor monitoring systems are now commercially available and in development. These online systems monitor important transformer performance including: partial discharge, water-in-oil and thermal performance.

Fig.1 Transformer condition-monitoring techniques [4]

4

Figure 1 shows the various techniques for transformer condition monitoring. The systems use a combination of online on-line sensors, computer-generated analysis and predictive data to continuously determine transformer-operating condition and to identify problems at an early stage [5]. MONITORING BY OIL ANALYSIS Insulating oils suffer from deterioration, which can become fatal for transformers. Also, discharge in oil can cause serious damage to the other insulating materials, making the monitoring of power transformers insulation an important task. The traditional way to monitor insulation condition of transformer is by oil analysis and this method is fully covered in international standards [5]. Chemical and physical analysis gives information on serviceability of the oil as both an insulator and coolant and of the transformer with respect to its thermal and electrical properties. Decomposition-products from breakdown of the oil, paper or insulating boards, glues etc are transported through the transformer by the coolant oil. Some are low molecular weight gases dissolved in the oil and can be identified by gas chromatography. Others indicating solid degradations include furans, cresols and phenols and detected by liquid chromatography.

Water content of Paper/oil system The electric breakdown strength of clean oil is little affected by water content until it is nearly saturated. Where such contamination is present, the relative amounts of water and contaminant have a significant, detrimental effect. Few catastrophic failures from arcing in oil occur without free water being present in the oil. However increase in water content in the cellulose (present as paper winding insulation and pressboard mechanical parts) not only increase the chances of a disastrous flashover, but also increases its rate of degradation, with reduction of the mechanical strength and potential failure of this weakest link in a transformer.

Routine test of Oil quality A minimum requirement for any size of oil filled transformer device, to provide a degree of confidence for its continued operation, is analysis of water content, together with electrical breakdown strength and acidity. Electrical breakdown strength test is measured with modern automated test cells. Breakdown voltages are measured with a rising AC voltage under prescribed conditions and the mean of six tests calculated for a single sample of oil. While scatter between the tests can be high, the mean value is reasonably repeatable for duplicate samples. To test Fibre and Particulate content, a simple count of visible fibres is made using a crossed Polaroid viewing system. Large, Medium (2-5 mm) and small (<2 mm) fibres are usually reported. The medium size fibres include the most cellulose fibres derived from the paper insulation whereas the large fibres are usually contaminants introduced either during maintenance or sampling. These fibres, in conjunction with water content, can give an indication of the cause of poor electrical strength. Other routine tests include acidity, resistivity, odour and colour tests. Measurement of the acidity can be done manually or automatically and the level detected either calorimetrically or

5

potentiometrically. Water content, acidity and dissolved or suspended contaminants may all individually affect resistivity. So the resistivity test is useful on site as a general indicator of oil condition. Appropriate further tests should be carried out to discover the cause of low values. Odour and colour tests can give an indication of thermal ageing of oil but are only ancillary to quantitative measurement. After a suspected fault, however, the assessment of the smell of oil from different parts of a transformer can be a valuable first indicator of the source and type of fault.

Dissolved gas analysis By analyzing oil sample for dissolved gas content it is possible to assess the condition of the equipment and detecting faults at an early stage. If a fault is indicated, the type of fault can be predicted using various analysis methods. Several dissolved gas analysis (DGA) tests should be taken over a period of time, in order to determine the rate of increase of fault gases, and therefore the rate of advancement of the fault. The gases involved are generally CO, CO2, H2, CH4, C2H4, and C2H6. Further analysis of concentrations, condition and ratios of component gases can identify the reason for the gas formation and indicate the necessity for corrective action.

KEY GAS CHARACTERISTIC FAULT H2 Partial discharge C2H6 Thermal Fault < 300ºC C2H4 Thermal Fault 300ºC- <700ºC C2H2, C2H4 Thermal Fault >700ºC C2H2, H2 Discharge of Energy

Table 1. Key Gas Interpretation Method [6] On-line gas-in-oil monitors became available soon after the introduction of the DGA technology. On-line gas analysis offers the potential for doing a much more revealing assessment of the dynamic conditions inside important transformers than possible through laboratory DGA. An advantage of those on-line monitors is the continuous measurement of one or more gases, so that any gassing trend, which is critical information for incipient fault screening, can be easily obtained. Originally only a hydrogen online monitor was available but now instruments detecting several gases are commercially available with a total oil monitor. The monitors use a combination of on-line sensors, computer generated analysis and predictive data to determine transformer operating condition on a continuing basis and identify problems in the incipient stage. A computer is used for data acquisition and to generate adaptive model-based transformer performance monitoring information. A database is established on important elements of transformer performance and long term trend analysis carried out to “tune” adaptive mathematical models to a particular unit. PARTIAL DISCHARGE MONITORING Dielectric breakdowns in transformers are most frequently proceeded by partial discharges (PD). PD occurs within a transformer where the electric field exceeds the local dielectric strength of the insulation. Possible causes include insulation damage caused by over voltages and lightning strikes, incipient weakness caused by manufacturing defects, or deterioration caused by natural aging processes. Although PD may initially be quite small, it is by nature

6

that a damaging process causes chemical decomposition and erosion of materials. Left unchecked, the damaged area can grow, eventually risking electrical breakdown. Dissolved gas analysis (DGA) is routinely employed to detect internal electrical discharging in power transformers. DGA can provide some information about the nature and severity of the PD [7]. However, knowledge of the PD location (which cannot be obtained from DGA results) would be a great help to the engineering specialist who must make decisions about remedial action. Various techniques have been developed to address the problem of PD monitoring in electrical plants. Measuring PD electrically is a very difficult task because the PD signals are extremely small (in the microvolt range), so the electrical interference can limit the sensitivity of the system. Recent years have seen the successful development and application of ultra high frequency (UHF) PD monitoring technology. UHF can be applied to realize not only the PD phenomena but also the location of a PD source. A typical UHF monitoring system is shown in Fig. 2. Signals from one or more sensors are filtered and amplified before they are detected and digitized. Analog-to-digital conversion is increasingly taking place at an earlier stage, as the bandwidth of affordable data acquisition hardware increases. The main reason for this trend is that adaptive digital signal processing can be used to condition the signals dynamically [8].

A clock and a phase reference signal derived from the power frequency waveform provide additional information that is logged with the digitized PD data. Each PD pulse recorded can then be associated with a particular time and “point-on-wave.” The amplitude of the displayed pulses is proportional to the energy of the UHF signal. Neural networks or intelligent software agents can be used to recognize patterns in this data and provide meaningful information concerning the nature and characteristics of the PD source. The key technique of UHF method is sensor and sensitivity. The sensor mainly adopts the apacitive sensor or UHF antenna. Because of the broad frequency content of the actual discharge, capacitive coupling in the UHF region has been shown to be an effective under certain conditions [9]. Scottish Power and Strathclyde University have developed a diagnostic tool for transformers, which uses UHF couplers operating in the 300-1500 MHz band [10]. The approach taken was to adapt technologies that were developed for continuous partial discharge monitoring in gas insulated substations (GIS). Principles such as pattern recognition and time-of-flight measurement are well known in relation to GIS, but involve greater challenge when applied to transformers.

Fig. 2 Principles of a typical UHF PD monitoring system

7

Acoustic PD monitoring has been another interesting technique getting the focus from both academic and industrial people for many years. Partial discharges occurring under oil produce a pressure wave that is transmitted throughout the transformer via the oil medium. Technique is available in which piezoelectric sensors are connected to the outside of the tank to measure the acoustic wave impinging on the tank either directly or via wave-guides. The advantages of acoustic method are that firstly, it can reach the possibility of PD location, which is of considerable value for power equipment maintenance. Secondly, it could recognize the PD acoustic signal regardless the electromagnetic noise in substation. Sometimes, it is difficult to discriminate the PD acoustic signals due to the interferences from either electrical or mechanical sounds in the substation. This is the obstacle for the method to be widely applied. The use of a new fibre optic acoustic sensor for the detection of discharges from within the transformer is developed by Virginia Polytechnic Institute and State University. The basic principle of the developed sensor is illustrated in figure 3. The system involves a sensor probe, optoelectronic signal processing and an optical fibre linking the sensor head and the signal-processing unit. The light from a laser diode is launched into a tow-by-two fibre coupler and propagates along the optical fibre to the sensor head. As shown in the enlarged view of the sensor head, the lead-in fibre and the silica glass diaphragm are bonded to form a cylindrical sensor-housing element. The incident light is first partially reflected (-4%) at the end face of the lead-in fibre. The remainder of the light propagates across the air gap to the inner surface of the diaphragm. The inner surface of the diaphragm is coated with gold, which reflects the entire incident light (96%), preventing any reflection from the outer surface; the fibre sensor is thus optically self-contained in any environment. This means that the optical signal is only a function of the length of the sealed cavity; and it is immune to the diaphragm outer surface contamination resulting from the contact with transformer oil. As indicated in the enlarged view of the sensor head, the diaphragm is tilted at an angle with respect to the lead-in fibre end-face so that the fibre captures only about 4% of the second reflection. The two reflections travel back along the same lead-in fibre through the same fibre coupler to the photo-detection end. The interference of these two reflections produces sinusoidal intensity variations, referred to as interference fringes, as the air gap is continuously changed. The development of the diaphragm pressure Sensor was concentrated upon utilizing an epoxy to bond the silica hollow core tube to the ferrule and the hollow core to the silica diaphragm. Using an online monitoring process, the air gap between the fibre and the inner surface of the silica diaphragm was adjusted to give the highest interference fringe visibility.

Fig. 3 Illustration of the principle of the fiber optic acoustic sensor

8

TEMPERATURE MONITORING Monitoring a transformer through temperature sensors is taken as one of the simplest and most effective monitoring technique. Abnormal temperature readings almost always indicate some type of failure in a transformer. For this reason, it has become common practice to monitor the hot spot, main tank, and bottom tank temperatures on the shell of a transformer. As a transformer begins to heat up, the winding insulation begins to deteriorate and the dielectric constant of the mineral oil begins to degrade. Likewise, as the transformer heats, insulation deteriorates at even a faster rate. Monitoring the temperature of the load tap changer (LTC) is critical in determining if a LTC would fail. In addition to the LTC, abnormal temperatures in the bushings, pumps, and fans can all be signs of impending failures. Recently, thermography has been used more widely for detecting temperature abnormalities in transformers. In this technique, an infrared gun is taken to the field and used to detect temperature gradients on external surfaces of the transformer. Infrared guns make it easy to detect whether a bushing or fan bank is overheating and needs to be replaced. The method is also useful in determining whether a load tap changer (LTC) is operating properly. Thermography is effective for checking many different transformers quickly to see if there is any outstanding problem [12]. However, thermography is not conducive to on-line measurements and; therefore, is prone to miss failures that may be developing between the periods when the transformers are checked. In order to make on-line monitoring possible, thermocouples are placed externally on the transformer and provide real-time data on the temperature at various locations on the transformer. In many applications, temperature sensors have been placed externally on transformers in order to estimate the internal state of the transformer. These temperature readings can be used to determine whether the transformer windings and oil are overheating or running at abnormally high temperatures. High main tank temperatures have been known to indicate oil deterioration, insulation degradation, and water formation [12]. VIBRATIONAL TECHNIQUE FOR MONITORING The diagnostic methods described so far have all dealt with trying to detect a failure in the electrical subsystem of the transformer, namely the electrical insulation around the coils. There has also been research carried out in regards to mechanical malfunctions in a transformer. According to the study, the factors generating the vibration in the transformer are of two types: core vibration and winding vibration [13]. Core vibration consists of excitation by magnetostriction and excitation generated at air gaps. Winding vibration is generated by Lorenz force due to correlation of leakage flux and winding current. These vibrations from winding and core penetrate into transformer oil, travel through it and reach the tank walls exciting their oscillations. Based on this analysis, we can say that tank vibration signals have strong relation with the condition of transformer’s core and windings and can provide useful diagnosis information. The vibrations from the windings and core can be measured at the tank wall by piezoelectric accelerometers. The accelerometer is positioned at different locations on the tank and measurements are taken twice in no-load and loaded modes, which is necessary to separate the vibrations of the core and windings. In no-load mode the electrodynamic forces in the

9

windings are practically absent, vibrations can be attributed to magnetic core conditions only. Measurements taken under loaded conditions include both core and coil vibrations. Therefore it is possible to find the spectrum related to winding vibration by subtracting the no-load (core) results from the loaded (core and coil) results. Such an approach is justified because the magnetic flux in the core is almost independent of the load. The vibration spectra consist of harmonics besides fundamental frequency (two times of power frequency).

Fig 4. Example of the measuring system

The core vibration will aggravate when core-clamping force is loosing. If high-voltage winding or low-voltage winding has displacement, distortion or lack in clamping force, the difference in height between the winds will increase, thus leads to ampere-turn imbalance and axial force deviation, resulting in intensified vibration. Acceleration sensors, which can be used to measure the vibration, are divided into piezoelectric type, train type and servo type. Low frequency response of servo-type acceleration sensor is excellent but its bandwidth is narrow (<500Hz), obviously not suitable for tank vibration measurement. Comparing piezoelectric-type with train type, piezoelectric-type sensor has wider application, installation resonance frequency is beyond 100Khz, and the bandwidth margin is ample. LOAD TAPCHANGER MONITORING On-load tapchangers (OLTCs) are one of the most problematic components of power transformers. The majority of transformer failures are directly or indirectly caused by tap changer failures, because a tapchanger contains the only moving components associated with transformer. The cost of tapchanger is very low compared to that of transformer but the failure of the tap changer can be responsible for the destruction of the complete unit. In earlier days the only method used by the tapchanger manufactures was the fitting of a temperature probe to monitor the temperature of the diverter switch oil. This proved totally inadequate, because the probe took time to register any significant change in temperature. Recently surge relays have been fitted to both the selector and diverter switch components but

10

they are still slow to respond. The diverter switches have to be carefully set up to avoid any spurious operation of the surge relays. On-line monitoring systems for on-load tapchangers are now available. In one on-load tapchanger diverter switch protection scheme a current transformer (CT) is mounted in the diverter switch compartment to monitor current passing through the transitional resistors of the tapchanger during operation. The output from the CT is fed to a timed relay, which operates if the duration of the current flow exceeds a preset limit. The CT is placed in such a way that it will be energized whenever current passes through either or both of the resistors during operation of the tapchanger, The output of the CT is used to energize a current transducer, and this in turn operates a time delayed relay. CONCLUSION The justification for condition monitoring of power transformers is driven by the need of the electrical utilities to reduce operating costs and enhance the availability and reliability of their equipment. Condition monitoring produces reliable information on plant condition, which allows maintenance resources to be optimized and assist with optimum economic replacement of the asset. Many techniques for the monitoring are available and new techniques are being developed constantly. Researches are concentrated on computer-based techniques on online monitoring of transformer and it’s components.

11

REFERENCES

1. Y. Han and Y.H. Song, Condition Monitoring Techniques for Electrical Equipment – A Literature Survey, IEEE transactions of power delivery, vol. 18, No. 1, January 2003

2. Muhammad Arshad, Syed M. Islam, Power transformer condition monitoring and assessment for strategic benefits. Australian Universities Power Engineering Conference ‘AUPEC2003’, 28September- 1 October 2003.

3. IEC Loading guide for oil immersed transformers. IEC Standard 60354, Sep.1991. 4. J. C. Steed, Condition monitoring applied to power transformers: an REC View. IEE

conference on ‘The reliability of Transmission and Distribution Equipment’, Conference Publication No. 406, pp 109-111, March 1995.

5. D. Harris, M. P. Saravolac, Condition Monitoring in Power Transformers. IEE colloq.

Condition Monitoring of Large Machines and Power Transformers, 1997, pp. 7/1–7/3. 6. Pahlavanpour, B.; Wilson, A.; Analysis of transformer oil for transformer condition

monitoring. IEE Colloquium on Engineering Review of Liquid insulation. 7 Jan. 1997 Page(s):1/1 - 1/5

7. M. Wang, A. J. Vandermaar, and K. D. Srivastava, “Review of condition assessment

of power transformers in service,” IEEE Elect. Insul. Mag., vol. 18, no. 6, pp. 12–25, Nov/Dec 2002.

8. Judd M D, Yang L and Hunter I B B 2005 Partial discharge monitoring for power transformers using UHF sensors 1: sensors and signal interpretation IEEE Ins. Mag. 21 5–14

9. Judd, M:D:, B. M. Pryor, S. C. Kelly and B. F. Hampton, Transformer monitoring using the UHF technique, Proc. 11th int. Symp. on High Voltage Engineering (London), Vol. 5, pp. 362-365, August 1999.

10. Judd, M. D, B.M. Pryor, O.Farish, J.S.Pearson and T. Breakenridge, Power Transformer Monitoring Using UHF Sensors. IEEE International Symposium on Electrical Insulation.April, 2000

11. Ward, B.H.; Lindgren, S. A survey of developments in insulation monitoring of power transformers. Conference Record of the 2000 IEEE International Symposium on Electrical insulation, 2000, Page(s): 141-147

12. Kirtley Jr., J.L., Hagman, W.H., Lesieutre, B.C., Boyd, M.J., Warren, E.P., Chou, H.P., and Tabors, R.D. 1996. Monitoring the Health of Transformers. IEEE Computer Applications of Power, 63, pp.18-23.

13. Ji Shengchang; Shan Ping; Li Yanming; Xu Dake; Cao Junling; The vibration

measuring system for monitoring core and winding condition of power transformer. Proceedings of 2001 International Symposium on Electrical Insulating Materials, 19-22 Nov. 2001, Page(s):849 - 852

AGEING PHENOMENA OF PAPER-OIL INSULATION IN POWER TRANSFORMERS

Henry Lågland University of Vaasa

[email protected]

1

Contents 1. INTRODUCTION 2. MECHANISM OF DEGRADATION OF THE PAPER-OIL INSULATION IN POWER TRANSFORMERS 3. THERMAL AGEING PROCESSES 3.1 Thermal ageing of paper-oil insulation 3.1.1 The ageing phenomena 3.1.2 Degree of polymerisation (DP) 3.1.3 Mathematical models for thermal ageing 3.1.4 Thermal ageing of oil/transformerboard insulation systems 3.1.5 Thermal ageing of solid and liquid insulating materials under the influence of water and oxygen 3.1.6 Comparison between open and closed expansion system 3.1.7 Comparison between constant ageing temperature and cyclic temperature changes 4. ELECTRICAL AND COMBINED ELECTRICAL AND THERMAL AGEING 3.1 Electrical ageing 3.2 Combined electrical and thermal ageing 5. CONCLUSION Literature

2

1. INTRODUCTION Power transformers are used in power generation units, transmission and distribution networks to step up or down the voltage of the power system (figure 1). The capacity is usually between a few MVA to about 100 MVA. To be able to use the real capacity of power transformers it is important to know the duration and level to which power transformers can be thermally stressed. Thus increasing demands are being imposed on the liquid and solid insulating materials with regard to the operating reliability and overloading capability. This paper describes the mechanism of degradation of the paper-oil insulation in power transformers. Mathematical models for thermal ageing are briefly presented as well as findings on thermal and electrical ageing phenomena for liquid cooled transformer insulation systems. Figure 1. Power transformer (ABB). The paper is mainly based on material chosen by Dr. Hasse Nordman, who is is chairman of the working group of the loading guide for oil-immersed power transformers [3]. Most of the material in this paper is based on the chapter Transformerboard II, Properties and application of transformerboard of different fibres by H.P. Moser and V Dahinden [1]. 2. MECHANISM OF DEGRADATION OF THE PAPER-OIL INSULATION IN POWER TRANSFORMERS Paper is a sheet of material made from vegetable cellulose fibres dispersed in water. The fibres are drained to form a mat. Cellulose is a linear polysaccharide consisting of hydro D-glucopyranose units held together by a β-linkage. A single cellulose fibre consists of many of these long chains [2].

Figure 2. Cellulose structure [2] The condition and strength of the fibres themselves and the physiochemical bonding, known as “hydrogen bonding”, between the cellulose molecules are the most significant factors that influence the strength of a dried sheet of paper (figure 2). The ageing performance of a sheet of paper is influenced by the degradation of the cellulose. The mechanism of degradation is rather complicated and depends on the environmental conditions. According to [2] there are three types of degradation:

3

1. Hydrolytic degradation, which refers to the cleavage at the glycoside linkage giving the sugar glucose

2. Oxidative degradation. Because cellulose is highly susceptible to oxidation, the hydroxyl groups are the weak areas where carbonyl and carboxyl groups are formed eventually causing secondary reactions giving chain scission

3. Thermal degradation below 200 °C is similar to, but faster than, the normal ageing of cellulose. Oxidative and hydrolytic degradation occur giving:

-severance of the chain, reducing the degree of polymerization and the strength -opening of the glucose rings

Decomposition products are mostly water and carbon oxides. Processes have been developed to improve the resistance of paper to degradation, or “upgrade” it. This is done either chemically by converting some of the OH radicals to more stable groups or by adding “stabilizers”, such as nitrogen-containing chemicals like dicyandiamide. The ageing of oil/solid insulation systems can be influenced by the treatment of the components of the insulation system, addition of inhibitors and sealing of the insulation system. 3. THERMAL AGEING PROCESSES The ageing behaviour of oil/solid insulation systems depends on the thermal, mechanical, electrical and combined electro-thermal stresses of the power transformers. 3.1 Thermal ageing of paper-oil insulation 3.1.1 The ageing phenomena [1] The ageing process in the oil/cellulose insulation system under thermal stress and their measurable effects are due to chemical reactions in the dielectric. Cellulose is a linear macromolecule, which in the unaged state consists of 1000- 3000 glucose rings (figure 3).

Figure 3. Basic structure of the cellulose macromolecule [1].

4

The periodically repeating structural units of the macromolecule, the β-glucose rings, are bonded to one another via oxygen bridges between the first and fourth carbon atom. Via the hydroxyl groups cross links are formed to crystalline regions, the micelles. Between the micelles individual cellulose molecules accumulate, forming a cavity system with a capillary diameter of 10 nm to a few µm within the fibre. The fibre length of pine pulp varies between 0.25 mm and 4 mm. The insulating oil consists principally of paraffins, naphthenes and a small portion of aromatics. The chemical composition of the mineral oil can only be stated approximately since the oil consists of a mixture of hydrocarbon compounds with different molecular structures. The main important parameters affecting the ageing of the solid and liquid insulation are:

1. The temperature of the oil/cellulose dielectric 2. The presence of water 3. The presence of oxygen

The temperature of the oil/cellulose dielectric is the critical ageing parameter for the change in the mechanical and electric properties of the material. Thermally supplied vibration energy of many atoms and groups of atoms is temporarily concentrated on individual C-H, C-O and C-C bonds and cleaves these bonds. This results in cleavage products such as carbon dioxide, carbon monoxide, water, hydrogen and scarcely measurable amounts of methane. By interacting with the oil components the entire final molecule can be separated off at the end of a chain and converted to other substances (e.g. sludge, acid). Also the ageing of oil under high thermal stress is characterized by chemical reactions. For the stability of the oil molecules the bond energy of the C-H and C-C single and double bonds is critical. Water forms as a reaction product, both during the thermo kinetic degradation of the cellulose and during the ageing of the oil. Apart from the thermo-kinetic degradation, the moisture present at the beginning of the ageing process, as well as the water formed by the reactions of the cellulose and of the oil, causes additional decomposition of the chain molecules. Because of the hydroscopic nature of the cellulose and fibre structure (capillaries), the water molecules accumulate between the cellulose chains and thus promote their thermo hydrolytic degradation. The water continuously causes fresh molecular cleavage thus having the negative property of constantly and retroactively accelerating the ageing process of the cellulose. In contrast to ageing of the cellulose, ageing of the oil is scarcely affected by water. Oxygen is predominantly present in the oil and thus noticeably accelerates the ageing of the oil while the effect of the oxygen on the ageing of the cellulose tends to be more moderate. On the other hand, in thoroughly dried insulation systems and in particular in the presence of fairly large amounts of oxygen, oxidation is the dominant process in the ageing of oil, at least in the initial stage. In the presence of reactive substances, in particular oxygen, the cleavage of oil molecule bonds is followed by a reaction sequence, the principal oxidation products of which are acids, solid constituents (sludge), water, carbon dioxide and carbon monoxide. Dissolved metals, such as iron and copper have a catalytic effect on the degradation process of the oil molecules. Since no free oxygen is formed during the ageing process of either the oil or the cellulose, oxygen enters the system only from the outside. Since the degradation of oil molecules produces other reactive substances, the decomposition of the oil molecules continues even where there is a deficiency of oxygen once the decomposition mechanism has been initiated.

5

3.1.2 Degree of polymerisation (DP) [1] The progress of the ageing of the oil/cellulose dielectric, the presence of water and the oxidation by oxygen, can be determined from the change in the material properties and from the formation and precipitation of reaction products. The degree of polymerization is the connection between the deterioration in the material properties and the formation of ageing products. It is a direct indication of the decomposition of the cellulose macromolecule and proves to be the most informative parameter for assessing the ageing or the progress of ageing of the cellulose (figure 4).

Figure 4. Degree of polymerisation (DP) [1]. 3.1.3 Mathematical models for thermal ageing [1] In 1930 Montsinger stated a law describing the existing interrelation between life expectancy and operating temperature of the transformer based on measurements on transformer insulating materials. Montsinger´s law states that an increase or decrease of the operating temperature by 6-10 K, according to the insulation raw material, results in the doubling or halving of the ageing rate. Using the Arrhenius relations Büssig and Dakin formulated a life law derived from the reaction equations of the chemical principles. By inserting the material characteristics in place of the molecule concentrations in the chemical reaction equations the thermo-kinetic deteriorations in characteristics are described.

6

The following differential equation for the first order reactions is:

( ) ( ) xTCxxdtd

×=−0 (1)

0x = material characteristic at time t=0 (initial value)

t = time in days x = material characteristic after ageing to time t C = ageing rate constant [1 / days] T = absolute temperature in K As is a constant the differential equation (1) can be reduced to: 0x

( ) xTCdtdx

×−= (2)

The following reduction in physical characteristic x subjected to thermal load T is produced by solving differential equation (2) with boundary condition x ( = 0) = : t 0x ( ) ( )[ tTCxTx ×−×= exp0 ] (3)

The differential equation (2) was modified by Dakin by introducing the general order of reaction α. The differential equation (4) is normalized with the initial value x ( t = 0) = : 0x ( ) ( ) 0xtxtX = ( ) αXTCdtdX ×−= (4)

α = order of reaction (α > 0) Taking into account the order of reaction α with boundary condition x ( = 0) = 1, the solution functions of differential equation (4) are:

t

for α = 0 ( ) ( ) tTCtX ×−= 1 for α = 1 [see (2)] ( ) ( )( )tTCtX ×−= exp (5) for α > 1 ( )( ) ( ) ( ) tTCtX ××−=− 11 1 αα Arrhenius equation can express the temperature dependence of ageing rate constant : ( )TC ( ) ( )( TRECTC A ×−×= exp ) (6)

AC = constant [1 / time]

7

E = activation energy [J / mol] R = gas constant [J / K mol] Or with Montsinger´s empirical formula: ( ) ( )ϑϑ ××= mCC M exp (7)

MC = constant [1 / time]

m = constant [1 / °C] ϑ = temperature in °C The so-called Montsinger step mM 2ln=∆ϑ (8) Deterioration in the physical characteristics of thermally stressed oil/solid insulation systems as a function of time can be described by means of the relation (3) and (5). The temperature influence (ageing parameter) is mathematically expressed with equations (6) and (7). 3.1.4 Thermal ageing of oil/transformerboard insulation systems [1] In the following the ageing behaviour of TRANSFORMERBOARD T III (density = 0.84 g/cm3) and T IV (density = 1.18 g/cm3) is described taking into account interactions with mineral oil as a liquid medium. The results are from measurements on testing rigs fitted with an open expansion system which were operated at temperatures of 90 °C, 105 °C, 120 °C and 135 °C with cycles. T III and T IV are made from the same raw material but produced by different processes. T IV is extremely highly compressed by hot pressing and T III rather less so by calendaring.

Figure 5. Degree of polymerization and normalized tensile strength (δ / δ0) as a function of time [1].

8

The almost identical reduction in degree of polymerization for the two materials indicates that the production process has little influence on the ageing, respectively on the breakdown of the cellulose macromolecules (Fig. S.12). The superior ageing behaviour of T IV compared with T III reveals when examining the mechanical characteristics. The higher density resulting from hot pressing and the greater degree of cross linking with its associated reduction of free fibre surface provides T IV with improved thermal stability with respect to the mechanical characteristics (Fig. S.14) No deterioration of the dielectric strength of the solid samples occurred under thermal stressing (Fig. S.18). The conductivity or specific resistance of the oil/solid dielectric is not a characteristic of the molecular structure of either cellulose or oil, but is due to ionic by-products. Ions are present even in unaged, freshly prepared samples, both in the oil and the solid insulation, in the form of residues from the production process. Additional ionic decomposition products are produced during oil and cellulose ageing by the high thermal stressing of the insulation system. Figure 6. Impulse withstand field strength as a function of ageing time [1]. Fig. S.20 shows that the ageing temperature plays an important part in lowering the specific resistance. The alternating voltage losses are also principally due to the ion condition. Hence the increase in loss factor tan δ of the solid samples over time with temperature as a parameter is caused by an increase in the ion concentration (Fig. S.19). This is on the one hand caused by dissociated ageing products from the chemical reactions of the oil and cellulose and also their interaction and on the other hand by the impurities and decomposition products absorbed from the fluid medium which increase tan δ.

9

Figure 7. Electric characteristics (impulse withstand field strength, loss factor and specific resistivity) of oil impregnated solid samples as a function of ageing time [1]. Gases produced in the system by the thermo-kinetic breakdown of the cellulose macromolecules and the decomposition of the oil molecules due to ageing, also the interaction between the reaction products of the solid and liquid insulation, are carbon dioxide CO2, carbon monoxide CO, hydrogen H2 and scarcely measurable amounts of aliphatic hydrocarbon gases. Water H2O is also produced and entire sections can be split off the end of the cellulose macromolecules. These separated molecules convert to other substances which are observable in the oil in the form of acids or low molecular sludge. Fig. S.21 shows the increase in water content in the solid samples as a function of time and at different temperatures. Shown in Fig. S.22 is the increase in water content in the oil which forms the insulation system together with T IV or T III. In the oil/solid insulation system the law applies that the relative moisture content in the oil must be identical to the relative moisture content in the board if the diffusion processes are complete. Since the absolute water content in the sheet material is far greater than in oil, the relative moisture content of the system is mainly determined by the solid sample. The saturation moisture of the oil and also that of the board is highly temperature dependent and moreover in opposition. Cyclic operating temperatures create non-equilibrium of the moisture content or promote equalizing processes between the solid samples, the oil and the air. With a reduction of the operating temperature, the solid sample absorbs water and releases it again during heating up.

10

Figure 8. Water content of the solid sample and oil as a function of time [1]. The most important electrical characteristics of the liquid insulation which in the transformer forms an inseparable insulation system with the solid samples are the breakdown voltage, loss factor, specific resistance and dielectric constant of the oil. The breakdown voltage of the oil, which has been aged together with T IV or T III, is principally dependent on the water content of the liquid medium (Fig. S.9). If the water is dissolved in the oil, i.e. the solubility limit of water in oil at the relevant temperature is not yet reached, then the breakdown voltage is approximately 65 kV, regardless of the ageing temperature, the ageing time and the composition of the insulation system. If the water is emulsified in the oil, then the breakdown voltage falls to 25 kV, the undissolved quantity of water having no further influence. Both the increase in loss factor (Fig. S.25) and the reduction in specific resistance (Fig. S.26) are relative small in comparison to the loss factor (Fig. S.6) and specific resistance changes (Fig. S.7) of the oil aged under the same conditions without the addition of the solid samples (pure oil ageing). This is because the T IV and T III absorb dissociated particles from the oil together with ionic decomposition products from the ageing process, thus exerting a cleansing effect. The TRANSFORMERBOARD reduces the direct and alternating current losses of the oil by approximately a factor of 10.

11

Figure 9. The loss factor and the specific resistivity of the oil as a function of ageing time and breakdown voltage of the oil as a function of the water content [1].

12

3.1.5 Thermal ageing of solid and liquid insulating materials under the influence of wateand oxygen [1]

r

g. S.30 show the effects of intentionally added water and the influence of the ontinuous extraction of water from the insulation system on the ageing process of T IV and the

and DP and tensile strength (Fig. .30) as a function of time [1].

Fig. S.29 and Ficoil. In Fig. S. 29 is the water and carbon dioxide production as a function of time plotted. They are both relevant values for assessing the ageing process. In Fig. S.30 are the representative degree of polymerization for the cellulose ageing behaviour and the tensile strength δz, which indicates the deterioration of the mechanical characteristics. Both the added water and the water formed by the reactions of the oil and cellulose due to ageing, retrospectively accelerate the ageing of T IV. This means that the life expectancy of poorly dried transformers can fall sharply compared to correctly prepared transformers. This is also confirmed by the results of experiments in which oil was constantly dried throughout the ageing period by means of molecular sieves. A life expectancy of double or even more is possible if the oil of a normally operated transformer is permanently dried and degassed during its total operating time. Figure 10. Production of water and carbon dioxide (Fig. S.29)S

13

As can be seen in Fig. S.33a the tensile strength, elongation and DP are only slightly influencedduring the experiments with a mo

lecular sieve as acceptor in the system constantly degassing and

rying the oil and thus, by virtue of equilibrium, also dehydrating the solid insulation. Adding

e experiments with xygen is due to the filtering effect of the NBC on the oil (Fig. S.34c). NBC proves to be an

b) Without molecular sieve

c) tion

d) r addition (50 ml)

e) 0 ml) and oxygen

ormerboard T IV/oil insulation (Fig. S.33), change in oil properties (Fig. S.35) as a

doxygen to the oil/cellulose insulation system significantly increases the water content in both the solid sample (Fig. S.33c) and the liquid medium (Fig. S.35c). The large rise in water content of the system is caused by the reactions of the oil molecules with oxygen. The loss factor reduction in the molecular sieve experiments shows the cleansing effect of the zeolites on both the solid samples (Fig. S.33a) and the oil (Fig. S.35a). An extremely large increase in loss factor measured at 90 °C occurs when particular board water content is exceeded (Fig. S.33d). The mechanical characteristics of NBC remain largely unaffected with a thermal stress at 135 °C over an ageing period of 100 days (Fig. S.34). The increase in loss factor in thoextremely ageing resistant material.

a) Addition of molecular sieve 5 Å [+ MS 5Å]

[-] Oxygen addi[+O2] Wate[+H2O] Water (5addition [+H2O+O2]

Figure 11. Change in solid properties of TransfNomex Board NBC/oil insulation (Fig. S.34) and function of time [1].

14

3.1.6 Comparison between open and closed expansion system [1] Fig. S.38 shows the effects of open and closed expansion vessels on the change in solid and oil haracteristics. The test chambers were operated at either a cyclically changing or a constant

the open system. On the other hand the water content of the board increases lightly more in the closed system than in the open. In the open vessels, with advanced ageing,

ctemperature. The tensile strength of T IV and the degree of polymerization, not plotted but running qualitatively parallel, which characterize the ageing state of the cellulose, fall less sharply in the closed than insthe extraction of water from the system can be achieved via the air cushion between the oil in the expansion vessel and the silica gel drier, supported by the temperature cycles which generate a regular air exchange. In long term tests at constant temperature, the tensile strength of the T IV decreases more sharply than in the experiments with cyclic temperature changes. The greater loss is mainly caused by the higher water concentration in the board which accelerates the thermo-hydrolytic decomposition of the cellulose. In the open system the oxygen produces accelerated oil ageing, in which, together with many other reaction products, ions occur, increasing the loss factor.

Figure 12. Change in Transformerboard T IV properties as a function of time [1].

.1.7 Comparison between constant ageing temperature and cyclic temperature changes

he tests also examined the influence of temperature cycles on the ageing behaviour of the mixed dielectric. The load cycles of a realistically loaded transformer were simulated by the daily

3 [1] T

15

recurring two hour cooling and subsequent three hour heating periods of the test vessel to thn

e ominal temperature. In the long term tests at constant temperature, the tensile strength of the T

decreases more sharply than in the experiments with cyclic temperature changes (Fig. S.38).

.1 Electrical ageing [1]

s of .33 kV/mm, 6.66 kV/mm and 10 kV/mm, on the ageing characteristic of the oil/solid dielectric. ransformerboard T IV together with inhibited oil was tested in the test vessels at room

purely electrical ageing.

lose molecules. In the transformer, where e insulation is exposed to continuous electric stresses with a maximum value of 3…4 kV/mm,

C were investigated at constant operating mperatures of 135 °C (120 °C) and a continuous electric stress with field strength of 5 kV/mm. he two solid materials were tested with mineral oil with or without a molecular sieve (Fig.

g of 100 % Aramid synthetic fibre.

. The st was discontinued. The increases found during ageing are a direct consequence of the reaction

IVThe greater loss is mainly caused by the higher water concentration in the board which accelerates the thermo-hydrolytic decomposition of the cellulose. The temperature cycles cause a regular exchange between the solid samples and the liquid insulation, also between the oil in the expansion vessel and the air cushion dehumidified by the silica gel drier. Hence the temperature cycles facilitate the extraction of water from the system, if only in small quantities. 4. ELECTRICAL AND COMBINED ELECTRICAL AND THERMAL AGEING 4 The aim of these tests was to determine the effect of AC electric fields (50 Hz) with strength3Ttemperature corresponding to During the tests no changes were observed which would indicate ageing of the samples. The constancy of the loss factor indicates that, during the 6000 hours test time, no ionic ageing products which increase tanδ were formed. Consequently, continuous electric field strengths of ≤ 10 kV/mm are incapable of cleaving either oil or celluththe electric field has little direct effect on ageing. 4.2 Combined electrical and thermal ageing [1] Transformerboard T IV and Nomex Board NBteTS.52). NBC is a highly compacted pressboard, consistin The loss factor of the oil/NBC insulation system remains unaffected during the entire test period under a stress of 5 kV/mm and 135 °C. In contrast to the tests with NBC, the oil/T IV insulation shows a marked increase in the loss factor. During the experiment on the oil/T IV insulation without a molecular sieve, partial discharges were observed after 2500 hours of operationteproducts which are formed by thermo-kinetic cleavage (135 °C) of the cellulose and oil molecular chains and their interactions. After the oil had been cleaned and degassed the experiment was continued with the same samples at a temperature of 120 °C. The abrupt decrease in the loss factor of the oil/T IV dielectric from 130 ‰ to 40 ‰ can be explained mainly by the experiment arrangements and the measurement technique. The loss factor measured at 120 °C after this operation decreased from 40 ‰ to 30 ‰ during the remaining 3500 hours. The reason for this decrease in the loss factor is the reduced rate of formation of ionic and gaseous reaction

16

products. In the investigations it was found that there is a change in the reaction mechanism in the ageing process of the T IV in the temperature interval between 120 °C and 135 °C. The molecular sieve degasses the oil permanently and thus promotes diffusion of the ageing products out of the solid sample. A prediction for an increase in the loss factor is a high temperature. This is also confirmed by the electrical tests at room temperature where no increase in loss factor was observed even at field strengths of 19 kV/mm over a test period of 6000 hours.

he electric field strength therefore has only an indirect effect on the ageing of the cellulose/oil

a function of time [1].

sulations with Kraft cellulose as a basic material, a similar decomposition process of the chain olecules was observed during thermal stressing in spite of their different production methods.

hig nce of T IV compared to T III became apparent only on comparison of e relative reduction in mechanical strength. At temperatures over 120 °C, the ageing rate of T

Tinsulation system by separating the ions formed by the supply of thermal energy and thus preventing them from recombining.

Figure 13. Variation of the loss factor of oil/T IV and oil/NBC dielectric as 5. CONCLUSION For the hot pressed Transformerboard T IV and calendared pressboard T III, both solid inmThe her ageing resistathIV is almost twice as high as that below 120 °C. The ageing rate of T IV is highly susceptible to the presence of water in the oil/solid insulation system at high thermal stresses, whilst the oil ageing is hardly changed by moisture. The presence of oxygen in the oil/cellulose insulation systems produces a severe ageing of the oil, but only slight ageing of the T IV. In the open expansion system, the ageing of the oil and cellulose solid insulation in interaction with the oil is accelerated by the admission of oxygen, supported by the cyclic temperature changes. An

17

excellent age stabilizing effect on Transformerboard T IV and the oil was achieved by the utilization of a molecular sieve in the oil/cellulose insulation system, principally by the adsorption of water and gas in connection with a hermetically sealed system. Nomex Board is very resistant to high thermal stresses, even with the additional influence of water and oxygen which had a significant effect on the ageing of the oil/cellulose insulation system [1]. The ageing is influenced not only by the temperature. Also the humidity, acid and oxygen content have a dramatic impact of the ageing. To get the influence of these parameters on the loading capability of power transformers Dr. Hasse Nordman has initiated a Cigre working group with a task list according to [8]. Another task for the working group is to define the content of humidity, cid and oxygen near the hot spot of the windings of the power transformer. If the working group

Transformerboard II, Properties and application of transformerboard of different fibres. Publisher Weidmann. 1987.

Shroff, A.W. Stannett. A review of paper ageing in power transformers. 1985.

] IEEE C57.91-1995. Annex D. Philosophy of guide applicable to transformers with 55 °C

] L.E. Lundgaard, W Hansen, D Linhjell, T.J.Painter. Ageing of Oil-Impregnated Paper in , vol. 19, No 1, January 2004.

cope. 2005.

afinds these base values, the article by Lundgaard, Hansen, Linhjell and Painter [7] can give the relevant factors for the ageing speed. Literature [1] H.P. Moser, V Dahinden. [2] D.H. [3] IEC 60076-7. Loading guide for oil-immersed power transformers. Proposal 2005. [4 average winding rise (65 °C hottest-spot rise) insulation systems. [5] IEEE C57.91-1995. Annex I. Transformer insulation life. [6] Cigre WG. Relative ageing rate and life of transformer insulation. [7 Power Transformers. IEEE Transactions on Power Delivery [8] Cigre WG Relative ageing rate and life of transformer insulation. S

18

ON-LINE MONITORING APPLICATIONS FOR POWER TRANSFORMERS

Pekka Nevalainen Tampere University of Technology

[email protected]

ABSTRACT Power transformers are very critical components in an electrical network. To provide continuous power supplying, transformers need to operate without failures. Condition monitoring of power transformer is a good tool to reduce power outages caused by transformer failures. There are many various methods for condition monitoring. This paper describes briefly several different types of applications for on-line monitoring. INTRODUCTION Modern power transformers are expected to operate very long and even a lifetime of 40 years may be expected. To provide long lifetime and to avoid power outages, condition monitoring of the transformers should be implemented. Furthermore, condition monitoring can reduce maintenance costs, may be used for identifying the reasons for failure and extend the lifetime even more [1], [2]. Condition monitoring can be carried out using different methods. Generally the methods can be divided in two groups; off-line and on-line. Off-line methods usually demand disconnecting the transformer from electric network and may use intrusive actions to do the needed measurements. These methods include for example: return voltage measurements (RVM), dielectric frequency response (tan-δ (f)) and gas analysis of transformer oil sample [2]. These methods and measurements are usually very accurate and provide good information of transformer’s condition [3]. On-line methods are using different type of sensors attached to the transformer. These sensors won’t affect the normal operation of the transformer, thus making it possible to perform continuous measurements for long periods of time. The on-line methods are based on sensors, data acquisition and analysis. Some of the methods are still experimental, but good commercial solutions exist [3]. On-line monitoring applications for power transformer include for example: measurements of different temperatures, gases in oil, humidity of the oil, partial discharges, winding movement, furfuraldehyde, tan-δ and load tap changers. Measurements are carried out using variable sensors including: optical fibers, vibration detectors, UHF antennas, gas sensors, thin film coils and capacitive sensors [4], [2], [3].

1

TEMPERATURE MEASUREMENTS Power transformer can overheat during too heavy loading or because of a cooling system malfunction. Too high temperatures in transformer cause aging of insulation. A simple model of thermal aging is called Motsinger equation, which states that if the temperature is above 98 °C, every 6 °C rise of hot-spot temperature doubles the aging rate of the insulation. On-line temperature measurements along with thermal modeling are key factors in temperature based condition monitoring [5]. Temperature can be traditionally measured using a Pt100 thermocouple device. Generally these devices can be used to measure transformer oil temperatures but they are also used to measure temperatures in very difficult locations, such as in the magnetic core itself [6]. However one drawback of the Pt100 sensors is the induced electrical noise [5]. Thin film sensors Thin film sensors are an advanced technique to measure temperatures. Thin film sensors are constructed on metallic or non-metallic bases using several techniques. The sensor described here is constructed using a vacuum evaporation to form a miniature thin film thermocouple (type K). Thin film sensors are more reliable and accurate than traditional Pt100 thermocouple devices. Thickness of thin film sensors ranges from 12 to 50 nm, while Pt100 devices have thickness of 100 µm. Therefore the thin film sensors won’t create an air gap or change the flux inside the core, hence they are harmless to use in critical installation locations. Thin film sensors can be used in on-line temperature measurements [6]. Enhanced fiber optic temperature sensor Fiber optics can be also used in temperature measurements. Two traditional fiber optic measurement techniques exist; a point sensor and a distributed measuring system. They provide sufficient accuracy for on-line monitoring; +/- 1 °C for point sensors and +/-1 °C/m for distributed measurement system. Unfortunately these kinds of systems are very expensive and the long fibers are not robust enough. An enhanced fiber optic temperature sensor was constructed to overcome these drawbacks [5]. The new system uses a sensing probe integrated with a plastic cladding large-core (200 µm) optical fiber. Peeling a small portion of the cladding and using a reference liquid as a replacement forms the sensing element. When the temperature of the reference liquid changes, the refractive index is modulated and therefore the propagation regime of the fiber is modified. The temperature refractive index change of the reference liquid is known, making it possible to determine the temperature of the fluid where the probe is immersed. The temperature detection is based on analog to digital processing hardware to monitor the power output of the optical fiber. The resolution is 0,2 °C and accuracy is 0,5 °C for the measurement system [5].

2

GAS ANALYSIS Power transformer gas-in-oil analysis (DGA) can be used for effective diagnostics and condition monitoring. Electrical and thermal stresses such as arching, partial discharges and overheating cause degradation of dielectric oil and solid dielectric cellulose materials. The degradation of insulation produces different gases. Important gases for fault detection include: H2, CO, CO2, CH4, C2H2, C2H4 and C2H6 [7]. Different degradation mechanisms generate different gases thus making it possible to determine the degrading part of the transformer [2]. On-line DGA Traditionally DGA is carried out using off-line measurements. However there are increasing availability of methods and sensors for on-line monitoring of dissolved gases. For example the Syprotec Hydran is widely used mainly for CO and H2 detection. It was developed in the late 1970’s. Recently, there have been better techniques for gas analysis using a membrane or vacuum extraction [8]. On-line measurements benefit from wide experience gained in laboratories over many decades. Semiconductor sensors, infrared sensors, combustible gas detector and gas chromatography are commercially available. Progress in process automation and microelectronics makes it possible to use more sophisticated measuring equipment, which can be used together with artificial neural networks (ANN) and fuzzy logic systems. These kind of methods applied to DGA can be used to reveal apparent fault conditions as well as hidden relationships between different fault types [7]. On-line gas phase monitoring On-line gas analysis can be carried out using infrared spectroscopy measurements. One application is to use a Clemet TM Fourier transform infrared spectroscope (FTIR) to measure the free gases in the inert gas blanket above the transformer oil. FTIR can detect all hydrocarbons and even Furans too. In temperatures above 120 °C the Furans are in gaseous state making it possible to detect them as gases. There has been discussion that measuring the free gas quantities will provide helpful information on insulation status more readily than DGA. If the continuous gas phase monitoring shows abnormal behaviour, more accurate diagnostic test should be conducted [9]. FURFURALDEHYDE MEASUREMENTS Concentration of furfuraldehyde (FFA) of the power transformers is usually measured during periodic inspections. Mostly used technique to measure FFA is high performance liquid chromatography (HPLC). Statistical survey shows that FFA concentration can vary from 0,1 to 10 ppm [10]. High performance liquid chromatography can be used for on-line FFA measurements but the systems are rather expensive [11].

3

On-line FFA measurements using optical sensor An alternative method for FFA concentration determination is introduced. It is reported experimental by the author. The method uses toxic chemicals, which react with FFA to produce colored complex in solution. A linear correlation between the optical absorbance of the complex in solution and the concentration of FFA in the oil is established [10]. The method is based on solid porous 2 cm thick glass-like discs coated with aniline acetate layer of 1 mm. Several discs are immersed in oil and when FFA is present, the discs turn into pink colour in a few minutes. The sensor is made of a light source, monochromator, lens, two-branched light pipe (fiber), mirror and a detector. The light travels along the fiber and through the discs and then reflects back from the mirror and goes through the discs again and finally arrives to the detector. There are several discs to improve sensitivity and to shorten response time. The amount of absorbed optical wavelength of ~530 nm can be measured with detector. The system can detect 0,1 ppm FFA [10]. The figure 1 describes the behaviour of the normalized transmission as a function of wavelength and FFA ppm consentration.

Figure 1. The figure represents measurement of different FFA concentrations [10]. The minor drawbacks of this application are that the time taken for a disc to change color is temperature dependant and the current system components are little bulky. However, an on-line application is possible using for example few LEDs as the light source and more compact design [10]. MECHANICAL FAULT DETECTION USING FREQUENCY RESPONSE ANALYSIS Internal condition of the power transformers is difficult to determine without using non-intrusive tests. Internal condition may get worse due to winding movement. Usually winding movement is caused by short circuits and loss of winding clamping pressure. Internal damage may also occur in transit or during initial installation. Typically these types of faults alter the distributed capacitance or inductance of the windings. Frequency response analysis (FRA) can be used to

4

detect the internal mechanical faults but the results are affected by many factors leading to uncertain conclusion [12]. On-line low voltage impulse test method Low voltage impulse (LVI) also known as FRA is used to monitor possible winding movement on-line. The application is based on short low voltage impulse applied to the one transformer winding and then measuring the impulse again on another winding. A high voltage bushing capacitance tap was used as impulse injection and also for measurement. Impulse used in the measurements was a lightning impulse. Switching pulses were also considered, but they did not contain enough power in high frequencies [13]. Figure 2 shows a measured on-line voltage impulse.

Figure 2. The measured voltage impulse used in on-line FRA tests [13]. Using Fourier transform for the original input impulse and the measured output impulse, a transfer function can be calculated. By comparing the results of the transformer before initial installation and the results made later on, the possible winding movement can be detected [13]. This technique can be called as a difference technique. It is also possible to take signatures from each phase, which can be called as a signature technique. This technique shows the similarity of windings at the testing time and provides a reference to evaluate the faults or abnormalities in the future [12]. Additional methods for on-line FRA measurements are to use different frequency ranges to detect different type of faults. For example major faults such as winding displacement and grounding can be detected by low frequency spectrum range of 2 kHz, while minor faults like interturn faults and bulging of conductors can be identified by high frequency range of 2 MHz [12].

5

TAN-δ ON-LINE MONITORING Measurement of the tan-δ of power transformer insulation can be used to determine the quality of the insulation as a standard test before initial installation. However, during in-service conditions such as interferences, limited time (periodic measurements) and difficult access to equipment makes the tan-δ measurements difficult to perform [14]. Field measurements needed the transformer to be disconnected because of the low testing voltage (10 – 12 kV) compared to the operating voltage of the transformer. Low levels of tan-δ are usually a sing of healthy insulation. Sudden increases in value in tan-δ over time are taken as a sign of insulation deterioration [2]. On-line continuous monitoring can overcome some of these drawbacks. The on-line method is based on a capacitor connected to a tap of HV equipment (e.g. a bushing). Together they form a voltage divider, which is used along with a reference voltage to calculate the tan-δ. The author reports applications for HV current transformers (CT) and bushings [14]. Therefore, a direct application for measuring a tan-δ of power transformer insulation is not reported. However, it is possible to monitor the tan-δ of a power transformer’s bushing, if the proper voltage divider tap is present. It is also possible to use a non-invasive capacitive divider as a sensor if the voltage tap is not present. The capacitive sensor is installed on the porcelain surface of the bushing next to the ground potential [15]. PARTIAL DISCHARGE MONITORING Power transformer breakdowns are most frequently preceded by partial discharges (PD). Monitoring the PD activity of the transformers can give valuable information about the possible breakdown [11]. Every partial discharge occurring inside the transformer generates an electrical pulse, a mechanical pulse and electromagnetic waves. These different types of pulses can be detected using various sensors and techniques. A very high frequency VHF PD detection uses narrowband measurements tuned to certain frequency with best sensitivity [2]. Also acoustical sensors can be used in frequency range of 100 – 300 kHz [11]. Acoustical sensors include for example microphones installed to the transformer hull. Also fiberglass rods immersed to the transformer oil can be used for acoustic measurements [23]. Ultra high frequencies (UHF) can be used to detect PD occurring inside the power transformers [16]. UHF PD detection Partial discharges occurring inside the transformer excite electromagnetic waves with resonance at frequencies 500 – 1500 MHz. Possible causes of PD include: a temporary overvoltage, weakness in the insulation introduced during manufacturing or due to various aging effects. Even if the PD pulses of an evolving fault are weak at first, PD will cause chemical decomposition and erosion of materials. UHF technique may use an UHF disc coupler that fits to the unused oil ports of the power transformers. Another approach is to use a dielectric window that is installed in the transformer hull [16], [17]. Different types of data handling layers are used in this on-line application. The system needed to be scalable and support new sensors, data sets and interpretation techniques as they become

6

available. Therefore four data layers were constructed including data monitoring layer, interpretation layer, corroboration layer and information layer. The system will integrate also other monitoring technologies and data sources to provide more accurate and diverse analysis. PD sensor data can be analysed using these methods: time-energy mapping and clustering of PD data, time-frequency analysis combined with feature extraction and clustering and phase-resolved data representation [17]. New techniques for on-line PD measurements On-line PD measurements of HV equipment suffer from heavy noise caused by various origins. A severe noise enough can reduce the measurement sensitivity and accuracy of PD measurements. Both software and hardware (HW) approaches can be used to reduce the noise. New sensors, such as fiber optic and directional sensors together with multiple terminal measurements and better differential and balanced circuits are main points of hardware development. In software development different type of modern filters and noise gating technologies and advanced digital signal processing [18]. Couple of new sensors is introduced including a PD coupler board and multi-channel PD detector. The new PD coupler is not directly connected to the HV conductor or components. The new coupler consists of a sensing board, a high frequency transformer and amplifier. The sensing board is installed near of the HV conductor and the stray capacitance acts as a coupling capacitor. Also a new multi-channel PD detector uses advanced digital signal processing, directional sensing and noise gating techniques [18]. VIBRATION MEASUREMENTS There are two inner factors that can cause vibration in power transformers. First there is core vibration, which is caused by excitation by magnetostriction and excitation generated in the air gaps. Second there is winding vibration, which is generated by Lorenz force due to correlation of leakage flux and winding current. These vibrations are carried along the transformer oil to the transformer tank walls. Vibration signals seem to have strong relation with the conditions of the transformer core and windings. That’s why vibration measurements can provide helpful information in transformer condition monitoring [19]. A piezoelectric accelerometer is positioned at different locations on the transformer tank wall. The accelerometer was isolated from the tank wall using insulation to overcome heavy 50 Hz noise in the measured signal. Additionally the signal needed amplification and a charge amplifier input was connected to the accelerometer output. The measurements showed that vibration frequencies vary from 10 Hz to 2 kHz with amplitude of 0,5 µm – 50 µm. On-line measurements are conducted under load and no-load conditions. The spectrum of the signal is calculated for analysis. It is possible to find the spectrum of winding vibration by subtracting the no-load results from the loaded results. This is possible because the magnetic flux in the core is almost independent of the load [19]. Figures 3 and 4 represent the measuring system and some results. Different characteristics of the spectrum of the measured vibration of a power transformer in a good condition can be used as a reference during on-line monitoring. However, more study with

7

database, vibration modeling and extracting the failure characteristic vector should be performed for proper condition analysis [19].

Figure 3. The diagram describes the measurement system [19].

Figure 4. The waveform on the right is the spectrum of the measured vibration [19]. MONITORING OF ON LOAD TAP CHANGERS Tap changers are the mechanical switching devices of the power transformer. On load tap changer (OLTC) cost is low compared to the power transformer, but its malfunction can destroy the complete transformer. Traditionally some temperature sensors were installed on OLTCs, but the sensors weren’t fast enough to detect the temperature changes [1]. On-line monitoring of OLTCs An international survey shows that OLTCs cause more failures and outages than any other component of a power transformer. OLTC possible failures include: motor malfunction, loss of power and flaw in controlling circuits may cause the tap change to stop before its completion. The mechanical parts can also wear out causing loss of synchronization between selector and diverter or jammed moving parts. In addition, the dielectric breakdown can occur due to failure of inter phase insulation or aging of the oil [20].

8

The tap changer needs 5 – 6 seconds to finish its operation. During this time, different sensors are used for on-line monitoring. Vibrations caused by contact movements are at very high frequencies thus a high sampling rate is needed. The system consists of a 30 kHz accelerometer, clamp-on current transformer and pair of thermocouples. Monitoring of OLTCs is carried out using measurements on vibration of the contact movements, temperature of the insulation oil, voltage and current of the drive motor. The system is triggered by operation of the OLTC and above-mentioned measurements are conducted together with detection of the tap position. Figure 5 represents two different vibration waveforms. These measurements are collected to a database in order to do analysis. The database consists of signatures of a vibration fingerprint, drive motor current waveform envelope and the deviation between the temperatures of transformer main tank and OLTC tank [20].

Figure 5. This figure describes two different vibration waveforms of OLTCs. The above waveform represents a faulty condition and the bottom waveform a normal condition [21].

Figure 6. On the right there is a diagram of a mean value of the condition indicator after 2400 tap change operations [21].

9

The on-line condition monitoring of OLTCs can be used to detect gradual deteriorating and sudden abnormalities as well. For example long-term measurements over 3 years showed a gradual drift in the mean value of the power transformer OLTC. The measurements included over 2400 tap change operations. Probabilities of rate of degradation can be thus determined using long-term tests together with short-term tests [21]. Figure 6 shows the waveform of the condition indicator value during the 3-year test. CONCLUSION The electrical equipment of the power network, particularly a power transformer, should function correctly without failures for many years. Various techniques for determining the condition of the transformer exist. Accurate enough long term continuous on-line monitoring together with more accurate and sophisticated off-line measurements can form a good technique for condition monitoring for power transformer. Good condition monitoring makes it possible to optimize the maintenance program thus minimizing the costs and maximizing the reliability. In this paper, various techniques were introduced. Some of them are still experimental and need more work in the future for reliable monitoring, while some are already commercially available. All techniques are promising and combining various measurement results, using for example fuzzy logic, neural network systems and different transformer models, an intelligent condition monitoring of power transformers can be established [3], [22]. However, the availability of many different type of measuring systems combined with insufficient knowledge of many aging mechanism and especially their interaction, makes an accurate and reliable condition monitoring a very challenging task to accomplish. REFERENCES [1] A. Basak, Condition monitoring of power transformers, Engineering science journal, pp.

41-46, 1999 [2] J. P. van Bolhuis, E. Gulski, and J. J. Smit, Monitoring and Diagnostic of Transformer Solid

Insulation, IEEE transactions on power supply delivery, vol. 17, no. 2, 2002 [3] T. Krieg, M. Napolitano, Techniques and experience in on-line transformer condition

monitoring and fault diagnosis in ElectraNet SA, Power System Technology, Proceedings, PowerCon, Vol. 2, 4-7 pp. 1019 - 1024, 2002

[4] T. Stirl, R. Skrzypek, C. Q. H. Ma, Practical experiences and benefits with on-line monitoring systems for power transformers, Electrical Machines and Systems. ICEMS 2003. Sixth International Conference on Volume 1, pp. 9-11, 2003

[5] G. Betta, A. Pietrosanto, A. Scaglione, An enhanced fiber-optic temperature sensor system for power transformer monitoring, Instrumentation and Measurement, IEEE Transactions on Volume 50, Issue 5, pp. 1138 – 1143, 2001

[6] F.J. Anayi, A. Basak, D.M. Rowe, Thin film sensors for flux and loss measurements, Condition Monitoring of Large Machines and Power Transformers, IEE Colloquium, Digest No: 086, pp. 3/1 - 3/4, 1997

[7] X.Q. Ding, H.Cai, On-line transformer winding's fault monitoring and condition assessment, Electrical Insulating Materials, Proc. ISEIM, pp.801 – 804, 2001

10

[8] T. McGrail, A. Wilson, On-line gas sensors, Condition Monitoring of Large Machines and Power Transformers, IEE Colloquium, Digest No: 086pp. 1/1 – 1/4, 1997

[9] M. K. Pradhan, T. S. Ramu, Criteria for estimation of end of life of power and station transformers in service, Electrical Insulation and Dielectric Phenomena, CEIDP, Annual Report Conference, pp. 220 – 223, 2004

[10] R. Blue, D. G. Uttamchandani, A novel optical sensor for the measurement of furfuraldehyde in transformer oil, Instrumentation and Measurement, IEEE Transactions, Volume 47, Issue 4, pp. 964 – 966, 1998

[11] A. White, A transformer manufacturer's perspective of condition monitoring systems, HV Measurements, Condition Monitoring and Associated Database Handling Strategies, Ref. No: 448, IEE Colloquium, pp. 4/1 - 4/4, 1998

[12] S. Birlasekaran, F. Fetherston, Off/On-line condition monitoring technique for power transformers, Power Engineering Review, IEEE Volume 19, Issue 8, pp. 54 – 56, 1999

[13] M. Wang, A. J. Vandermaar, KD Srivastava, Condition monitoring of transformers in service by the low voltage impulse test method, High Voltage Engineering, Eleventh International Symposium, Conf. Publ. No: 467, Volume 1, pp. 45 - 48, 1999

[14] P. Vujović, R. K. Fricker, Development of an on-line continuous tan(δ) monitoring system, Electrical Insulation, IEEE International Symposium, pp. 50 – 53, 1994

[15] A. Setayeshmehr, A. Akbari, H. Borsi, E. Gockenbach, New sensors for on-line monitoring of power transformers’ bushings, Nordic Insulation Symposium, pp. 151-158, 2005

[16] J. Pearson, B. F. Hampton, M. D. Judd, B. Pryor, P. F. Coventry, Experience with advanced in-service condition monitoring techniques for GIS and transformers, HV Measurements, Condition Monitoring and Associated Database Handling Strategies, Ref. No. 448, IEE Colloquium, pp. 8/1 – 810, 1998

[17] M. D. Judd, S. D. J. McArthur, J. R. McDonald, O. Farish, Intelligent condition monitoring and asset management. Partial discharge monitoring for power transformers, Power Engineering Journal, Volume 16, Issue 6, pp. 297 – 304, 2002

[18] Q. Su, K. Sack, New techniques for on-line partial discharge measurements, Multi Topic Conference, IEEE INMIC, Technology for the 21st Century, Proceedings, IEEE International, pp. 49 – 53, 2001

[19] J. Schengchang, S. Ping, L. Yanming, X. Dake, C. Junling, The vibration measuring system for monitoring core and winding condition of power transformer, Electrical Insulating Materials, ISEIM, Proceedings, International Symposium, pp. 849 – 852, 2001

[20] P. Kang, D. Birtwhistle, J. Daley, D. McCulloch, Noninvasive on-line condition monitoring of on load tap changers, Power Engineering Society Winter Meeting, IEEE, Volume 3, pp. 2223 – 2228, 2000

[21] P. Kang, D. Birtwhistle, On-line condition monitoring of tap changers-field experience, Electricity Distribution Part 1, CIRED, 16th International Conference and Exhibition, IEE Conf. Publ No. 482), Volume 1, pp. 5, 2001

[22] O. Roizman, V. Davydov, Neuro-fuzzy computing for large power transformers monitoring and diagnostics, Fuzzy Information Processing Society, NAFIPS 18th International Conference of the North American, pp. 248 – 252, 1999

[23] D. Harris, M. Saravolac, Condition monitoring in power transformers, Condition Monitoring of Large Machines and Power Transformers, Digest No: 086, IEE Colloquium, pp. 7/1 - 7/3, 1997

11

TESTS AND DIAGNOSTICS OF INSULATING OIL

Kaisa Tahvanainen Lappeenranta University of Technology

[email protected] INTRODUCTION The importance of oil as an insulation material is significant in compositions, where warmth must be transported out or where it is important to impregnate the laminate insulation material. Oil is commonly used as insulating material in power and instrument transformers, switchgear installations, transformers, capacitors, bushings and cables. Experiences worldwide have shown that lack of attention to oil condition can lead to shorter operational lives of equipment. In addition to having good electrical, thermal and mechanical characteristics, insulation oil should endure the stress of service without deteriorating. Especially long term stress in high temperatures can alter the characteristics of insulation oil. This somewhat natural ageing of insulation oil can be fueled by electrical and chemical stress leading to poorer cooling conditions as well as insulation properties of oil and in the worst case in unplanned outages and failure of equipment. Changes in insulation oil can be analyzed in order to determine the oil condition and hence functioning of the equipment. In this paper, some of the most common insulating oil testing methods and principles are presented. Some of the oil testing techniques are expensive and require expert-knowledge. Testing is therefore done in the most important sites as the cost is small in comparison with that associated with insulation failure. This usually means on-line testing. Oil test performed on site can also be representative and can be performed by relatively unskilled staff. Many electricity distribution companies use laboratory tests for important sites as well as on-line monitoring of oil condition. INSULATING OILS Insulating oils have been used in oil filled electrical equipment as coolant and insulation since the 1900s. The purpose of insulation oil is to protect the solid parts of insulation structures (used in the construction of the equipment) from electric discharges, assist in quenching arcs or to dissipate heat generated in the equipment during use. The characteristics required for insulation oil depend on the equipment and circumstances in which the insulation is used. In transformers liquid insulation requires great dielectric strength and good conveyance of heat in order to ensure cooling. Insulation liquid should also have great resistivity, low loss factor and good tolerance for discharge. In cables and capacitors insulation liquid should possess low viscosity (impregnation) in addition to good tolerance for discharges. In addition to technical features the use of insulation liquids is influenced by environmental and life span facts. Lately, the research has focused on e.g. biodegradable vegetable-based oils, such as turnip rape oil. (Aro et al. 1996)

1

Several different types of insulating oils have been produced and introduced into the market. These range from mineral to synthetic oils with different chemical and electrical properties for different applications. Mineral oil is the most common insulation liquid used due to its availability and affordability. Mineral oils consist of hydrocarbon composition of which the most common are paraffin, naphthene and aromatic oils. The main hydrocarbon composition of mineral oil and different impurities affect to what kind of features mineral oil possesses. Paraffin is the least likely to oxidize (antioxidants may be added to advance this), it is mainly used in breakers. In transformers paraffin precipitates easily, so naphthene is mainly used. Aromatic oils are most suited for cases, where good discharge tolerance is needed. The most common mineral oil in use is transformer oil (required characteristics are defined e.g. in standard IEC 60296). The electrical features and low viscosity make it a good insulation and refrigerating medium. Transformer oil boiling temperature is 250-300 ˚C and it is very fluid. It also oxidizes easily and is flammable (in liquid form the flashing point is however over 130 ˚C). The features of transformer oil in service are also influenced by moisture and impurities in addition to oxidation. (Aro et al. 1996) Synthetic insulation liquids include synthetic aromatic hydrocarbons, alkylbenzenes, esters, silicon oils and polybutans. Esters are used for refilling transformer oil or in combination of transformer oil. Silicon oil is synthetic oil, which is more environmentally friendly than transformer oil. It is also non-combustible. Synthetic oils are more expensive than mineral oils and they have poor thermal conductivity and discharge tolerance. Silicon oils cannot be used in breakers because arcs generate flammable gases. In addition silicon oils must be protected from moisture, because they absorb water. (Aro et al. 1996) In insulation constructions the combination of solid and liquid insulation is often used. By impregnating the solid insulation with liquid insulation, the insulation characteristics can be improved. By combining mineral oil and paper, insulation is created that has better electrical strength than either material alone. This kind of insulation is used in transformers, cables and capacitors. The weakness of this insulation combination is that both of the materials are sensitive to impurities, especially to moisture and oxidation. (Aro et al. 1996) INSULATION OIL TESTS In addition to having good electrical, thermal and mechanical characteristics, insulation structures should endure the stress of service without deteriorating (Aro et al. 1996). Prolonged bulk oil temperatures of greater than about 75°C usually involve spot temperatures of over 98°C. Further increases rapidly increase degradation of oil impregnated paper and pressboards. Ageing of these cellulose-based materials is also increased by higher moisture and oxygen contents. Additionally such high temperatures increase the rate of oxidation of the oil, producing acids, moisture and sludge which impair both cooling properties and dielectric strength. Water increases the rate of oil oxidation and effectively self-catalyses the reaction. All of these changes increase the possibility of electrical breakdown. Chemical and physical analysis gives information on serviceability of the oil as both an insulator and coolant. (Myers 1998) Oil testing can be split into two categories, tests that are performed on site and test that are performed in an oil laboratory (table 1). On-site tests are intended to determine the oil quality and

2

acceptance of new oil at the point of delivery. There are limited tests that can be performed on site and they are relatively simple and low cost. Furthermore, non-specialist personnel usually can carry out these tests. Laboratory tests are carried out in a controlled environment in the laboratory for quality of the supplied unused oil, condition of in service oil and plant condition monitoring. (Pahlavanpour et al. 1999)

Table 1. Location and type of the test conducted on insulation oil. (Pahlavanpour et al. 1999)

Location of test Type of test Field test Moisture

Breakdown voltage Color and appearance

Laboratory test Acidity Resistivity Breakdown voltage Moisture Dielectric dissipation factor Interfacial tension Flash point Dissolved gas analysis Furan analysis

Off-line techniques can only be carried out during outages. They are only applicable to large or strategically important sites. Post fault forensic tests also include paper analysis and metallurgical tests. To use the information from these tests as a fault diagnostic or an ongoing monitoring programme, it is necessary to compare results with previous datum levels. These tests are generally sensitive and specific and are not expensive in the context of fault detection for such plant. A multi-parameter approach is essential. On-line techniques may be by discrete tests or can be applied continuously and avoid the need for outages. Temperature is usually recorded for large plant, but for routine detection of abnormal conditions while maintaining output, analysis of insulating oil provides a cheap but powerful tool to evaluate the condition of any oil-insulated plant, be it power or instrument transformers, bushings, cables or switches. (Myers 1998) In general, the optimum interval for sampling and testing of oil will depend on type of equipment in operation and power, action and service conditions of the equipment. The duty experienced by insulating oil in transformers and selectors is different from that experienced in circuit breakers and divertors. This may lead to different changes in chemical characteristics of the oil and a different rate of oil deterioration. A check interval can be every 1-4 years. Economical factors and reliability requirements have to be compromised. Details of the oil sampling technique are outlined in IEC 60475. Frequency of oil sampling and testing is given in IEC 60422. (Pahlavanpour et al. 1999)

3

Dissolved Gas in Oil Analysis Dissolved gas analysis (DGA) is widely accepted as the most reliable tool for the earliest detection of inception faults in electrical equipments using insulating oil. Gas-in-oil analysis by gas chromatography (for details, see Annex 1) has proven to be predictive and valuable for some of the problems, such as arcing, corona, overheated oil, and cellulose degradation. When insulating oils and cellulose materials in reactive equipment are subjected to higher than normal electrical or thermal stresses, they decompose to produce certain combustible gases referred to as fault gases. For incipient fault conditions (i.e. slowly evolving fault), the gases generated will be dissolved into the oil long before any free gas is accumulated in the gas relay. Thus by analyzing oil sample for dissolved gas content it is possible to asses the condition of the equipment and detecting faults at an early stage. If a fault is indicated, the type of fault can be predicted using various analysis methods. Table 2 shows the concentrations for gases to determine whether there is any problem and whether there is sufficient generation of each gases for the ratio analysis to be applicable. (Ward 2003)

Table 2. Concentration of dissolve gas (Saha 2003).

Key gas Concentrations (ppm) H2 100

CH4 120 CO 350

C2H2 35 C2H4 50 C2H6 65

Several DGA tests should he taken over a period of time, in order to determine the rate of increase of the fault gases, and therefore the rate of advancement of the fault. The assumption is made that the change in the rate should not exceed 10 percent per month for gases, the concentration of which exceeds the typical concentration value. (Arakelian 2002) The most important part of DGA is interpretation of the results which can vary from simple use of key gases to suggest a type of fault, to sophisticated computerized calculations of gas ratios, rates of increase, equilibrium between free and dissolved gases and predicted times to Buchholz alarm operation. Such systems, used by experienced analysts, can decrease effort on routine samples where no significant changes occur, allowing more time to examine the subtle changes that can give early warning of incipient faults. Trend analysis is of paramount importance, as residual effects from previous faults and multiple types of overheating can seriously distort interpretation of results from a one-off sample. (Myers 1998) Interpretation of DGA results is often complex and should always be done with care. Doernenberg, Roger’s and Duval’s triangle methods are the most commonly used in gas-in-oil diagnostics in addition to IEC 60599. The key gas method identities the key gas for each type of fault and uses the percent of this gas to diagnose the fault. Key gases formed by degradation of oil and paper insulation are hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon monoxide (CO)

4

and oxygen (02). Except for carbon monoxide and oxygen, all these gases are formed from the degradation of the oil itself. Acetylene is mainly associated with arcing, where temperatures reach several thousand degrees, ethylene with hot spots between 150°C and 1000°C and hydrogen with the partial discharges. Gas type and amounts are determined by where the fault occurs in the transformer and the severity and energy of the event. The IEC-standard 60599 is a guide describing how the concentrations of dissolved gases or free gases may be interpreted to diagnose the conditions of oil-filled electrical equipment in service and suggest further action. In addition to threshold values given in the standard, it is suggested that empirical values characteristic to the device type could be used. According to the recommendations of IEC 60599, the existing method for the interpretation of gas analysis is based on the ratio of concentration of CH4/H2, C2H2/C2H4, and C2H4/C2H6 to evaluate the defect. These ratios are to be used when the concentration of at least one of the gases exceeds the limiting concentration for normal equipment. (Ward 2003)

Table 3. Examples of the interpretations of dissolved gas analysis (Aro et al. 2003)

Ratios of characteristics gases Fault type

42

22

HCHC

2

4

HCH

62

42

HCHC

Partial discharges insignificant <0,1 <0,2 discharges of low energy density >1 0,1-0,5 >1 discharges of high energy density 0,6-2,5 0,1-1 >2 Hot spots T< 300 ˚C insignificant insignificant <1 Hot spots 300 < T < 700 ˚C <0,1 >1 1-4 Hot spots T< 700 ˚C <0,2 >1 >4

Furan analysis Electrical aging of paper, which is an integral part of oil insulation (oil-barrier insulation, paper-oil insulation), can lead to the formation of light gases. The thermal influence on paper initiates dehydration processes, resulting in the formation of water and compounds related to furans. The presence of oxygen and increased temperature initiate oxidizing reactions in the cellulose insulation. (Arakelian 2002) The five most prevalent derivatives of furan that arise from the degradation of the cellulose and that are soluble in the oil are 2-Furaldehyde, Furfuryl alcohol, 2-Acetylfuran, 5-Methyl-2-furaldehyde and 5-Hydroxymethyl-2-furaldehyde. A sample of the oil is extracted with either another liquid such as acetonitrile or with a solid phase extraction device. The extract is then analyzed using liquid chromatography. The five compounds mentioned above are separated on an appropriate column and each is detected by use of an ultraviolet detector that is adjusted automatically to the appropriate wavelength for each of the five components. Calibration solutions are made up for each of the components to be analyzed and these are used to standardize the instrument responses. From the data on the standard solutions, the extraction efficiencies for each component can be calculated and corrections can be made accordingly. The results are usually reported in terms of parts per billion (ppb). (NTT)

5

Moisture/Water content During the service life of an oil-filled equipment, the moisture content may increase by breathing damp air, natural ageing of the cellulose insulation, oil oxidation, condensation or by accidental means and the water content of the paper may rise to five or even six percent. The presence of moisture increases the ageing rate of both the oil and the paper. Insulating paper with a one percent moisture content ages ten times faster than one with only 0.1 %. Water is a polar liquid and is attracted to areas of strong electrical field. Water-soluble acids produced by oxidation of the oil act as a catalyst for almost all reactions and will combine with water or oil to assist or promote corrosion to exposed metal parts in the equipment. The cellulose has a greater affinity for water than oil and so water will replace the oil in oil-impregnated cellulose. Presence of water in the oil will reduce the electrical strength of the oil and may shorten life of the insulation system and lead to early transformer failure. Like other oil properties, the moisture content should be monitored regularly. (Pahlavanpour & Roberts 1998) A number of techniques have been investigated over the years to measure the quantity of moisture in a dielectric fluid, but the only method which has stood the test of time is that developed by Karl Fischer in the early 1930's. The method is outlined in IEC 60814; Insulating liquids – Oil-impregnated paper and pressboard – Determination of water by automatic coulometric Karl Fischer titration. Titration is a chemical analysis that determines the content of a substance, such as water, by adding a reagent of known concentration in carefully measured amounts until a chemical reaction is complete. There are two types of Karl Fischer titrators: volumetric and coulometric titrators. The main difference between the two is that with the volumetric method, the titrant is added directly to the sample by a burette. Conversely, with the coulometric method, the titrant is generated electrochemically in the titration cell. The coulometric method measures water levels much lower than the volumetric method. Measurements by coulometric Karl Fischer moisture meters, is quick and can be carried out by relatively unskilled staff. It is capable of measuring moisture contents down to 1 ppm or 0,0001% in the oil and can perform field analysis. The most obvious direct benefit of a portable moisture meter is the elimination of the possibility of further contamination that might occur while a sample is being transferred to a laboratory for analysis. (Pahlavanpour & Roberts 1998, Poynter & Barrios 1994) Estimates of moisture content of the cellulose can be made by relating water content of the oil in ppm to the % concentration of water in the cellulosic insulation. However, this requires knowledge of the moisture equilibrium data for the oil in question together with its normal temperature and moisture content. Also, for a long time, water determination has not been a problem for gas chromatography. The water extraction from transformer oil occurs simultaneously with the extraction of gases. The maximum allowable water content of oil in service depends on the transformer voltage, recommended value of 30 ppm at delivery is given in IEC 60296 for new oil. (Pahlavanpour & Roberts 1998, Poynter & Barrios 1994)

6

Acidity/Neutralization Number (NN) The acidity of an oil sample is related to the deterioration of the oil. New oils contain practically no acids if properly refined. The acidity test measures the content of acids formed through oxidation. The oxidation products polymerize to form sludge which then precipitates out. Acids react with metals on the surfaces inside the tank and form metallic soaps, another form of sludge. The presence of these acidic materials can be quantitatively determined by a procedure called titration. The amount of a standardized base that is needed to neutralize the acidic materials present in a known quantity of an oil sample is determined. The result referred to as the acid number (formerly referred to as neutralization number) equals the milligrams of KOH (potassium hydroxide) required to neutralize the acid contained in 1 gram of oil. The titration procedure can be done either volumetrically or gravimetrically and the end point can be determined either colorimetrically or potentiometrically (IEC 62021-1). With old oils, the colourimetric determination is sometimes difficult because of the dark colour of the oil (Myers 1998). The maximum neutralization value given by the IEC 60296 is 0,03 mgKOH/g. (NNT) Interfacial Tension The interfacial tension (IFT) test is employed as an indication of the sludging characteristics of oil (soluble polar contaminants and products of deterioration). In this procedure the surface tension of the oil is measured against that of water, which is highly polar. The more nearly the two liquids are alike in their polarity the lower the value of the surface tension between them. Thus the higher the concentration of hydrophilic materials in the insulating fluid, the lower will be the interfacial tension of the oil measured against water. The attraction between the water molecules at the interface is influenced by the presence of polar molecules in the oil in such a way that the presence of more polar compounds causes lower IFT. The test measures the concentration of polar molecules in suspension and in solution in the oil and thus gives an accurate measurement of dissolved sludge precursors in the oil long before any sludge is precipitated. There are several methods that can be used to measure the interfacial tension of oil against water. One method measures the size of a drop of water that is formed below the surface of the oil, however, if more accurate values are needed it is recommended that the Nouy ring method is used. The method involves placing a clean, platinum wire ring on the surface of the oil, where the force required to pull the ring away from the surface is measured. The method uses a tensiometer and a platinum ring. The ring is lowered into a beaker of water and oil. It is then brought up to the water-oil interface where the actual measurement takes place. The force required to pull the ring through the interface is measured by the tensiometer and considered to be the interfacial tension of the oil. The value for mineral oil varies from 24 to 40 dynes/cm (IEEE C57.106-1991). (NNT)

7

Dielectric dissipation factor (tan δ) The power factor of insulating oil is the cosine of the phase angle between a sinusoidal potential applied to the oil and the resulting current. This can be measured for example using Schering bridge. For insulating oils the value for this characteristic is called the power factor, loss tangent or dissipation factor and is expressed at a specified temperature. Power factor indicates the dielectric loss of an oil; thus the dielectric heating. Oxidation and contamination of oil can cause dissipation factor of an oil to rise, so determination of this property may provide useful information about used electrical insulating oil. Since these values vary with temperatures, comparisons must always be made at the same temperature. Test methods are outlined in IEC 61620 (Insulating liquids – Determination of the dielectric dissipation factor by measurement of the conductance and capacitance – Test method) and IEC 60247 (Measurement of relative permittivity, dielectric dissipation factor and d.c. resistivity of insulating liquids). The maximum value for dissipation factor is determined in IEC 60296 to be 0,005 at 90 ˚C (50 Hz). (Aro et al. 1996) Electrical Breakdown Strength The breakdown voltage is indicative of the amount of contaminant (usually moisture) in the oil. The effect of moisture in insulation oil increases when there are impurities present in oil. Also the oil temperature affect to the breakdown strength. Testing electrical breakdown strength begins by immersing two electrodes in a sample of the oil, then applying an AC voltage across the electrodes. The voltage is then increased in a specified manner until electrical breakdown occurs. The various tests used differ in electrode spacing and shape, rate of increase of voltage (and durance of test in DC voltage) and thus give different breakdown values for the same oil. Electrical breakdown strength for new, clean transformer oil in AC measurements is over 60 kV/2,5 mm (effective value) and the breakdown strength is independent of the oil brand. Testing is standardized in IEC 60156. Oil is not necessarily in good condition even when the dielectric strength is adequate because this tells nothing about the presence of acids and sludge. IEC 60296 determines minimum value for AC breakdown voltage at delivery to be 30 kV and 50 kV after treatment. (NNT, Aro et al. 1996) Resistivity The resistivity of electrical insulating oil is a measure of the resistance to DC current flow between conductors. The resistivity of mineral insulating oil is naturally high but, as with loss tangent, is very sensitive to the presence of even minute amounts of suspended water, free ions or ion forming materials such as acidic oxidation products or polar contaminants. (Aro et al. 1996)

8

Flash point Flash point is an indication of the combustibility of the vapors of a mineral oil and is defined as the lowest temperature at which the vapor of oil can be ignited under specified conditions. The flash point is considered to be the lowest temperature at which the oil vapors will ignite, but not sustain a flame. Impurities in oil lower the flash point. Usual method for flash point determination is Pensky Martens closed cup flash point test. IEC 60296 determines the minimum value for flash point to be 140 ˚C for higher viscosity mineral oil and 130 ˚C for lower viscosity mineral oil. Method used is ISO 2719. Analysis of antioxidant Oxidation inhibitors in mineral oils readily react with oxygen at elevated temperatures to first form hydroperoxides, then organic acids. These compounds lead to viscosity increase, formation of sludge, discoloration, acidic odor and corrosion of metal parts. Oxidation resistance may be due to natural inhibitors or commercial additives. Four types of oxidation inhibitor additives are zinc dithiophosphates, aromatic amines, alkyl sulfides and hindered phenols. Metal surfaces and soluble metal salts, especially copper, usually promote oxidation. Therefore, another approach to inhibiting oxidation is to reduce the catalysis by deactivating the metal surfaces. The effectiveness of the anti-oxidants in delaying oil oxidation can be measured by laboratory tests known generally as oxidation stability tests. Oxidation stability is measured in accelerated tests at high temperature, in the presence of excess oxygen, catalysts and possibly water. Results are expressed as the time required to reach a predetermined level of oxidation. Criteria can be a darkening color, the amount of sludge, gum, acids and the amount of oxygen consumed and in some cases by the depletion of the anti-oxidant chemical compound itself. The maximum value given by the IEC 60296 for sludge is 0,10 % by mass or 0,40 mgKOH/g for neutralization value. Method used is IEC 1125. (Godfrey & Herguth 1995) Viscosity Viscosity is the resistance of oil to flow under specified conditions. The viscosity of oil used as a coolant influences heat transfer rates and consequently the temperature rise of an apparatus. Low viscosity ensures that oil flows well in particularly in low temperatures and helps quenching arcs. The viscosity of oil also influences the speed of moving parts in tap changers and circuit breakers. The IEC 61868 specifies a procedure for the determination of the kinematic viscosity of mineral insulating oils, both transparent and opaque, at very low temperatures, after a cold soaking period of at least 20 h, by measuring the time for a volume of liquid to flow under gravity through a calibrated glass capillary viscometer. The number of seconds the oil takes to flow through the calibrated region is measured. The oil's viscosity in cSt is the flow time in seconds multiplied by the apparatus constant. It is particularly suitable for the measurement of the kinematic viscosity of liquids for use in cold climates, at very low temperatures (–40 °C) or at temperatures between the cloud and pour-point temperatures (typically –20 °C) where some liquids may develop

9

unexpectedly high viscosities under cold soak conditions. IEC 60296 determines the minimum value for viscosity to be 16,5 cSt at 20 ˚C and 800 cSt at -15 ˚C for higher viscosity mineral oil. For lower viscosity mineral oil the values are 11,0 cSt and 1800 cSt. (Godfrey & Herguth 1995) Color and appearance Mineral oil fresh from the refinery is essentially colorless. As the sample ages over time or is subjected to severe conditions such as local hot spots or arcing the sample will become darker in color. The clarity of a fresh virgin sample of oil should be sparkling with no indication of cloudiness, sludge, or particulate matter. The clarity of an oil sample is determined by observation of the sample when illuminated by a narrow focused beam of light. The color of a sample is determined by direct comparison to a set of color standards. Also the assessment of the smell of oil from different parts of a transformer can be a valuable first indicator of the source and type of fault (Myers 1998). It should be pointed out that the color of the oil by itself should never be used to indicate the quality of the oil, rather it can be used to determine whether more definitive tests should be done. (NNT) The insulation oil condition can also be determined by its fibre and particulate content. A simple count of visible fibres can be made using a crossed Polaroid viewing system with results usually reported in terms of small (<2 mm), medium (2 - 5 mm) and large fibres. The range of 2 - 5 mm includes most cellulose fibres derived from the paper insulation whereas larger fibres are usually contaminants introduced either during maintenance or sampling. These results, in conjunction with water content, can give an indication of the cause of poor electrical strength. Accurate fibre and particulate counts require carefully controlled filtration in clean conditions followed by microscopic examination and are only needed for very high quality insulation is required. (Myers 1998) CONCLUSIONS Practical diagnostics of oil filled equipment are executed in a standard manner, see picture 1. Sample of oil is taken regularly from the equipment to enable the early determination of developing defects. If all values remain below the limiting values, the condition of the insulation is considered to be satisfactory. For abnormal values, the analysis is repeated to confirm the results, and to calculate the rate at which the defect is developing. If the abnormal results are not confirmed, or the dynamics of the development of the defect are absent, normal operation can be assumed, especially if additional tests to determine the electrical, physical, physico-chemical, and chemical characteristics of the oil are normal. On confirmation of a problem, the type of defect—thermal or electrical—and its severity are determined, and a decision is made on further checking by means of monitoring or frequent gas chromatographic analysis. The recommendation for refurbishment, repair, or replacement is made on the basis of the data accumulated (IEC 60422, Supervision and maintenance guide for mineral insulating oils in electrical equipment) (Arakelian 2002)

10

Scheduled Periodic Inspection of OFEE in Service

Wsatotcdaea

Electrical GC-Analysis of Oils from OFEE for the Concentrations of Dissolved Gases

and Water

Thermovision Control Measurements

No Deviations Present Deviation from Normal,

Assume Defect Present

Repeat GC –Analysis for

Concentrations of Gases and Water

Additional Selective Measurements:

1. Tanδ, UBreakdown, ρv 2. Furfural,

Antioxidant 3. d20, n20, σ20 4. Acidity

Establish or Confirm Defect Present and Determine its

Rate of Development

Continue in Service

Decision on Type of Monitoring

Monitoring

Decision on Periodic GC-Control

The Decision on Scale, Type and Expediency of Repair

Repair

Possible Problems

Problems No Problems

Continue in Service

P (

icture 1. Ideology and tactics of oil-filled electronic equipment (OFEE) diagnostic checkArakelian 2002).

e are dealing or talking about oil diagnostics only when information on the owner, oil ampling, oil testing results, technical and operational data and history of oil maintenance ctions, are gathered periodically with expert opinion about suitability of the tested oil filling ogether with guidelines for any needed corrective actions (filtering, adding inhibitor, exchange r reclaiming). For the optimal oil diagnostics, the expert opinion should take into account also he owners strategic lifetime of equipment and his maintenance and investment strategy, as ritical values of specific test results (degree of degradation), at which some actions should be one, depend on these parameters. Having all cited information the diagnostic expert should also dvise the techno-financial optimal frequency and type of oil testing. The highly experienced xpert having enough information can reduce expenses for equipment supervision, maintenance nd refurbishment. (Gradnik 2002)

11

REFERENCES (Arakelian 2002) Arakelian, V.G, 2002. Effective Diagnostics for Oil-Filled

Equipment. IEEE Electrical Insulation Magazine, November/December 2002 – Vol. 18, No. 6.

(Aro et al. 1996) Aro, M., Elovaara, J., Karttunen, M., Nousiainen, K., Palva, V.,

1996. Suurjännitetekniikka. Otatieto 568. Jyväskylä 2003. ISBN 951-672-320-9.

(Barnes 2002) Barnes, M. 2002. Gas Chromatography: The Modern Analytical

Tool. Practicing Oil Analysis Magazine. Available: www.practicingoilanalysis.com/article_detail.asp?articleid=352&relatedbookgroup=OilAnalysis

(Gradnik 2002) Gradnik, M.K., 2002. Physical-Chemical Oil Tests, Monitoring and

Diagnostics of Oil-Filled Transformers. Proceedings of 14th International Conference on Dielectric Liquids, Austria.

(Godfrey & Herguth 1995) Godfrey D. & Herguth W. R. 1994. Physical and Chemical

Properties of Mineral Oil That Affect Lubrication. Herguth Laboratories. Available: www.herguth.com/technical/PHYSICAL.HTM

(Myers 1998) Myers, C., 1998. Transformers – Conditioning Monitoring by Oil

Analysis Large or Small; Contentment or Catastrophe. Power Station Maintenance: Profitability Through Reliability, Conference Publication No. 452.

(NNT) Northern Technology & Testing. Available:

www.nttworldwide.com (Pahlavanpour et al. 1999) Pahlavanpour, B., Wilson, G., Heywood, R. 1999. Insulating Oil in

Service: Is It Fit for Purpose? The Institution of Electrical Engneers.

(Pahlavanpour & Roberts 1998)

Pahlavanpour B., Roberts I. A., 1998, Transformer Oil Condition Monitoring. The Institution of Electrical Engneers.

(Poynter & Barrios 1994) Poynter W.G & Barrios R.J. 1994. Coulometric Karl Fischer

titration simplifies water content testing. Oil & Gas Journal. Available : www.kam.com/techcenter-karlfischer.htm

(Saha 2003) Saha, T. 2003. Review of Modern Diagnostic Techniques for

Assessing Insulation Condition in Aged Transformers). IEEE Transactions on Dielectrics and Electrical Insulation.

12

(Ward 2003) Ward S.A., 2003. Evaluating Transformer Condition Using DGA

Oil Analysis. 2003 Annual Report Conference in Electrical Insulation and Dielectric Phenomena.

13

Annex 1

Gas-Liquid Chromatography In gas-liquid chromatography, it is the interaction between the gaseous sample (the mobile phase) and a standard liquid (the stationary phase), which causes the separation of different molecular constituents. The stationary phase is either a polar or nonpolar liquid, which, in the case of capillary column, coats the inside of the column, or is impregnated onto an inert solid that is then packed into the GC column.

Figure 2. Gas Chromatography Instrument.

A schematic layout of a GC instrument is shown in figure 2. The basic components are an inert carrier gas, most commonly helium, nitrogen or hydrogen, a GC column packed or coated with an appropriate stationary phase, an oven that allows for precise temperature control of the column and some type of detector capable of detecting the sample as it exits or elutes from the column. Gas-liquid chromatography works because the molecules in the samples are carried along the column in the carrier gas, but partition between the gas phase and the liquid phase. Because this partitioning is critically dependent on the solubility of the sample in the liquid phase, different molecular species travel along the column and elute at different times. Those molecules that have a greater solubility in the liquid phase take longer to elute and thus are measured at a longer interval. Solubility is dependent on the physical and chemical properties of the solute; therefore, separation between different components of the sample occurs based on molecular properties such as relative polarity (like ethylene glycol versus base oil) and boiling point (like, fuel versus diesel engine base oil). For example, using a polar stationary phase, with a mixture of polar and nonpolar compounds will generally result in longer elution times for the polar compounds, because they will have greater solubility in the polar stationary phase. (Barnes 2002)

14

1

GAS DIAGNOSTICS IN TRANSFORMER CONDITION MONITORING

Pauliina Salovaara Tampere University of Technology, Institute of Power Engineering

[email protected] ABSTRACT Transformers are vital components in both the transmission and distribution of electrical power. The early detection of incipient faults in transformers reduces costly unplanned outages. The most sensitive and reliable technique for evaluating the health of oil filled electrical equipment is dissolved gas analysis (DGA). Insulating oils under abnormal electrical or thermal stresses break down to liberate small quantities of gases. The qualitative composition of the breakdown gases is dependent upon the type of fault. By means of dissolved gas analysis (DGA), it is possible to distinguish faults such as partial discharge (corona), overheating (pyrolysis) and arcing in a great variety of oil-filled equipment. Information from the analysis of gasses dissolved in insulating oils is valuable in a preventative maintenance program. A number of samples must be taken over a period of time for developing trends. Data from DGA can: Provide advance warning of developing faults, provide a means for conveniently scheduling repairs and monitor the rate of fault development. INTODUCTION Monitoring and maintenance of mineral-oil-filled power transformers are of critical importance in power systems. Failure of a power transformer may interrupt the power supply and result in loss of profits. Therefore, it is of great importance to detect incipient failures in power transformers as early as possible, so that we can switch them off safely and improve the reliability of power systems. If a long in-service transformer is subjected to higher than normal electrical and thermal stresses, it may generate by-product gases due to the incipient failures. Dissolved gas analysis (DGA) is a common practice for incipient fault diagnosis of power transformers and widely accepted as the most reliable tool for the earliest detection of inception faults in transformers and other electrical equipments using insulating oil. [7,8] The utility tests and periodically samples the insulation oil of transformers to obtain the constituent gases in the oil, which are formed due to breakdown of the insulating materials inside. Gas-in-oil analysis by gas chromatography has proven to be predictive and valuable some of the problems, which could progress to catastrophic failures in transformers. Problems that can be detected are: arcing, corona, and both overheated oil and cellulose degradation. These problems result in gas production as they start to develop and gas production increases with increasing severity of the problem. The energy dissipation is the least in corona, medium in overheating, and highest in arcing. According to the IEC Standard (Publication 567), nine dissolved gases can be determined from a DGA test (i.e., hydrogen (H2), oxygen (O2), nitrogen (N2) methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon monoxide (CO), and carbon dioxide (CO2)). Therefore, if we can relate the future gas content of transformers with the faults, then forecasting fault conditions for power transformers will be easily done. Future prediction of fault conditions is the most import information for maintenance engineering group to avoid system outages. In the past, various fault diagnosis

2

techniques have been proposed, including the conventional key gas method, gas ratio method, expert systems, neural networks (NN), and fuzzy logic approaches. Recently, the combinations of fuzzy logic and artificial intelligence (AI) have given promising results in the fault analysis. [7,8] METHODS OF GAS DETECTION Three different methods of gas detection will be discussed and their advantages and disadvantages will be compared. The first method is the one that determines the total combustible gases (TCG) that are present in the gas above the oil. The major advantage of the TCG method compared to the others that will be covered is that it is fast and applicable to use in the field. In fact it can be used to continuously monitor a unit. However, there are a number of disadvantages to the TCG method. Although it detects the combustible fault gases (hydrogen, carbon monoxide, methane, ethane, ethylene, and acetylene), it does not detect the non-combustible ones (carbon dioxide, nitrogen, and oxygen). This method is only applicable to those units that have a gas blanket and not to the completely oil-filled units of the conservator type. Since most faults occur under the surface of the oil, the gases must first saturate the oil and diffuse to the surface before accumulating in the gas blanket above the oil. These processes take time, which delays the early detection of the fault. The major disadvantage of the TCG method is that it gives only a single value for the percentage of combustible gases but does not identify which gases are actually present. It is this latter information that is most useful in determining the type of fault that has occurred. [1] The second method for the detection of fault gases is the gas blanket analysis in which a sample of the gas in the space above the oil is analyzed for its composition. This method detects all of the individual components; however, it is also not applicable to the oil-filled conservator type units and it also suffers from the disadvantage that the gases must first diffuse into the gas blanket. In addition, this method is not at present best done in the field. A properly equipped laboratory is preferred for the required separation, identification, and quantitative determination of these gases at the part per million level. [1] The third and most informative method for the detection of fault gases is the dissolved gas analysis (DGA) technique. In this method a sample of the oil is taken from the unit and the dissolved gases are extracted. Then the extracted gases are separated, identified, and quantitatively determined. At present this entire technique is best done in the laboratory since it requires precision operations. Since this method uses an oil sample it is applicable to all type units and like the gas blanket method it detects all the individual components. The main advantage of the DGA technique is that it detects the gases in the oil phase giving the earliest possible detection of an incipient fault. This advantage alone outweighs any disadvantages of this technique. [1] FAULT GASES Insulating materials within transformers and related equipment break down to liberate gases within the unit. The distribution of these gases can be related to the type of electrical fault and the rate of gas generation can indicate the severity of the fault. The identity of the gases being generated by a particular unit can be very useful information in any preventative maintenance program. The causes of fault gases can be divided into three categories; corona or partial

3

discharge, pyrolysis or thermal heating, and arcing. These three categories differ mainly in the intensity of energy that is dissipated per unit time per unit volume by the fault. The most severe intensity of energy dissipation occurs with arcing, less with heating, and least with corona. [1] Mineral insulating oils are made of a blend of different hydrocarbon molecules containing CH3, CH2 and CH chemical groups linked together by carbon-carbon molecular bonds. Some of the C-H and C-C bonds may be broken as a result of electrical and thermal faults, with the formation of small unstable fragments, in radical or ionic form, which recombine rapidly through a complex reaction, into gas molecules such as hydrogen, methane, ethane, ethylene, acetylene, C3, and C4 hydrocarbon gases, as well as solid particles of carbon and hydrocarbon polymers (X-wax), are other possible recombination products. [9] Fault gases that can be found within a unit are listed in the following three groups:

1. Hydrocarbons and hydrogen: methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), hydrogen (H2) 2. Carbon oxides: carbon monoxide (CO), carbon dioxide (CO2) 3. Non-fault gases: nitrogen (N2), oxygen (O2)

Except for carbon monoxide, nitrogen and oxygen, all these gases are formed from the degradation of the oil itself. Carbon monoxide, carbon dioxide (CO2), and oxygen are formed from degradation of cellulose (paper) insulation. [8] Faults can be identified based on the formed fault gases. Below is a list of fault and the main fault gases [10]

o Arcing: Large amounts of hydrogen and acetylene are produced, with minor quantities of methane and ethylene. Key Gas: Acetylene

o Corona: Low-energy electrical discharges produce hydrogen and methane, with small quantities of ethane and ethylene. Key Gas: Hydrogen

o Overheated Oil: Decomposition products include ethylene and methane, together with smaller quantities of hydrogen and ethane. Key Gas: Ethylene

o Overheated Cellulose: Large quantities of carbon dioxide and carbon monoxide are evolved. Key Gas: Carbon monoxide

Figures 1, 2, 3, and 4 illustrate the chemical processes occurring with corona, pyrolysis, and arcing in oil and pyrolysis of cellulose respectively. Typical fault gas distributions are also shown. [1]

4

Figure 1. Corona in Oil

H2 88 % C02 1 % C0 1 % CH4 6 % C2H6 1 % C2H4 0.1 % C2H2 0.2 %

Figure 2. Pyrolysis in Oil

H2 16 % C02 trace C0 trace CH4 16 % C2H6 6 % C2H4 41 % C2H2 trace

Figure 3. Arcing in Oil

H2 39 % C02 2 % C0 4 % CH4 10 % C2H4 6 % C2H2 35 %

Figure 4. Pyrolysis of Cellulose

H2 9 % C02 25 % C0 50 % CH4 8 % C2H4 4 % C2H2 0.3 %

IEC PUBLICATION New IEC Publication 60599 Ed. 2.0 b: Mineral oil-impregnated electrical equipment in service - Guide to the interpretation of dissolved and free gases analysis, concerning the interpretation of dissolved gas-in-oil analysis, was issued in 1999 as a result of the revision of IEC TC 10 of the previous IEC Publication 599, issued in 1978. It describes how the concentrations of dissolved gases or free gases may be interpreted to diagnose the condition of oil-filled electrical equipment in service and suggests future action. It is applicable to electrical equipment filled with mineral insulating oil and insulated with cellulosic paper or pressboard-based solid insulation. Information about specific types of equipment such as transformers (power, instrument, industrial, railways, distribution), reactors, bushings, switchgear and oil-filled cables is given only as an indication in the application notes. Publication may be applied only with caution to other liquid-solid insulating systems. In any

5

case, the indications obtained should be viewed only as guidance and any resulting action should be undertaken only with proper engineering judgment. [3] The main body of IEC Publication 60599 contains an in-depth description of the five main types of faults usually found in electrical equipment in service. The familiar gas ratios have been retained for the diagnoses, but with new code limits, while additional gas ratios are suggested for specific fault cases. More precise definitions of normal and alarm gas concentrations in service are indicated compared to old one. In the Annexes, examples of typical (normal) gas concentration values observed in service are given for six different types of equipment. Extensive databases of faulty equipment inspected in service and of typical normal values related to various types and ages of equipment and types of faults have been used for the revision of Publication 60599. [3] Classification of faults in IEC Publication 60599 is according to the main types of faults that can be reliably identified by visual inspection of the equipment after the fault has occurred in service [3]:

o partial discharges (PD) of the cold plasma (corona) type with possible X-wax formation, and of the sparking type inducing small carbonized punctures in paper.

o discharges of low energy (D1), evidenced by larger punctures in paper, tracking, or carbon particles in oil.

o discharges of high energy (D2), with power follow-through, evidenced by extensive carbonization, metal fusion, and possible tripping of the equipment.

o a thermal fault below 300 ºC if paper has turned brownish (T1), above 300 ºC if paper has carbonized (T2).

o thermal faults above 700 ºC (T3), evidenced by oil carbonization, metal coloration, or fusion.

The number of characteristic faults is thus reduced from nine in the previous IEC Publication 599 to five in new IEC Publication 60599. In the Table 1 there are shown the old IEC Publication 599 gas ratio codes and the nine fault types that can be detected according to the codes. Identification of Faults in Service Using New IEC Publication 60599 The three basic gas ratios of IEC 599 (C2H2/C2H4, CH4/H2, and C2H4/C2H6) are also used in IEC 60599 for the identification of the characteristic faults. The ratio limits have been made more precise, using the data of Annex 1 in IEC 60599, in order to reduce the number of unidentified cases from around 30 % in IEC 599 to practically 0 %. Unidentified cases occurred in IEC 599 when ratio codes calculated from actual DGA results would not correspond to any of the codes associated with a characteristic fault. Graphical methods that allow one to follow more easily and more precisely these cases, as well as the evolution of faults with time, are also described. A more detailed version of the Triangle method, updated from a previously published version, is also included to the publication. [3]

6

Table 1. Old IEC Publication 599 gas ratio codes and fault types according to the codes. [7]

In addition to the three basic interpretation ratios, two new gas ratios have been introduced in IEC 60599 for specific diagnoses: the C2H2/H2 ratio, to detect possible contamination from the on-load tap changers (OLTC) compartment (when > 3), and the O2/N2 ratio, to detect abnormal oil heating/oxidation (when <0.3). The limit below which theCO2/CO ratio indicates a possible involvement of paper in the fault has been made more precise (< 3). Finally, other sources of gas, not related to a fault in service (mainly, H2) are also indicated. [3] Triangle Method The Triangle graphical method of representation is used to visualize the different fault cases and facilitate their comparison. The coordinates and limits of the discharge and thermal fault zones of the Triangle are indicated in Figure 5. Zone DT in Figure 5 corresponds to mixtures of thermal and electrical faults. Readers interested in visualizing their own DGA results using

7

the Triangle representation should preferably use triangular coordinate drawing paper for better precision. The Triangle coordinates corresponding to DGA results in ppm can be calculated as follows: %C2H2 = 100x / (x+y+z); %C2H4 = 100y / (x+y+z); %CH4 = 100z / (x+y+z), with x = (C2H2); y = (C2H4); z = (CH4), in ppm. [4]

Figure 5. Coordinates and fault zones of the Triangle. [4] ON-LINE MONITORING Gas chromatographic diagnostics of power transformers has been recognised in the world for some time as the most efficient physical-chemical on-line diagnostic method for determination of potential thermal or electrical faults in transformers. Today the method is increasingly complemented by sensor on-line monitoring of transformers. This analysis is described as an on-line diagnostic testing method because the sampling of oil as well as the performance of the analysis can be carried out during normal operation of the power transformer, in other words, without temporary disconnection. [9] Monitoring of the state of transformers with various on-line sensors built directly into transformers or their insulation has been gradually put forward for the detection of disturbances and sudden faults. Operation of these sensors is based on the measurement of different signals. The development of on-line sensors for monitoring the state of transformers proceeds in two main directions. - Sensors for a certain typical gas (hydrogen, acetylene), which enable us to monitor the development trends of the gas in question. Certain sensors draw attention to specific disturbances. Hydrogen sensors thus draw attention to partial discharges and discharges with low energy, whereas e.g. acetylene sensors draw attention to discharges with high energy.

8

Neither of the two types of sensor mentioned draws attention to a thermal disturbance or a thermal damage of paper insulation. - More complex sensors, analytical instruments in their own right, detect all gases typical of certain faults in transformers. These draw attention to disturbances in transformers to the same extent as results of a DGA and should allow for on-line diagnostics. However, due to their complexity these instruments belong to an entirely different price class and their application makes sense only in special cases. Examples of such analysers include Transformer Nursing Unit (TNU) - a mobile unit for temporary on-line monitoring of typical gases in critical situations, and Transformer Monitoring & Management System (TMMS) developed for permanent installation in more important transformers. [9] Gas chromatographic diagnostics of power transformers based on DGA has been improved since it was introduced, and it still holds the most important place in the monitoring of transformers in on-line diagnostics. Experience has led to the conclusion that, in order to make a proper diagnosis in interpretation of results of a DGA, it is necessary first for each individual transformer to establish “normal” concentrations and, where these are exceeded, to use the method of ratios to determine the type of fault. In addition to these typical ratios, it is necessary to consider the speed of development of gases, the possibility of contamination and numerous data from the operation and maintenance of a transformer. On-line gas monitoring is being increasingly used to complement this. [9] METHODS OF INTERPRETATION Dissolved gas analysis (DGA) has long been the standard on-line tool used by engineers to determine the condition of power transformer. The popularity of DGA stemmed from the fact that testing is performed without disrupting the transformer’s operation. When DGA was conceived back in the 1960s, it was heralded as huge success with millions of pounds of losses being avoided by early detection of faults. However, its failures were almost as spectacular as it successes and it soon became apparent that DGA was by no means a complete solution. Many attempts have since been made to refine the decision process used to guide DGA engineers in their evaluations; such attempts include expert systems and analysis of the data using e.g. artificial neural networks. Typically, when a DGA engineer examines DGA data, they will compare the values that they have with the decision rules of several traditional analysis methods. What the engineer then does is to make subjective decisions and allowances based on what they see, i.e., does the data fit any of the decision criteria, if not how close is the data to those criteria? [5] Existing approaches for the interpretation of dissolved gas data relate gaseous composition to the condition of power transformers via the utilization of ratio-based schemes or artificial intelligence (AI) techniques. However, these approaches still contain some limitations. [6]

o Conventional Approaches Several renowned DGA interpretation schemes are, for example, Dörnenburg Ratios, Rogers Ratios, Duval Triangle, and the IEC Ratios. These schemes have been implemented, either in modified or improvised format, by various power utilities throughout the world. The implementation of these schemes requires the computation of several key gas-ratios. Fault diagnosis is accomplished by associating the value of these ratios with several predefined conditions of power transformers. Before subjecting the dissolved gas data for interpretation, a decision has to be made on whether fault diagnosis is necessary based on the comparison of

dissolved gas concentrations with a set of “benchmark” concentration values, which are also referred to as the typical values of gas concentration. If all dissolved gas concentrations are below these typical values, then the power transformer concerned can be regarded as operating in a faultless manner. These “benchmark” values should be calculated from a large historical DGA database, if available, based on the 90% or 95% thresholds. Although well received by power utilities, there are several limitations pertaining to the foregoing ratio-based approaches. [6] Table 2. Key gas ratios of conventional DGA interpretation schemes. [6]

o AI Approaches Attempts have been made to utilize arof transformer condition based on approaches is to resolve some inherenand to improve the accuracy of diagnoone AI technique. The most commoneural network (NN). Other AI appnature. Hybrid-AI approaches such asystem (ES) and NN are more promistackle the ambiguity of conventionalthe foregoing approaches, and expertof diagnosis. [6] Conventional DGA theory The most important aspect of fault gacorrectly diagnosing the fault that is traditional DGA methods are (i) RoMethod (DRM) and (iii) the Key Gatheory routed in organic chemistry generated by a fault to a general faulfault temperature within a prescribtemperature. Because the insulating oprimarily of hydrocarbons), certain ranges, therefore allowing the traditrange and therefore the possible fault

9

tificial intelligence (AI) techniques to perform diagnosis the dissolved gas information. The intention of these t limitations of the conventional interpretation schemes sis. Single AI approaches only involve the utilization of n AI technique within this category is the supervised roaches applied for DGA interpretation are of hybrid s fuzzy expert system (FES) and the combined expert ing due to the fact that fuzzy logic (FL) or NN is used to DGA interpretation schemes, which are integrated into experiences are incorporated to improve the credibility

s analysis is taking the data that has been generated and generating the gases that have been detected. The three ger’s Ratio Method (RRM), (ii) Dornenburg’s Ratio s Method (KGM). All three of the methods have their

and base their diagnoses on matching the temperature t type. Put simply, each fault type typically generates a ed range, the more severe the fault the higher the il used in power transformers is organic (i.e., composed fingerprint gases are generated at specific temperature ional methods to identify a possible fault temperature type. The KGM actually uses four characteristic charts

10

that represent typical relative gas concentrations for four general fault types: overheating of cellulose (OHC), overheating of oil (OHO), partial discharge (PD) or arcing. The other two methods use ratios of the same fingerprint gases to try and pinpoint specific temperature ranges. The fingerprint gases used are again carbon monoxide (CO), hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4) and acetylene (C2H2). One of the earliest methods is that of Dornenburg in which two ratios of gases are plotted on log-log axes (Fig. 6). The area in which the plotted point falls is indicative of the type of fault that has developed. [1]

Figure 6. Dornenburg’s Ratio Method [1] Grey model In the past, forecasting approaches have mostly used time series like least-squares regression or neural network models like back propagation based neural networks. Generally, these traditional forecasting models need a large amount of input data. However, the DGA of a power transformer is usually done only once in every year by power companies due to inspection cost consideration, so the historical database is very limited. The traditional forecasting methods are not appropriate for application in this field. The grey dynamic model (GM model) is particularly designed for handling situations in which only limited data are available for forecasting while system environment is not well-defined and fully understood. The GM model has been proved successful in many forecasting fields. For example, due to few historical dissolved gas records (only one test value for a year), a modified grey model (MGM) is proposed to predict the trend of dissolved gases. Then, future faults of power transformers can be directly identified by the fault diagnosis techniques, so that we can switch them out safely and improve the reliability of power systems. [7] The tested results show that the proposed method is simple and efficient. The grey theory describes random variables as a changeable interval number that varies with time factors and uses ‘colour’ to represent the degree of uncertainty in a dynamic system. If a system whose information is completely clear is called as a white system. In opposition, if a system whose information is not clear at all is called as a black system. In other words, if a system whose

11

information is partly clear or partly unclear is called as a grey system. The grey forecasting model is one of the applications of the grey theory. Instead of analyzing the characteristics of the grey systems directly, the grey model exploits the accumulated generating operation (AGO) technique to outline the system behaviours. The AGO practice may reduce the white noise embedded in the input data from statistics. [7] Due to the lack of sampling data, the grey model is very useful to set up the accurate forecasting model, since it can work with very little data. According to the field test results, it is shown that the proposed method can, not only provide the high accuracy model of the transformer oil-dissolved gas; it can also combine with other fault diagnosis method to detect useful information for future fault analysis. In addition, the calculation of the proposed method is fast and very simple and can be easily implemented by PC software. [7] Fuzzy model The criteria used in dissolved gas analysis are based on crisp value norms. Due to the dichotomous nature of crisp criteria, transformers with similar gas-in-oil conditions may lead to very different conclusions of diagnosis, especially when the gas concentrations are around the crisp norms. To deal with this problem, gas-in-oil data of failed transformers were collected and treated in order to obtain the membership functions of fault patterns using a fuzzy clustering method. All crisp norms are fuzzified to linguistic variables and diagnostic rules are transformed into fuzzy rules. A fuzzy system originally is used to combine the rules and the fuzzy conditions of transformers to obtain the final diagnostic results. It is shown that the diagnosing results from the combination of several simple fuzzy approaches are much better than traditional methods especially for transformers which have gas-in-oil conditions around the crisp norms. [2] ER (evidential reasoning) Algorithm A novel approach to the analysis and handling of dissolved gas analysis (DGA) data from several traditional methods, namely Roger’s Ratio Method, Dornenburg’s Ratio Method and the Key Gas Method, is presented also in Spurgeon paper. Ideas taken from fuzzy set theory are applied to ‘soften’ the fault decision boundaries employed by each of the three methods. This has the effect of replacing traditional Fault or No Fault crisp reasoning diagnoses, with a set of possible fault types (i.e., those fault types distinguishable by one particular method) and an associated probability of fault for each. These diagnoses are then considered as pieces of evidence ascertaining to the condition of the transformer and are aggregated using an evidential reasoning (ER) algorithm. The results are presented as probabilities of four possible general fault types: overheating of cellulose (cellulose degradation), thermal faults, partial discharge and arcing (corona). Finally the remaining belief is assigned to the possibility that no fault exists. The results show that the pseudo fuzzy representations of the traditional methods, perform adequately over a wide range of test values taken from actual failed transformers, and that the overall system can effectively combine the evidence to produce a more meaningful and accurate diagnosis. [5] The new approach generates subjective judgments such as these by using fuzzy membership functions to soften the decision boundaries that are currently utilized by the traditional methods. More precisely, crisp decision boundaries imply that the probability of fault can

12

either be zero or one. Softening such boundaries using appropriate functions means that the probability of fault can take on any value in the closed interval [0, 1]. By changing the boundaries in this way, the belief that an engineer has that a transformer is faulty can be represented by a single value. [5] In the case of DGA there is uncertainty in the accuracy of the diagnoses provided by the traditional methods. What ER does is provide a mathematical framework for combining such uncertain information (and subjective judgments). By considering each piece of information as evidence either supporting or denying a hypothesis, the validity of all possible hypotheses can be calculated. In the case of DGA, each hypothesis corresponds to a possible fault condition and the validity to the chance that this may be the condition of the transformer, e.g. 20 % chance the transformer has suffered from or is currently suffering an arcing fault. [5] Test clearly shows the power of the ER algorithm to combine effectively all of the available evidence from the three diagnosis methods and provide an array of possible faults, mimicking the natural reasoning process of a DGA engineer. It also demonstrates the practicality of using fuzzy membership functions for generating subjective beliefs in a simple manner, based on only two mathematical functions. The potential of this system lies in the fact that, whereas other systems treat the problem as one of classification, ER treats the problem as one of reasoning based on the DGA data. The flexibility of the tree structure used to make the decision and the algorithm for combining the evidence means that the system can be extended easily to encompass new diagnosis techniques by simply adding extra branches, parallel to the ones currently used. [5] CONCLUSIONS The technology presently exists and is being used to detect and determine fault gases below the part per million level. However there is still much room for improvement in the technique, especially in developing the methods of interpreting the results and correlating them with incipient faults. It is also important to realize that even though there is further need for improvement in the technique, the analysis of dissolved fault gases represents a practical and effective method for the detection of incipient faults and the determination of their severity. In addition to utility companies, many industries and installations that have on-site transformers are recognizing that the technique of dissolved fault gas analysis an extremely useful, if not essential, part of a well developed preventative maintenance program. [1] Obvious advantages that fault gas analyses can provide are summarized blow [1]:

1. Advance warning of developing faults 2. Determining the improper use of units 3. Status checks on new and repaired units 4. Convenient scheduling of repairs 5. Monitoring of units under overload

13

REFERENCES [1] DiGiorgio, Joseph B., Dissolved Gas Analysis of Mineral Oil Insulating Fluids, NTT –

Technical Bulletin. 28.6.2005, available in www.nttworldwide.com/tech2102.htm [2] An-Pin Chen & Chang-Chun Lin, Fuzzy approaches for fault diagnosis of transformers,

Fuzzy Sets and Systems, 2001, Vol 118, No 1, pp 139-151. [3] Duval, M. & dePabla, A., Interpretation of gas-in-oil analysis using new IEC publication

60599 and IEC TC 10 databases, IEEE Electrical Insulation Magazine, 2001, Vol 17, No 2, pp. 31-41.

[4] Duval, M., A review of faults detectable by gas-in-oil analysis in transformers, IEEE Electrical Insulation Magazine, 2002, Vol 18, No 3, pp. 8-17.

[5] Spurgeon, K., Tang, W.H., Wu, Q.H., Richardson, Z.J. & Moss, G., Dissolved gas analysis using evidential reasoning, IEE Proceedings on Science, Measurement and Technology, 2005, Vol 152, No 3, pp. 110 – 117.

[6] Thang, K.F., Aggarwal, R.K., McGrail, A.J. & Esp, D.G., Analysis of power transformer dissolved gas data using the self-organizing map, IEEE Transactions on Power Delivery, 2003, Vol 18, No 4, pp. 1241-1248

[7] Wang, M. H. & Hung, C. P., Novel grey model for the prediction of trend of dissolved gases in oil-filled power apparatus, Electric Power Systems Research, 2003, Vol 67, No 1, pp. 53-58.

[8] Ward, S.A., Evaluating transformer condition using DGA oil analysis, Conference on Electrical Insulation and Dielectric Phenomena, 2003, Annual Report, pp. 463-468.

[9] Varl, A., On-line diagnostics of oil-filled transformers, Proceedings of 14th IEEE International Conference on Dielectric Liquids, ICDL 2002, 7-12 July 2002, pp. 253-257.

[10] http://www.powertech.bc.ca/cfm quoted 30.6.2005

Dielectric Diagnostics Measurements of Transformers

DIELECTRIC DIAGNOSTICS MEASUREMENTS OF TRANSFORMERS

Xiaolei Wang Institute of Intelligent Power Electronics

Helsinki University of Technology Otakaari 5 A, FIN-02150 Espoo, Finland

Phone: +358 9 451 4965, Fax: +358 9 451 2432 E-mail: [email protected]

TABLE OF CONTENTS

1 Introduction ........................................................................................................................ 1

2 Transformer insulation structure ........................................................................................ 2

3 Dielectric diagnostics measurement method...................................................................... 3

3.1. Return Voltage Measurement (RVM)........................................................................ 4

3.2. Polarization and Depolarization Current method (PDC) ........................................... 6

3.3. Frequency Domain Spectroscopy (FDS).................................................................... 8

3.4. Comparison and conclusion on three dielectric measurement approaches ................ 9

3.5. Influences on dielectric measurement ...................................................................... 10

4 Conclusions ...................................................................................................................... 10

5 References ........................................................................................................................ 10

1 INTRODUCTION

During recent years, the diagnostics of power system equipments, for example, transform-ers, has gained great research attention. Diagnostics refers to the interpretation of data and off-line measurements on transformers. It is normally used either for determining the actual condition of a transformer, or as a response to a received warning signal [1]. Generally, the current transformer diagnostics approaches become more and more advanced, which can be classified by different fault types, e.g., thermal, dielectric, mechanical, and degradation re-lated faults. To detect ageing phenomena of transformers, there are several methods that can be employed, such as partial discharge measurement and gas-in oil analysis, etc. Dielectric Diagnostics Method (DDM) is one distinguished approach to asseting ageing condition of transformers, which represents a family of methods used for characterization of dielectric materials as well as practical insulation systems [2].

The focus of this report is on the basic principle of DDM as well as its three important measurement methods. Firstly, the general insulation structure of transformers will be de-scribed in the following section. Three dominated DDM-based measurement approaches,

1



Return Voltage Measurement (RVM), Polarization and Depolarization Current (PDC), and Frequency Domain Spectroscopy (FDS), will be introduced in section 3. Finally, there will be a short conclusion and discussion.

2 TRANSFORMER INSULATION STRUCTURE

The life span of the magnetic device highly depends on its insulation system, an amalgam of different insulating materials, processes, and interactions. Generally, the insulation system of power transformers consists mostly of mineral oil and cellulose paper. With the age in-creasing, the oil/paper insulation of transformers will degrade due to thermal, oxidative and hydrolytic factor. One aging indicator is the water content in the solid part of the insulation. Increased water content accelerates the deterioration of cellulose through depolymerisation, and causes bubble formation resulting in electrical breakdown as well [3].

To estimate the humidity of transformer insulation, one needs a data library of dielectric

properties ( ∞ε , dcσ , and f(t)) of oils and pressboard at different humidity content. This data

library is necessary to calculate the dielectric response of the composite duct insulation, and for comparing with the measurement results. In frequency domain, the material of trans-former is characterized by a complex frequency and temperature depended permittivity, as shown in (1).

),(),(),( TiTT ωεωεωε ′′⋅−′= (1)

In the frequency range of interest, for transformer oil the real part is constant ( =r 2.2ε ), and

the imaginary part is dominated by the contribution from the DC conductivity. However, in

the time domain the material is characterized by the power frequency permittivity ( rε ), DC

conductivity ( DCσ ), and dielectric response function f(t) [4].

the

idit lectric permittiv-

ity,

In addition, the dielectric response is influenced by the insulation structure, as shown in Fig.1 [5]. In the winding configuration (Fig.1 (a)), the section of insulation duct consists of cylindrical shells of pressboard barriers separated by axial spacers. In the modeling of the combination of oil and cellulose in the duct (Fig.1 (b)), parameters X and Y are defined as the relative amount of solid insulation (barriers) and spacers respectively. Generally,barriers fill 20-50% of the main duct, and the spacers fill 15-25% of the circumference.

Based on the abovementioned material and geometric properties of composite system, the dielectric response can be derived. In the time domain (PDC and RVM methods), the calcu-lation is based on the known response function ƒ(t), and it also depends on temperature and hum y. In the frequency domain (FDS method), the composite die

ductε , of the insulation duct is calculated as the following function [5].

2


barrieroilbarrierspacer

duct XXY

XXYT

εεεε

ωε+

−−

++

−= 1

11),( (2)

Fig. 1. (a) Section of an insulation duct of a power transformer with barriers and spacers, (b) schematic representation of the barrier content and the spacer coverage in the insula-

tion duct.

On the right side of (2), the permittivity of the oil, spacers, and barriers deduced from the measurements on the insulation model, are complex quantities dependent on frequency, temperature, and humidity. The materials properties are varied until a good fit with the measured values is achieved [6].

3 DIELECTRIC DIAGNOSTICS MEASUREMENT METHOD

It is well known that one important application of Dielectric Diagnostics Method (DDM) is to asset the humidity in the insulation system of transformer. Due to its oil/paper insulation structure, transformer shows the characteristics of polarization and conductivity. DDM can work in such a field dominated by interfacial polarization at the boarders between cellulose and oil, and cellulose and oil conductivity. Fig. 2 illustrates a basic diagram for electric measurements [7]. In figure 2, the Low Voltage (LV) and High Voltage (HV) winding ter-minals of transformer are connected together as a two-terminal test object to the DDM in-strument.

DC voltage, DC current, AC voltage, and AC current can be measured to evaluate dielectric response phenomena, which develop three widely employed dielectric diagnostics meas-urement methods:

• Recovery (Return) Voltage Method (RVM),

• Dielectric spectroscopy in time domain, i.e., Polarization and Depolarization Currents method (PDC),

• Frequency Domain Spectroscopy analysis (FDS), i.e., measurements of electric ca-pacitance and loss factor in dependency frequency.

3


Fig. 2. Basic circuit diagram for dielectric measurements.

DC voltage measurements are applied as recovery voltage measurements after charging the insulation with a DC voltage. The derived diagnostic method is called the RVM. A series of recovery voltage measurements with increased charging time leads to the so called “Polari-zation Spectrum” which is commonly used to evaluate the moisture content of cellulose. A DC current measurement will record the charging and discharging currents of insulation. They are known as the PDC. AC voltage and current measurements are derived from the well-known Tangent Delta measurements. However the frequency range is much enhanced especially to low frequencies, e.g. 0.1MHz. The derived measurement method is the FDS [8].

In this section, we will discuss these three methods in detail.

3.1. Return Voltage Measurement (RVM)

The dielectric measurements can be performed in both frequency and time domain. The fea-ture of time domain measurement methods is applying a step voltage across the sample. Figure 3 shows a simplified diagram of dielectric response measurement in time domain for a power transformer. For the RVM, s3 closed and s1, s2 open [9]. In this case, the RVM is coupled to the low voltage terminal and the high voltage windings and the tank are grounded. The principle of the measurement is that the test object is first charged for a given time, then discharged for half the charging time and after that the return voltage is measured under open circuit conditions.

Assume a DC step voltage is applied to the initially completely discharged system:

4


⎪⎩

⎪⎨⎧

≤≤≤>

=t t 0

tt0 t0 0

)(1

10UtU (3)

DC

S1

S2S3

A

V

HV

LV

Fig. 3. Simplified Diagram of Dielectric Response Measurement.

When the step voltage is applied during period 10 tt ≤≤ , the charging (polarization) current

is generated

⎥⎦

⎤⎢⎣

⎡+= )()(

000 tfUCti r

pol εσ (4)

where is the permittivity of vacuum of the dielectric material, and mF /10854.8 120

−×=ε

rσ is the average conductivity of the composite insulation system. The response function of

the composite insulation f(t) describes the fundamental memory property of the dielectric system and can provide significant information about the insulation material. Afterwards the step voltage is disconnected from the insulation (grounded), and the discharging (depolari-zation) current is generated as shown in (5) [10].

[ ])()()( 100 ttftfUCtidepol +−= (5)

The source of the recovery voltage is the relaxation of remaining polarization in the insula-tion system, giving rise to an induced charge on the electrodes. The polarization spectrum is established by performing a series of recovery voltage measurements with stepwise charg-ing and discharging time. For each sequence in the spectrum, the peak of recovery voltage

as well as the initial rate of rise of the recovery voltage rV dtdVr are recorded and plotted

versus the charging time used [5].

Figure 4 demonstrates such a polarization spectrum. In this RVM, a transformer is charged initially for 0.5 second, after that in every next cycle the charging time is doubled, until 1024 seconds. The ratio of charging to discharging time is a constant two. Charging and discharging current and return voltage data are recorded for every test cycle.

5


Fig. 4. Dielectric response measurement of return voltage.

A return voltage spectrum, indicating the insulation condition, can be drawn with the return voltage and the central time constant from each test cycle. Figure 5 illustrates such return voltage spectra, and the operation times of these transformers are list in Table 1. The peak in the spectrum can provide indication of the insulation condition. The spectrum also pro-vides the range of the response in the time domain [9]

Fig. 5. Typical return voltage spectra.

Table 1. Transformer operation time in Fig.5.

Transformer T4 T5 T6

Age (year) 3 38 33

3.2. Polarization and Depolarization Current method (PDC)

The polarization current is also obtained by applying a step voltage between the HV and LV windings during a certain time. The charging current of the transformer capacitance, i.e. in-

6


sulation material, called polarization current is generated. The depolarization current is measured with power supply removed. The connection of the PDC is same as that of the RVM in the time domain measurement illustrated in Fig. 3. For this case, polarization cur-rent measurement is performed with s1 closed, s2 and s3 open. For the depolarization cur-rent measurement, s2 is closed, and s1 as well as s3 are open. Figure 6 demonstrates the cur-rent waveform during the instant of voltage application which decreases during the polariza-tion to a certain value given by the conductivity of the insulation system [9].

Fig. 6. Polarization and depolarization current waveform.

From the (4) and (5), we can conclude that both polarization and depolarization current con-sider dielectric response function. The DC conductivity of the test object can be estimated from the measurements of polarization and depolarization current. However, it is easier to employ the depolarization current due to no DC current involved. Figure 7 shows the meas-ured polarization currents from some moisture-conditioned samples. Obviously, the ampli-tude of long term DC polarization current is quite sensitive to the moisture content in paper insulation, which demonstrates that polarization current measurement is capable of assess-ing the insulation moisture lever [9].

Fig. 7. Measured polarization current from moisture conditioned samples.

7


3.3. Frequency Domain Spectroscopy (FDS)

In the frequency domain measurement, a sinusoidal voltage is applied, and the complex di-electric constant is determined from the amplitude as well as phase of the measured current flowing through the test object. The dielectric susceptibility can be considered as the re-sponse function in time domain, which is related through the following Fourier transform function [11].

∫∞ −=′′−′=

0)()()()( dtetfXiXX tj

sssωωωω (6)

The susceptibility is a complex function of frequency, and is related to the relative permit-tivity as shown in (7)-(9).

ωεσωωεωεωεωε0

)()()()()( iXiXi ssrrr −′′−′+=′′−′= ∞ (7)

)()( ωεωε sr X ′+=′ ∞ (8)

ωεσωωε0

)()( iX sr +′′=′′ (9)

In (7), the imaginary part of the complex relative permittivity (loss part) contains both the resistive (DC conduction) losses and the dielectric (polarization) losses, and that at a given frequency it is impossible to distinguish between the two. However, the resistive part is dominant at low frequency. In this case, the imaginary part of the complex relative permit-tivity has a slope of and the real part is constant. Based on this, the conductivity of the test object could be calculated. Another way of presenting the measured information of a FDS is to use the loss factor [5].

1−ω

Alternatively, loss factor method is usually employed to present the measured information. Loss factor is the frequency dependent ratio of imaginary and the real parts of the complex permittivity as shown in (10) [11]. The geometrical independency of the rest object makes the loss factor important to study when the object geometry is unknown.

)()(tan

ωεωεδ

r

r

′′′

= (10)

Figure 8 demonstrates the loss factor of the four units as a function of frequency. Obvi-ously, T11 (1973) is similar to T12 (1973), and T41 (1977) is similar to T42 (1977). How-ever, the main reason of the difference is that the oil conductivity of T11/T12 is higher than that of T41/T42. With the fixed oil conductivity, the influence of the moisture content of the cellulose is illustrated in Fig. 9. The moisture content is around one percent in T11/T12, and lower in T41/T42 [6].

8


Fig. 8. Loss factor as a function of frequency for different transformers.

Fig. 9. Loss factor for different moisture contents of the cellulose.

3.4. Comparison and conclusion on three dielectric measurement approaches

Nowadays, all of these three methods are widely employed for the diagnostics of power transformer insulation, with commercial available or test set-up instruments sometime.

The RVM method is a useful but more sensitive to systematic errors than the others. It is a high impedance input method, and leakage currents on the bushings could easily corrupt the measurements. Basically, the PDC is a non-destructive method, and is same as the insula-tion resistance measurement. If the currents are low the method can be sensitive to offset currents and interference in the field. The oil conductivity can be deduced from the initial current and board conductivity affected by moisture is from the final current. The FDS method has the advantage of including the loss factor and capacitance measurement. There-fore, it is easy to detect fault in the test set-up. The inherent small bandwidth makes the method relatively insensitive to interference and there is no need for a high voltage power supply [6].

9


3.5. Influences on dielectric measurement

Although dielectric diagnostics techniques have been improved recent years, there are still some factors affect measurement reliability and stability of analysis results. These influ-ences are also the main source of error. For instance, some essential issues of them are list as following [7].

• Insulation temperature

• Migration processes

• Decreasing oil conductivity

• Parallel current paths

• Temperature compensation in analysis software

• Interpretation of measurement data

• Comparison to moisture equilibrium charts

• Measuring time

4 CONCLUSIONS

Due to their remarkable characteristics, the RVM, PDC, and FDS are the three dominant dielectric diagnostics approaches to asses the power transformer insulation. In this report, their basic principles and analysis results are discussed respectively. The analysis demon-strates that these diagnostics methods are useful for off-line assessment of power trans-former insulation. However, these approaches are still influenced by some conditions and source errors. Recently, more and more research work are concerning about the condition factor to the measurement.

5 REFERENCES

[1] C. Bengtsson, “Status and trends in transformer monitoring,” IEEE Transactions on Power Delivery, vol. 11, no. 3, pp. 1379-1384, 1996.

[2] U. Gafvert, “Dielectric response analysis of real insulation systems,” in Proceedings of the IEEE International Conference on Solid Dielectrics, Toulouse, France, July 2004, pp. 1028-1037.

[3] Y. M. Du, B. C. Zahn, A. V. Lesieutre, and S. R. Lindgren, “A review of moisture equilibrium in transformer paper-oil systems,” IEEE Electrical Insulation Magazine, vol. 15, no. 1, pp. 11-20, 1999.

[4] A. K. Jonscher, Dielectric Relaxation in Solids. London, UK: Chelsea Dielectrics Press, 1984

[5] CIGRE, “Dielectric response methods for diagnostics of power transformers,” IEEE Electrical Insulation Magazine, vol.19, no.3, pp.12-18, 2003.

10


[6] U. Gafvert, L. Adeen, M. Tapper, P. Ghasemi, and B. Jonsson, “Dielectric spectros-copy in time and frequency domain applied to diagnostics of power transformers,” in Proceedings of the International Conference on Properties and Applications of Dielec-tric Materials, Xi’an, China, June 2000, pp. 825-830.

[7] K. Maik, and F. Kurt, “Reliability and influences on dielectric diagnostic methods to evaluate the ageing state of oil-paper insulations,” in Proceedings of International Con-ference on Advances in Processing, Testing, and Application of Dielectric Materials, Wrocław, Poland, Septermber 2004.

[8] W. S. Zaengl, “Dielectric spectroscopy in time and frequency domain for HV power equipment, part I: theoretical considerations,” IEEE Electrical Insulation Magazine, vol. 19, no. 5, pp. 9-22, 2003.

[9] T. Y. Zheng, and T. K. Saha, “Analysis and modeling of dielectric responses of power transformer insulation,” in Proceedings of IEEE Power Engineering Society Summer Meeting, Chicago, IL, July 2002, pp. 417-421.

[10] T. K. Saha, and P. Purkait, “Effects of temperature on time-domain dielectric diagnos-tics of transformer,” Australian Journal of Electrical & Electronics Engineering, vol. 1, no. 3, pp. 157-162, 2004.

[11] T. K. Saha, “Review of modern diagnostic techniques for assessing insulation condi-tion in aged transformers,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 10, no. 5, pp. 903-917, 2003.

11

1

ASSET MANAGEMENT IN POWER SYSTEMS

CONDITION MONITORING OF GENERATOR SYSTEMS

Matti Heikkilä

Vaasa

[email protected]

2

CONTENTS

1. INTRODUCTION........................................................................................................ 3

2. ON LINE CONTINUOUS MEASUREMENTS..................................................... 4

2.1 Vibration Measurements ............................................................................................. 4 2.2 Temperature Measurements ....................................................................................... 4 2.3 Protection Relays......................................................................................................... 5 2.4 Measurement of Shaft Movement.............................................................................. 5

3. CONDITION MONITORING................................................................................... 5

3.1 On-line Partial Discharge (PD) Measurements ...................................................... 6 3.2 Measuring of Isolation Resistance ......................................................................... 11 3.3 Mechanical Inspection .............................................................................................. 11 3.4 Tan Delta Monitoring System ................................................................................... 11

4. REFERENCES........................................................................................................... 13

3

1. INTRODUCTION The generator is one of prime devices in a power plant. The arrangement of the condition monitoring of generator is extremely important to avoid many failures, because the fixing of the generator takes very long time and the costs of the lost production are significant. So with the condition monitoring systems it is possible to follow the generator condition and to plan the revision in right time. Normally there are many measuring devices in generator, which follow the condition of generator during production. Such measurements are e.g.:

• Vibration measurements • Temperature measurements of generator windings • Protection relays • Measurement of generator shaft movement

Additionally for previous measurements, it is normal that during the revision the resistance of generator isolation is measured and mechanical inspections are made. The modern condition monitoring system is an on-line partial discharge (PD) monitoring system. The aim of partial discharge measurement on windings of rotating electrical machines is the assessment of the insulating conditions of the windings. The measurement and subsequent analysis of PD generates important information, which pay attention to the type of defects that may occur due to local over-stressing within an insulating system. These measurements are therefore well suited for non-destructive diagnosis of various dielectric materials e.g. the insulation of generator windings. With regard to risk assessment and early planning of preventive maintenance for rotating electrical machines it is necessary to get reliable information on the type and intensity of the partial discharges and therefore on the actual conditions of the winding insulation. Without performing meaningful diagnostic measurements a critical PD activity may, over a short or medium time range, lead to an unexpected machine failure, which causes considerable expenses be due to unscheduled downtimes and the replacement and/or reconditioning of damaged components. In the following Figure 1 is shown an example of air-cooled generator, in which have made all those measurement, which are mentioned in this seminar report.

4

Figure 1. Air cooled generator, 250 MW, 277 MVA, 15 kV

2. ON LINE CONTINUOUS MEASUREMENTS 2.1 Vibration Measurements If the generator vibration is very high it may cause damage to the shaft bearings of generator and also to the mechanical construction of generator. So it is normal that there are installed vibration measurement sensors (axial and radial x/y) into generator bearings. Into automatic controlling system is also set the limits to permitted vibration level; the automation system gives an alarm, if the limit is exceeded and the shut down command, if the vibration is too high. 2.2 Temperature Measurements The temperature of generator windings is followed by temperature measurement sensors (normally Pt100 sensors), which are connected into automatic controlling system. Into automatic controlling system is set limits for alarm and shut down commands.

5

2.3 Protection Relays Generator and its electrical systems are protected by protection relays. Modern generator has equipped with following protection relays:

• Generator over current • Generator over voltage • Generator under voltage • Generator differential protection • Generator overload • Generator reverse power • Generator pole slipping • Generator under excitation • Generator minimum impedance • Generator negative phase sequence • Generator under frequency • Generator over frequency • Generator stator earth fault 90 % and 100 % • Generator circuit breaker failure • Transformer bus earth fault • Unit transformer over current • Unit transformer differential protection • Unit transformer oil temperature • Unit transformer winding temperature • Unit transformer tab switch failure • Unit transformer gas relay • Unit transformer over pressure • Block transformer differential protection • Block transformer gas relay • Block transformer oil temperature • Block transformer winding temperature • Overall differential protection

2.4 Measurement of Shaft Movement Generator and turbine has equipped with shaft movement measurement system. High temperature, vibration and other reasons can cause that the shaft moves a little bit or has heat expansion. In that case the bearings function is not good. Measurement of shaft movement gives an alarm or shut down command if needed.

3. CONDITION MONITORING Generator condition must be followed and measured during running and during revision. On Chapter 2 are explained normal on-line measuring systems. Modern condition monitoring system, Partial Discharge (PD) monitoring system is explained in following Chapter 3.1. In

6

Chapter 3.2 is explained isolation resistance measurement, in Chapter 3.3 mechanical inspections and in Chapter 3.4 Tan delta measurements. 3.1 On-line Partial Discharge (PD) Measurements Partial discharges are small sparks which occur in high voltage insulation systems. Partial discharges (PD) in electrical machines arise

• in cavities in insulation systems when subjected to high electric fields, • in overcharged gas-filled gaps in the vicinity of windings, • in the area of faulty anti-corona systems.

Considered individually, the partial discharges in electrical machines are harmless. However, when high energy partial discharges occur permanently they can destroy any insulation system. The result is dielectric breakdown leading to machine failure. Partial discharge (PD) monitoring is the continuous acquisition and analysis of electrical discharges on an object interacted by voltage. The aim of partial discharge monitoring on rotating electrical machines in operation, is the early recognition of discharge phenomena which are dangerous for operation. The partial discharge monitoring system very sensitively detects and analyses location and cause of partial discharges arising in the machine while in operation. Partial discharge phenomena can not be recognized with any of the conventional protection systems. Partial discharge monitoring measurements are particularly recommended

• for key machines as generators • for planning condition-based maintenance • where enhanced operational reliability is called for • for fully automatic plant operation • in cases of severe operating conditions

In the partial discharge system are needed couplers which are connected to the machine to be monitored, the PD signal processor and the data acquisition and analyzing system as is shown in the following Figure 2.

7

Figure 2. Partial Discharge Monitoring System. [16] The partial discharge signals are decoupled by a special high-frequency current transformer or a capacitive coupler on a low-voltage side (star point) of the stator winding and depending on the machine, also by capacitive couplers on the high-voltage side. The measured signals are transmitted through special screened cables to the PD signal processor. The data acquisition and analyzing system is permanently connected to the PD signal processor. The system has also equipped to record the voltages of the objects being monitored. The individual components of PD- monitoring system are [16]:

• Couplers: capacitive couplers and high-frequency current transformers • PD signal processor: multiplexer for many high-frequency channels and voltage

channels including amplifier and filter • Data acquisition and analyzing system: components for digitizing and processing

the measured data, and for controlling all system components and functions. In this unit, disturbances are automatically eliminated; characteristic values and trends are calculated and indicated.

• Screened high-frequency signal cable with TNC / BNC connectors • Matching unit: only needed if couplers are only periodically connected to

measurement system • PD calibrator: needed during installation only

8

How does the PD measuring system works [16]: The signals originating in the insulation system are decoupled by the couplers. The signals are then transmitted from the couplers to the PD signal processor. There the analogue signals are prepared for fast scanning. The filters and amplifiers are set automatically by the computer of the data acquisition and analyzing system. Characteristic values, in conformity with IEC Publication 60270, are acquired by the data acquisition and analyzing system. After the processing, the signals are digitized at a scanning rate of up to 40 million measurements per second (40 MHz) (tuned to the filter characteristic of the PD signal processor) in the data acquisition and analyzing unit. After a complex noise recognition there follows an automatic suppression of time and frequency-stable and generator-specific interference. Analytic processes then statically evaluate the numerous partial discharges and reduce the immense amount of data. Besides the usual worldwide known evaluation criteria, additional quantities describing the distributions are also determined and stored. In the processing result are used tree dimensions as we can see in Figure 4:

• Phase angle (deg) • Number of pulses n • Apparent charge q (nC)

Long-time trends of these characteristic values permit continuous comparison with reference values to be made. Should the measured values exceed reference values an alarm is immediately initiated. The result of the analysis supply information on the kind, origin, intensity, and frequency of occurrence of the discharges. This information is then evaluated to determine the influence on the operational reliability of the machine. The benefits of PD measuring system:

• Partial Discharge Monitoring provides high operational reliability for the machine.

• PD Monitoring system supplies important information on the actual state of the machine in operation an on any critical changes arising. This information can not be detected by any other protective or monitoring system.

• When the results are positive, the time until the next maintenance inspection can be extended. When the results are critical the cause of the fault can be recognized and eliminated at an early stage before an actual failure occurs in the machine.

• Failure costs can be avoided and maintenance costs minimized so that the investment for PD Monitoring system pays for itself in a short time.

Examples of PD measuring: In the following Figure 3 is shown the measuring arrangements of air cooled generator 250 MW, 277 MVA, 15 kV:

• Measuring with generator half load without inductive load • Measuring with generator half load with 80 MVAr inductive load • Measuring with generator full load without inductive load • Measuring with generator full load with 80 MVAr inductive load

9

VL2: Planned PAMOS measurement time schedule 21.4.2002

0

50

100

150

200

250

0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

Time

[MW

]

Test Output

Normal Output

Inductive Output

Test Output

Normal Output

Inductive Output

Figure 3. Measuring arrangements for PD measurement. In the following Figure 4 are measuring results: The normally visible inner PD’s are superimposed by base noise of the machine. This is a sign of very low PD activity, especially of the inner PD’s. Inner discharges are typical for all mica based and resin impregnated insulation systems. The sources are small gaps and voids within the ground wall insulation. Since the mica based insulation is highly PD resistant such inner PD’s are regarded as rather harmless. They are classified as uncritical because of the low level within the same range as the base noise band. PD sources coming from the insulation system of the machine cannot be detected. Therefore the measured generator seems to be in good condition at time of the measurement. Needle shaped disturbances from the excitation device superimpose all PD readings. These disturbances are narrow and appear on a low level, so that they don’t mask any real PD phenomena from the machine. Those disturbances are marked in the following measuring result Figure 4.

10

Figure 4. PD Measuring results of air cooled generator with full load 235 MW and minimum reactive load 4 MVAr.

11

3.2 Measuring of Isolation Resistance At least once per year for example during the revision or during long shut down, it is good to measure the isolation resistance of generator windings. In the following Figure 5 is shown one example of insulation resistance measuring.

VL3 eristysvastus

-2000

0

2000

4000

6000

8000

10000

12000

0 10 20 30 40 50 60

Aika/min

Eris

tysv

astu

s/M

oh

m.

Figure 5. Generator windings insulation resistance measurement. The insulation resistance measuring has made with Metriso 5000 measuring device with 2500 V voltage and measuring time was one hour. Measuring time must be long enough for the insulation resistance to reach the final value. 3.3 Mechanical Inspection During generator revision it is necessary to make mechanical inspections for generator. The making of the following inspections are depending on the running hours of generator:

• Mechanical inspection of breakings and cracs of different mechanical constructions • Retaining rings must check by ultrasonic devices after about 70 000 running hour if

retaining rings are made of old material. In new generators has used new material (18% Mn / 18% Cr) and it is not necessary to check retaining rings any more, because the new material don’t split or break any more.

• Generator stator windings slots and end-windings must check after about 50 000 running hours. Stator windings might get loose and they must be tightened again.

3.4 Tan Delta Monitoring System Tan delta measurements are the most suitable for oil-paper insulated high voltage (HV) cables used in transformers, generators, bushings and other electrical devices. So in the new generator, in which has used mica- insulation and other new insulation materials, it is better to use partial discharge measurements.

12

The tan delta measurement is a comparative method in which the reference measurement device is normally constructed from high voltage capacitors. The measured values are also compared to previous measurements of examined cables or devices.

Figure 6. Basic arrangement for the Tan delta measurement of C. [14.] HV = Operating Voltage C = Capacitance of Insulation (tan delta) (corresponding to dielectric losses of insulation) Ca = Additional Capacitance for Signal Conditioning Ur = Reference Voltage Um = Measurement Signal Voltage MS = Monitoring System

MS

13

4. REFERENCES

1. Miomir U. Kotlica, On-Line Monitoring of Power Generator Systems, KES

International Ltd, Toronto Canada, 1998. 2. Yunsok Lim, Jayoon Koo, Joenseon Lee, Wonjong Kang, Chaotic Analysis of Partian

Discharge (CAPD) – A novel approach to identify the nature of PD source, SMDT Lab., Dept. of Electrical Engineering, Hanyang University, Korea, 2001.

3. Y. Han and Y. H. Song, Condition Monitoring Techniques for Electrical Equipment –

A Literature Survey, IEEE Transactions on Power Delivery, vol. 18, No 1, January 2003.

4. P. H. F. Morshuis, R. Bodega, M. Lazzaroni, F. J. Wester, Partial Discharge Detection

Using Oscillating Voltage at Different Frequencies, Delft University of Technology, The Nederlands, 2002.

5. X. Ma, C. Zhou and I. J. Kemp, Wavelet for the Analysis and Compression of Partial

Discharge Data, School of Engineering, Science and Design, Glasgow Caledonian University, UK, 2001.

6. Ümmühan Basaran, Mehmet Kurran, The Strategy for the Maintenance Scheduling of

the Generating Unit, Anadoly University Eskisehir, Turkey, 2003.

7. A. M. Leite da Silva, G. J. Anders, L. A. F. Manso, Generator Maintenance Scheduling to Maximize Reliability and Revenue, IEEE Porto Power Tech Conference, 2001.

8. S. Cherukupalli, R. A. Huber, C. Tan, G. L. Halldorson, Application of Some Novel

Non-Destructive Diagnostic Tests for Condition Assessment of Stator Coils and Bars Following Voltage Endurance Tests, Conference Record of the 2002 IEEE International Symposium on Electrical Insulation, Boston, USA, 2002.

9. Dr. Q. Su, New Techniques for On-Line Partial Discharge Measurements, Monash

University, Australia, 2001.

10. Zhan Wang, JiWei guo, JingDong Xie, GuoQing Tang, An Introduction of a Condition Monitoring System of Electrical Equipment, Southeast University, Nanjing, China.

11. H. M. Banford, R. A. Fourache, Nuclear Technology and Ageing, Scottish

Universities Research and Reactor Center, 1999.

12. Z. Berler, I. Blokhintsev, A. Golubev, Partial Discharge On-Line Monitoring in Switchgears and Bus Ducts, Cutler-Hammer Predictive Diagnostics, Minnetonka, USA, 2001.

14

13. Karim Younsi, Paul Me´nard, Jean C. Pellerin, Seasonal Changes in Partial Discharge Activity on Hydraulic Generators, IEEE 2001.

14. Dr. P. Vujovic, R. K. Fricker, Development of an On-Line Continuous Tan ()

Monitoring System, IEEE 1994.

15. Mr. Jan Franlund, The four sides of Asset Management, Swedish Maintenance Society, UTEK,

16. ABB Partial Discharge Monitoring System, PAMOS

Asset management in power systems post graduate course

On-line monitoring applications for Secondary substation

Petri Trygg Institute of Power Engineering Tampere University of technology P.O. Box 692 Finland Email: [email protected]

Table of contents

TABLE OF CONTENTS .......................................................................................................................2

INTRODUCTION ..................................................................................................................................3

SECONDARY SUBSTATIONS............................................................................................................4

METERS FOR SECONDARY SUBSTATION...................................................................................5

ADDITIONAL SENCORS ....................................................................................................................6

COMMUNICATIONS ...........................................................................................................................7

INFORMATION SYSTEMS.................................................................................................................8

ANALYSIS..............................................................................................................................................9

CONCLUSION.....................................................................................................................................11

REFERENCES :...................................................................................................................................12

Introduction This study has been prepared for Assets management in power systems post graduate

course. The course is to be held in Helsinki University of technology in august 2005.

The subject of the study is on-line monitoring applications for secondary substations.

Theoretical backround for the subject has been the foundation but more emphisis is on

actual solutions on markets and some future sights to give path for development.

On-line monitoring applications have been earlier introduced for primary substations.

The whole concept of application consist of multiple levels. First level is the

measurement location. Second level is formed by the meter and sometimes also

additional sencors. Third part is the telecommunications. Recent development is

focused on making the communication more efficient. This means that speed has

increased while the costs have stayed on same level or even decreased in some cases.

Information systems forms the heart of the application. These are also the most

rapidly developing part of the whole concepts. The types of applications are

manufacturers own systems, separate independed systems and existing monitoring

systems in the grid company.

Information system / Application

Location/site

Meter and additional sencors

Telecommunications

Database

Analysing tools

User Interface

Interface to other information systems

SCADA/ remote control system

Secondary substations The secondary substation consist of transformer, breakers and bus bars similarly as

primary substation. The main difference is the lower voltage level. Voltage levels

vary according to the national practices. In Finland typical voltages are 20 kV to 0,4

kV

Picture 1. Outdoor substation [El05]

Secondary substations form the last

link before the customer. Traditionally

any information of this level has been

difficult to get and real time

information is only been under

discussion. Technical development has

now made it possible to broaden the

distribution grid monitoring to cover

also secondary substations.

Secondary substations consist of three

different types. First one is the one

usually seen rural areas. It is its own

separate building that consist only of

necessary devices and premises. The

substation building protects the devices

from weather and unauthorized access.

The other type is the indoor

substations. This type is typical in

cities. It is built as part of building as a

separate room. The facility serves the

same purpose as in rural areas, but due

to lack of space it is build this way.

The thing to remember with the indoor

secondary substation is the risk of fire.

Picture 2. Indoor secondary substation. [WI05]

Meters for secondary substation Meters for secondary substations have exist for some time now. In Finland Elkamo

produced meters called PIHI already around ten years ago. Latest meters have more

advanced metering capabilities and communication methods. In picture 3 is lay out of

the WIMOTEC meter. The meter consist of the measurement module and

communications module. This meter type measures current from three phases. It also

includes temperature measurement which can be utilized also for condition

monitoring. Measurement capabilities are increased by analog and binary inputs.

These inputs connect to separate additional sensors which are described more closely

on the next chapter.

Picture 3. Example of a secondaryWIMOTEC[WI05]

Operating power inputs t

Temperature input

GSM module and antenna
Current measuremen
substation on-line monitoring meter

Binary and analog inputs

Additional sensors In addition to a meter in secondary substations the on-line monitoring is also based on

external sensors inside the substation and it’s devices. Possible sensors types are:

• Door indication

• Temperature measurement

• Smoke detection

• Humidity detection

• Short-circuit detection

• Fault indicator

• Switch / breaker status

• Oil temperature

With these additional information a lot of information can be formed straight or by

calculating using measurement and sensor data. Due to the fact that secondary

substations are usually not attached into information systems the information provides

much more detailed data for operating the network.

Pic 4. Typical lay out of a secondary substation. [NoLe05]

Communications The main reason for lack of on-line monitoring in secondary substations is the lack of

reasonable communication technologies. So far most of the monitoring applications

are based on manual data acquisition from the secondary substation. This is made by

downloading the measurement data during routine visit in secondary substations.

Some use of the information can be seen, but only with on-line communication it is

reasonable to talk about monitoring of secondary substations.

Radio links have been used to communicate with primary substations. In some extent

these are one possibility to provide communication. Development of the GSM

technology has provided solutions also for secondary substation monitoring. Wireless

communication is flexible way to create communication. GSM can be applied by two

separate methods. Firstly data calls can be used to make the connection. Other method

is to use SMS short messages. Also secured TETRA communication is being studied

to increase safety of the communication [NoLe01]. Currently GSM is also reasonable

priced communication method if all the costs are considered. Problem is that GSM is

quite slow for some solutions and further development in wireless communication is

provided e.g. in form of GPRS.

Some distribution companies have installed WLAN or other wireless communication

devices on their distribution area. These solutions can be also used to replace

GSM/GPRS. The speed is far greater and if the technology is existing and used also

for other purposes the cost of usage is low.

The new technology that is called Power Line Communication or PLC is also one

possibility to adopt for secondary substation monitoring. So far in Finland at least

Turku Energia is using the PLC technology.

Nowadays the most developed technology for continuous monitoring is the GSM.

There are ready and available products for on-line monitoring of secondary

substations in the markets already. Other communication methods require more

adjusting for this purpose.

Information systems New technology in secondary substation monitoring has also boosted the new

technology in software designing. For example wimotec provides the Wimo DTMS

system also in Application Service Provisioning model (ASP). This means that

software is delivered through Internet. The user interface of the application is on the

web browser. The advance of this model is the easiness in information delivery to any

party willing to have it. Also the up keeping of the whole system can be outsourced

wich allows flexibility in cost management.

Also interface with Scada or remote control system is already tested in Finland. In

Kuopion energia there are currently WIMOTEC devices attached into Scada system

[WI05]. This makes it possible to have much more detailed information of the entire

network. According to the Wimotec this seems to be the future trend.

Interface with other information systems such as Network Information System NIS

are necessary. Todays demands in information systems are that they truly can interact

and be flexible for other systems. Open interfaces and databases are part of the policy

to guarantee the best usability also in future.

Analysis

1. On-line monitoring On-line monitoring include the usage of the meter and communications to gather the

information on real time from the secondary substations. Outages information, and

other real-time events are the first and most critical part of the on-line monitoring. In

these kind of cases the faster the information of an event is in control room the better

fo all. Secondary substation level has earlier been the black spot where no information

on what so ever has come on real-time. Many companies have wanted to change this

weakness the the electricity network. Real-time information decreases the outage

times crucially. Manufacturers and research institutes have been working with this

issue and finally some results can be seen. Also more advanced analyzing methods are

being developed currently [Wu98].

2. Condition monitoring

Temperature and current information

can be used to evaluate the life cycle of

the secondary substations transformer.

Harmonics are used to make the

calculation more precise. Standards to

apply are IEC 354 for oil-immerse

transformers, IEC 905 for dry-type

transformers and CENELEC HD428.4

for current harmonics. [NoVe02]

Picture 5. Distribution transformer photographed with heat camera.

3. Power Quality analysis Power Quality base on voltage measurement. Standard EN 50160 defines the limits

for voltages. In Finland power quality analysis are also made with own national tools

such as quality classes and quality reports which are based on the same standard.

Quality report is being used specially for dealing with customer complaints. [Mä01]

Picture 6. Power Quality classes [PQ05]

Picture 7. ITIC curve for voltage sags

and swells. [PQ05]

Voltage sags and swells can also be

analyzed. The typical presentation is

the depth and time based table. This

tools is suitable for limited amount of

sags and swells analyzing. For longer

time period and larger amount of sags

and swells the ITIC curve is more

efficient. It separates sags and swells

according to how much they interfere

customer. This is based on classifying

them based on length and depth

according to the ITIC standard. This is

very useful tool specially on rural

areas.

Conclusion In the past secondary substations formed a black spot for distribution companies.

Even though some of secondary substations had meters the information that they

provided was limited and by any means didn’t provide on-line monitoring features.

Today and specially in the future new communication technologies have made it

possible to attach secondary substations in on-line monitoring of the electricity

network.

Several manufacturers already exist that provide other or both meter and software for

them. Attaching applications into existing information system is critical to achieve the

best possible usability. More analyzing tools and methods are required to help the

network operator be providing only the most essential information.

References : [Wu98] Wu et al., "Application of Regression Models to Predict harmonic

Voltage and Current Growth Trend from Measurement Data at secondary substations", IEEE Transactions on Power delivery, Vol. 13, No. 3, July 1998, pages 793-799.

[NoLe01] Nordman et al, "TETRA radio in Monitoring and Control of Secondary

Substations". Developments in Power System Protection, Conference publicationNo. 479. pages 283-286. IEE 2001.

[NoLe05] Nordman et al, “An agent concept fo Managing Electrical Distribution

Networks”. IEEE Transactions on Power Delivery, Vol 20, No. 2, april 2005. Pages 696-703.

[Mä01] Mäkinen et al, “Power Quality monitoring integrated with distribution

automation”. Cired 2001 Juna 18-21, conference publication. IEE [WI05] Wimotec Ltd homepage. Visited 4.8.2005. www.wimotec.com [PQ05] PowerQ Ltd homepage. Visited 5.8.2005. www.powerq.net [El05] Elkamo Ltd homepage. Visited 4.8.2005. www.elkamo.fi [NoVe02] Nousiainen K. & Verho, P. ”Monitoring the temperature and ageing of

distribution transformers with distribution automation”. Tampere University of Technology. Insucon 2002, Berlin.

Partial Discharge Detection by Wireless Sensors in Covered Conductors Overhead Lines

Report submitted for the course work

“ASSET MANAGEMENT IN POWER SYSTEMS”

Submitted By G. Murtaza Hashmi

Partial Discharge Detection by Wireless Sensors in Covered Conductors Overhead Lines AM in Power Systems ______________________________________________________________________________________________

PARTIAL DISCHARGE DETECTION BY WIRELESS SENSORS IN

COVERED CONDUCTORS OVERHEAD LINES

G. Murtaza Hashmi Researcher, Power Systems and High Voltage Engineering Lab,

Helsinki University of Technology (HUT), ESPOO. [email protected]

WHAT IS PARTIAL DISCHARGE (PD)? The term "PD" is defined by IEC 60270 (Partial Discharge Measurements) as a localized electrical discharge that only partially bridges the insulation between conductors and which may or may not occur adjacent to a conductor. A PD is confined in some way that does not permit complete failure of the system, i.e., collapse of the voltage between the energized electrodes such as the cable conductor and neutral wires. PD can result from breakdown of gas in a cavity, breakdown of gas in an electrical tree channel, breakdown along an interface, breakdown between an energized electrode and a floating conductor, etc. [1]. PD is a small electrical avalanche caused by locally disrupted electric fields in dielectric materials, and is symptom of insulation weakness and at the same time can lead to severe deterioration of the insulating material. So it is known as one of the major factors to accelerate degradation of electrical insulation. SIGNIFICANCE OF PD MONITORING PDs are small discharges caused by strong and inhomogeneous electrical fields. The reason for such fields could be voids, bubbles, or defects in an insulation material. Detection of PD is performed in order to ascertain the condition of the insulating material in high voltage elements, e.g. cables and covered conductors. Since PD usually occurs before complete breakdown, PD monitoring provides a warning to remove the power system component from service before catastrophic failure occurs [2]. Therefore, the area of PD measurement and diagnosis is accepted as one of the most valuable non-destructive means for assessing the quality and technical integrity of high voltage (HV) power apparatus and cables. PD monitoring involves an analysis of materials, electric fields, arcing characteristics, pulse wave propagation and attenuation, sensor spatial sensitivity, frequency response, calibration, noise, and data interpretation.

______________________________________________________________________________ Page 2 of 22


TYPES OF PDs PDs could be classified in following four types:

i. Internal discharges ii. Surface discharges

iii. Corona discharges iv. Discharges in electrical trees, which may be considered as internal discharges of specific

origin.

UNDERSTANDING THE INITIATION OF PD SIGNALS

The generation of PD signals could be analyzed by considering a cavity in the dielectric material of the covered conductor. The cavity is generally filled with air or some gas. The equivalent circuit of PD cavity in the dielectric is shown in Fig.1. The capacity of the cavity is represented by a capacitance which is shunted by a breakdown path. The capacity of a dielectric in series with the cavity is represented by a capacitance . The sound part of the dielectric material is represented by capacitance

,cb

.a

AC SOURCE

b

c

a Va

Vc

Fig. 1: Equivalent circuit of a dielectric with cavity The electric fields inside the conductor insulation ( ), and in cavity ( ) are given as: iE cE

εDEi = ……………………………………………………………………………………….… (1)

______________________________________________________________________________ Page 3 of 22


0εDEc = ………………………………………………………………………………………… (2)

Where is the electric flux density in the conductor measured in and is directly proportional to the applied voltage (V ) to the conductor.

D ,/ 2mCoulombsε is the permittivity of the insulating

material and rεεε 0= , where rε is the dielectric constant (relative permittivity) of the conductor insulating material (always greater than unity), and 0ε is the permittivity of the free space (or air). The electric field (or dielectric breakdown strength) is measured in , and this value is

at 1 atmosphere air pressure. Its magnitude depends upon the shape, location, and pressure of air or gas inside the cavity.

mmkV /mmkV /3

As the applied voltage V is increased, the electric field in the cavity is greater than the field in the surrounding dielectric as a result of the lower permittivity of the air or gas in the cavity.i.e. as 0εε f then, ic EE f

When the field becomes sufficiently higher in the cavity, the air can break down, in the process of which it goes from non-conducting to conducting, and the field in the cavity goes from very high to nearly zero immediately after the discharge. The measured PD signal is the result of the change in the image charge on the electrodes as a result of the transient change in the electric field distribution caused by the discharge. Such a discharge generates a voltage PD signal between the system conductors as a result of the change in the electric field configuration, which takes place when the discharge occurs. This phenomenon could be well understood by considering the transient change in capacitance between the conductor and ground shield of the conductor when the cavity goes from non-conducting to conducting. Obviously the capacitance increases when the cavity is conducting, which means that a current must flow down the conductor to charge the additional capacitance and maintain constant voltage on the conductor. This current flows through the impedance of the cable and generates a voltage pulse, which propagates down the conductor [1]. QUANTITIES RELATED TO THE MAGNITUDE OF PD Charge transfer The charge , which is transferred in the cavity could be taken as a measure. If the sample is large as compared to the cavity, as will usually be the case, this charge transfer is equal to [3].

1q

Vcbq ∆+≅ )(1 …………………………………………………………………………………...(3)

Where , is the breakdown voltage at which the discharge occurs in the cavity, and

is the voltage at which discharge extinguishes. ei VVV −=∆ iV

eV

______________________________________________________________________________ Page 4 of 22


As the deterioration of the dielectric certainly is related to the charge transfer in the defect, would be an attractive choice. However, cannot be measured with a discharge detector and is therefore not a practical choice.

1q

1q

Apparent charge transfer in the sample The displacement of charge in the leads of the sample can be taken. This quantity is equal to: q

Vbq ∆= ……………………………………………………………………………………….…(4) It causes a voltage drop in the sample. Most discharge detectors respond to this voltage drop and are thus capable of determining

)/( cbVb +∆.q

The charge delivered at the cavity by the PD is not identical with that recouped from the power supply network, measurable at the terminals as "apparent" pulse charge Thus.

1q.q

1)( qcb

bq+

= ……………………………………………………………………………………(5)

The only disadvantage in using is that it is inversely proportional to the insulation thickness. Thicker insulations are thus measured with less sensitivity than thinner ones.

q

PD SIGNAL CHARACTERISTICS The voltage in the cavity collapses in at most a few ns , so that the resulting voltage pulse which propagates in both directions away from the PD source has an initial pulse width in the range. However, all shielded power cables and covered conductors have substantial high frequency attenuation that increases the pulse width and decreases the pulse amplitude as a function of distance propagated, which also limits the optimum signal detection bandwidth [4].

ns

Recent studies have shown that radiation from PDs is impulsive in nature and consists of individual high-energy, wide band impulses of a few in length. Thus PD signals have very wide frequency range. The digital storage oscilloscopes and advanced digitizers enable study of the PD signals more closely using window processing, zooming and auto-advance features. Such techniques promise to be superior to the currently used conventional PD detector methods.

ns

The PD data transferred need to be processed to obtain the PD characteristics such as peak value, apparent charge, phase position, repetition rate and PD energy. The PD signal attenuates during its propagation and it give rise to the following critical detection issues, such as:

• Sensor locations and sensitivity • Measurement system response to attenuated signals • Noise detection and elimination

______________________________________________________________________________ Page 5 of 22


CONVENTIONAL PD DETECTORS When a PD occurs, a current pulse is produced and this current pulse interacts with the insulation capacitance as well as the external elements in the test circuit. Consequently a voltage pulse is superimposed on to the HV supply voltage. The conventional detection methods generally employ matching impedance consisting of resistors, inductors and capacitors in the PD current path. There are following two different methods of investigation [5]. (a) Straight detection including direct and indirect methods (b) Balance detection method (bridge circuit) LIMITATIONS OF CONVENTIONAL PD DETECTING SYSTEMS The conventional PD measurement techniques have been in practice for several decades. Most conventional PD detectors use a single input detection method to measure a voltage or a current signal at a terminal of the test object. It is based on processing of analogue signals derived from coupling impedance in the PD current path. These conventional techniques experience severe limitations when it comes to on-line monitoring due to the influence of background noise, absence of non-intrusive sensors and processing facilities. The conventional PD detection technique can identify discharges in short isolated cable lengths only. Unfortunately, it has insufficient sensitivity for a long circuit because of the large capacitance involved. It is also required to isolate the cable from the circuit. PD testing requires, besides the PD detector, additional HV components like a test-voltage supply and a coupling capacitor, which are heavy and expensive and not suitable for on-site tests [6]. They are, therefore, restricted to testing in a laboratory environment. Conventional PD detection has had a limit in detection frequency range, especially for CC overhead lines, due to the attenuation of high frequency partial discharge signals. Low detection frequency imposes a fundamental limitation on positioning partial discharge location that is one of the major concerns of the insulation monitoring. Recently, the detection frequency range has been extended up to radio frequency band with the development of the new sensors e.g. Rogowski coils. INTRODUCTION TO ROGOWSKI COIL Rogowski coils have been used for detection and measurement of electric currents for decades. They have generally been used where other methods are unsuitable [7]. They have become an increasingly popular method of measuring current within power electronics equipment due to their advantages of low insertion loss and reduced size as compared to an equivalent current transformer (CT) [8]. Actually, they are the preferred method of current measurements having more suitable features than CTs and other iron-cored devices.

______________________________________________________________________________ Page 6 of 22


The coils have also been used in conjunction with protection systems particularly high accuracy systems or where there are DC offsets which would degrade the performance of CTs. This is useful when measuring the ripple current, e.g., on a DC line. The features of Rogowski coils which make them particularly useful for transient measurements stem from their inherent linearity and wide dynamic range. CONSTRUCTION OF ROGOWSKI COIL A Rogowski coil is basically a low-noise, toroidal winding on a non-magnetic core (generally air-cored), placed round the conductor to be measured, a fact that makes them lighter and smaller than iron-core devices. The end of the winding is usually wound back through the tube in order to reduce the coupling of any radiated noise signals. The coil is effectively a mutual inductor coupled to the conductor being measured and the output from the winding is an electromotive force proportional to the rate of change of current in the conductor. This voltage is proportional to the current even when measuring complex waveforms, so these transducers are good for measuring transients and for applications where they can accurately measure asymmetrical current flows. The self inductance of the coil is fixed, and its mutual inductance with the HV test circuit varies depending on the position of the coil in relation to the conductor. But once the coil is clamped and held stationary the mutual inductance remains constant. Usually the coil is not loaded and the voltage appearing across it is used as PD measurement signal. Under this condition the coil voltage is directly proportional to the derivative of the current in the conductor. Owing to the small value of the mutual inductance it acts as a high pass filter; the sensor therefore attenuates the power frequency component of the current in the conductor and minimizes electromagnetic interference at lower frequencies. However the PD signals are in the range of MHz generally and induce sufficiently large voltage in the range of . mV The coils are designed to give a high degree of rejection to external magnetic fields, for example from nearby conductors. The coils are wound either on a flexible former, which can then be conveniently wrapped round the conductor to be measured, or are wound on a rigid former which is less convenient but more accurate. The rigid coils have a cross sectional area of about and the flexible coils have a cross section of about [7]. Both of these transducer types exhibit a wide dynamic range, so same Rogowski coil could often be used to measure currents from to . They also exhibit wide band characteristics, working well at frequencies as low as 0.1 Hz, but typically useful out to hundreds of kilohertz, too. All this with low phase error and without the danger of open circuited secondary (as it could happen in case of CT).

26cm24.0 cm

mA kA

TYPES OF ROGOWSKI COILS Rigid Rogowski coils Rigid Rogowski coils have a greater accuracy and stability than flexible coils and excellent rejection of interference caused by external magnetic fields. They are more suitable for low

______________________________________________________________________________ Page 7 of 22


current and low frequency measurements. All rigid coils are provided with electrostatic screening as standard to reduce noise and to minimize the effect of capacitive pick-up from the conductor voltage. For a particular coil size, the typical value of mutual inductance is 3 - 5 Hµ , in the current range of 100 to 100 , up to frequencies in tens of . The actual specification will depend on the wire gauge used for the winding.

mA kA kHz

Rigid coils have a very stable output and can be calibrated to an accuracy of better than 0.1% using traceable standards. Their output is not affected significantly by the position of the conductor threading the coil. Flexible Rogowski coils Flexible coils are generally more convenient to use than rigid coils but are less accurate. (1% compared with 0.1% for a rigid coil). They are better than rigid coils for high frequency measurements The coils are fitted by wrapping them round the conductor to be measured and bringing the ends together. It is important to align the ends correctly otherwise accuracy will be impaired and the coil will become sensitive to interference from adjacent conductors or other sources of magnetic fields. The standard locating method is a simple push-together system. A typical mutual inductance for a flexible coil is 200 – 300 . These coils can be used to measure currents from less than 1A to more than 1MA and at frequencies of up to several hundred kHz. An electrostatic screen is sometimes useful to reduce noise with very low current measurements or for minimizing capacitive pick-up with high frequency measurements.

nH

WORKING PRINCIPLE OF ROGOWSKI COIL A Rogowski coil could be modeled as a transformer consisting of a primary circuit with current

(the current to be measured in the conductor) coupling to a secondary (measuring) circuit with inductance loaded with resistance The coupling strength is given by the mutual inductance

PI

SL SR .M as depicted in Fig.2 [9].

The secondary voltage across the terminals of the Rogowski coil could be given as: SV

SS

PSS LjR

IMRjV

ωω+

= ………………………………………………………………………................(6)

Usually the air-cored coil is used in combination with a large resistance, i.e. SS RL ppω , and the measured voltage becomes:

______________________________________________________________________________ Page 8 of 22


IP

LP

M

IS

RS VS

Fig. 2: Equivalent circuit of the Rogowski coil

PS MIjV ω= ……………………………………………………………………………………...(7) So the output voltage at the terminals of the winding wounded around the toroidal coil is proportional to the time derivative of the current flowing in a conductor passing through the coil and is given by Eq. 8.

dttdi

RAN

dttdiMtv PP

S)(

2)()( 0 ⋅==

πµ

........................................................................................(8)

Where and are the instantaneous values of the voltage measured by the Rogowski coil, and current flowing in the conductor. is the permeability of the free space equal to ,

)(tvS )(tiP

0µmH /104 7−×π A is the cross sectional area, are the number of turns, and N R is the radius

of the Rogowski coil. An integrator is incorporated with the coil, which integrates the output voltage according to the following equation to convert it into the current following through the conductor,

)(tvS

dttvM

ti SP )(1)( ∫= ……………………………………………………………………………....(9)

The components of flexible Rogowski coil i.e. the toroidal coil and integrator is shown in Fig. 3.

______________________________________________________________________________ Page 9 of 22


Fig. 3: Components of flexible Rogowski coil. ADVANTAGES OF ROGOWSKI COIL PD SENSORS In the conventional sensor the test circuit capacitance determines the frequency bandwidth and it is usually not very wide compared with this sensor. The frequency band-width of the Rogowski coil is not influenced by the capacitance of the test circuit. It is determined largely by the self inductance and the capacitance of the coil and signal cables. It has following advantages:

i. The frequency response of the Rogowski-coil sensor is very wide. ii. There is no conductive coupling between the coil sensors and the high voltage test

circuits. Furthermore, the coil installation does not necessitate disconnection of the grounding leads of the test objects and therefore becomes non-intrusive sensor which is a very important aspect for on-site, on-line monitoring.

iii. It has the advantage of possessing high signal to noise ratio with wide frequency band-width.

iv. The Rogowski coil based PD measurement system is a low cost solution to the PD measurement and can be easily implemented on-site.

These advantages are essential for on-line PD measurements. The challenge for on-line PD measurements is to find the optimal locations for these sensors with respect to their sensitivity, interference level, signal distinction, and universal applicability. SIGNIFICANCE OF COVERED CONDUCTORS The use of covered conductors in the distribution system started with the need of decreasing the number of faults caused by the trees as well as reducing the expenses with tree clearance, and maintenance. The advantages for using covered conductors are [10]:

______________________________________________________________________________ Page 10 of 22


• No faults or troubles with snow or hoar frost. • No interruptions by dropping branches. • No faults with touching conductors by ice-shedding. • Smaller lanes through forests, reimbursement for owners is smaller. • The clearing of the lane from growing trees is more seldom. The big trees at the border to

the forest can remain in their place. Therefore, a lot of savings are possible. • Covered conductors are a cheaper alternative to underground cable, especially in difficult

terrain. • Lines nearer to areas where the public visit are not so dangerous because of accidental

touching as it is possible to touch the conductor without an electrical shock. However, these kinds of conductors have some problems such as:

• PDs are produced due to falling trees on the conductors which produce knife traces on the surface of the conductors.

• The sensibility to ultra-violet radiations. • The results produced at different locations could be different due to climatic differences. • The different dielectric constants of the material employed, generating electric field

concentration and consequently the possibility of the corona effects. • The susceptibility to thermo mechanical effects, causing cracks.

MEASURING TEST SET-UP As the PD signals have frequency in the range of MHz, so the flexible Rogowski coil is used in this study. The measuring test set-up is arranged in HV Engineering Lab, at HUT, and consists of the followings:

i. Covered conductor of approximate length 25 meters. ii. Rogowski coil (without integrator), model i2000 FLEX (Flexible AC current probe),

make FLUKE. iii. Tree leaning against the conductor to simulate real world analysis. iv. Digitizing signal analyzer (DSA) or oscilloscope. v. Computing system (laptop) for data acquisition from DSA.

vi. Calibrator for measuring system calibration. vii. HV power supply system 20 kV AC. Following photographs give an over view and arrangement of the measuring test set-up:

______________________________________________________________________________ Page 11 of 22


Fig .4: Placement of the Rogowski coil (without integrator) on the conductor

Fig. 5: Measuring test set-up

______________________________________________________________________________ Page 12 of 22


Fig. 6: Application of 20kV AC voltage for the energizing conductor

Fig. 7: Leaning of the tree on the conductor

Fig. 8: Knife traces made on the conductor for producing PDs

______________________________________________________________________________ Page 13 of 22


METHODOLOGY

Steps involved for PD detection from Rogowski coil measurements

i. The charge entering into the system due to additional capacitance produced by PDs is:

.)(1)()( dttvM

dttitq SP ∫ ∫∫ == ………………………………………………...............(10)

ii. Acquisition of data from Rogowski coil ( ) in the form of MAT file. This voltage

waveform is in the form of high frequency (Sv

MHz ) oscillatory transient. iii. The magnitude of M lies between 200-300 The average value 250 is selected

for this study. .nH nH

iv. By using the above steps and mathematical expression, following model is used for simulation in Simulink.

B

Voltage v(t) obtained from Rogowski coil

250e-9

Value of mutual inductance M

PD measurement in coulombs

1s

Integrator-2

1s

Integrator-1Division

Fig. 9: Simulink model for PD measurements by using Rogowski coil voltage pulse data Placement of Rogowski coil for PD measurements At long distance The Rogowski coil is placed at the long distance (at open end side of the conductor) from the point where insulation of the conductor is scratched to produce PD. At medium distance The Rogowski coil is placed at the mid distance between the points where insulation of the conductor is scratched and to the open end side of the conductor.

______________________________________________________________________________ Page 14 of 22


At short distance The Rogowski coil is placed at a short distance from the point where insulation of the conductor is scratched to produce PD. The measurement results are only shown for long distance placement of Rogowski coil. MEASUREMENTS AND CALCULATIONS Calibration of the measuring system The measurements for different charges of calibrator (10 100 1 , and 100 ) are made. The calibrator sends pulses of different charges into the system, and Rogowski coil measures those pulses in terms of voltage signals (without integrator). Those measured voltage signals are processed in Simulink model for the calculation of the charge.

,pC ,pC nC nC

Rogowski coil response for 10pC calibrator charge

-2 -1 0 1 2 3 4

x 10-6

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Time in seconds

Am

plitu

de in

vol

ts

Fig. 10: Rogowski coil voltage response for 10pC calibrator charge.

______________________________________________________________________________ Page 15 of 22


Rogowski coil response for 100pC calibrator charge

-5 0 5 10 15 20

x 10-6

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2x 10-3

Time in seconds

Am

plitu

de in

vol

ts

Fig. 11: Rogowski coil voltage response for 100pC calibrator charge.

0 0.5 1 1.5 2 2.5

x 107

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

Frequency in Hertzs

Am

plitu

de

Fig. 12: Rogowski coil voltage FFT response for 100pC calibrator charge.

-5 0 5 10 15 20

x 10-6

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1x 10-10

Time in seconds

PD

mea

sure

men

t in

coul

ombs

Fig. 13: PD measurement by Rogowski coil for 100pC calibrator charge.

______________________________________________________________________________ Page 16 of 22


Rogowski coil response for 1nC calibrator charge

-5 0 5 10 15 20

x 10-6

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

Time in seconds

Am

plitu

de in

vol

ts

Fig. 14: Rogowski coil voltage response for 1nC calibrator charge.

0 0.5 1 1.5 2 2.5

x 107

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Frequency in Hertzs

Am

plitu

de

Fig. 15: Rogowski coil voltage FFT response for 1nC calibrator charge.

-5 0 5 10 15 20

x 10-6

0

0.5

1

1.5

2

2.5

x 10-10

Time in seconds

PD

mea

sure

men

t in

coul

ombs

Fig. 16: PD measurement by Rogowski coil for 1nC calibrator charge.

______________________________________________________________________________ Page 17 of 22


Rogowski coil response for 10nC calibrator charge

-5 0 5 10 15 20

x 10-6

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

Time in seconds

Am

plitu

de in

vol

ts

Fig. 17: Rogowski coil voltage response for 10nC calibrator charge.

0 0.5 1 1.5 2 2.5

x 107

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Frequency in Hertzs

Am

plitu

de

Fig. 18: Rogowski coil voltage FFT response for 10nC calibrator charge.

-5 0 5 10 15 20

x 10-6

0

1

2

3

4

5

6

7

8

9x 10

-9

Time in seconds

PD

mea

sure

men

t in

coul

ombs

Fig. 19: PD measurement by Rogowski coil for 10nC calibrator charge.

______________________________________________________________________________ Page 18 of 22


Discussion The sensitivity of the coil is not so good for the measurements of 10 or less values of PDs, so it is not possible to use this data for simulation. The coil also gives the same response (noise) when placed at short distance near the calibrator.

pC

In case of calibrator 100 pulse, the Rogowski coil response gives the PD measurement of 93 when placed at long distance. In case of calibrator 1 pulse, the Rogowski coil response gives the PD measurement of 0.22 , and in case of calibrator 10 nC pulse, the Rogowski coil response gives the PD measurement of 9 . The frequency contents in acquired signals are in the range of 2.5-5

pCpC nC

nCnC

MHz for all above cases. These values are shown in Tab. 1 below.

Calibrator charge Charge measured by Rogowski coil placed at long distance Percentage error

10pC Could not be detected - 100pC 93pC 7% 1nC 0.22nC 78% 10nC 9nC 10%

Tab. 1: Comparison of calibrator charge and PD measurements by Rogowski coil. PD measurements from different tests Different tests are made by making knife traces on the surface of the insulation of the conductor (as could be seen in Fig. 8) . The PDs thus, are produced by deteriorating the insulation, and are measured by the Rogowski coil. In this measuring test set-up, the transmission line (conductor) is energized with 20 AC, and tree is leaned against the conductor as shown in the Fig. 7. The Rogowski coil is placed at long distance from the point of application of HV. The results form one of the tests is given below.

kV

Rogowski coil response without making knife traces with tree leaning on the conductor

-2 -1 0 1 2 3 4

x 10-5

-8

-6

-4

-2

0

2

4

6x 10-3

Time in seconds

Am

plitu

de in

vol

ts

Fig. 20: Rogowski coil voltage response without making knife scratches with leaning tree on the conductor

______________________________________________________________________________ Page 19 of 22


0 1 2 3 4 5 6 7 8 9 10

x 106

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Frequency in Hertzs

Am

plitu

de

Fig. 21: Rogowski coil voltage FFT response without making knife scratches with leaning tree on the conductor

-2 -1 0 1 2 3 4

x 10-5

0

1

2

3

4

5

6

7

8x 10-9

Time in seconds

PD

mea

sure

men

t in

coul

ombs

Fig. 22: PD measurement by Rogowski coil without making knife scratches with leaning tree on the conductor Rogowski coil response for a test after making knife traces

-2 -1 0 1 2 3 4

x 10-5

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

Time in seconds

Am

plitu

de in

vol

ts

Fig. 23: Rogowski coil voltage response when making knife scratches with leaning tree on the conductor

______________________________________________________________________________ Page 20 of 22


-2 -1 0 1 2 3 4

x 10-5

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1x 10-8

Time in seconds

PD

mea

sure

men

t in

coul

ombs

Fig. 24: PD measurement by Rogowski coil when making knife scratches with leaning tree on the conductor Discussion This first measurement is taken without scratching the conductor. The conductor is energized with 20 kV voltage. The Rogowski coil response gives the PD measurement of 7.8 .This PD could be produced due to corona effect by leaning of tree on the conductor, as coil does not give any response when tree is not leaning against the conductor.

nC

A test is made by making knife traces on the surface of the conductor. The conductor is energized with 20KV voltage. The Rogowski coil response gives the PD measurement 0f 10 . nC CONCLUSIONS

i. It is possible to measure PD by using Rogowski coil. ii. The sensitivity of the coil is low as it is unable to measure PDs of 10 or of less value. pC

iii. Although the coil does not give the exact value of PD as pulse sent by calibrator, but it could be used for approximation. The results could be improved by calculating the exact value of mutual inductance .M

iv. The frequency of the PD pulses is in the range of 2.5-5 MHz in case of measurements taken at the long distance.

v. Corona could be produced due to the leaning of the trees on the conductors. RECOMMENDATIONS FOR FUTURE WORK

i. Filters and wavelet transforms could be implied for reducing noise in the measured voltage signals obtained from Rogowski coil, thus improving results.

ii. Covered conductor line could be modeled and the results obtained from the measurements should be compared with those obtained from the simulation of models for verification.

iii. The attenuation of the PD signals is an important factor in the analysis of PD detection, and the location of PD sensors over the length of the conductor. The attenuation could be determined by taking measurements at different known lengths over the conductor.

______________________________________________________________________________ Page 21 of 22


REFERENCES [1] S.A. Boggs, J. Densley, “Fundamentals of Partial Discharge in the Context of Field Cable Testing”, IEEE Electrical Insulation Magazine, Vol. 16, N0.5, September-October 2000, pp. 13-18. [2] T. Babnik, R.K. Aggarwal, P.J. Moore, and Z.D. Wang, “Radio Frequency Measurement of Different Discharges”, Paper accepted for presentation at 2003 IEEE Bologna PowerTech Conference, June 23-26, Bologna, Italy. [3] F. H. Kreuger, "Partial Discharge Detection in High Voltage Equipment" [4] S.A. Boggs and G.C. Stone, “Fundamental Limitations to the Measurement of Corona and Partial Discharge,” 1981 Annual Report of the Conference on Electrical Insulation and Dielectric Phenomena and reprinted in the IEEE Trans. EI-17, April, 1982, p. 143. [5] IEC: Partial discharge measurement, IEC 270, 1981. [6] N. H. Ahmed, N. N. Srinivas, “On-line Partial Discharge Detection in Cables”, IEEE Transaction on Dielectrics and Electrical Insulation, Vol. 5, N0.2, April 1998, pp. 181-188. [7] Ward, D.A.; Exon, J.L.T.," Experience with using Rogowski coils for transient measurements", Pulsed Power Technology, IEE Colloquium on 20 Feb 1992 Page(s):61 - 64. [8] Z. Mingjuan, D. J. Perreault, V. Caliskan"Design and Evaluation of an Active Ripple Filter with Rogowski Coil Current Sensing", Power Electronics Specialists Conference, PESC99, 30th Annual IEEE, vol. 2, pp. 874 -880, 1999. [9] P.C.J.M. van der Wielen, J. Veen, P.A.A.F. Wouters, and E.F. Steennis,“Sensors for On-line PD Detection in MV Power Cables and there Locations in Substations”, Proceedings of the 7th International Conference on Properties and Applications of Dielectric Materials June 1-5 2003, Nagoya, pp.215-219. [10] W. Panosch, K. Schongrundner, K. Kominek,“20 kV Overhead Lines with Covered Conductors”, CIRED 2001, IEE Conference Publication No. 482, 18-21 June 2001.

______________________________________________________________________________ Page 22 of 22

1

EVALUATION OF THE REPRESENTATION OF POWER SYSTEM COMPONENT MAINTENANCE DATA IN IEC STANDARDS

61850/61968/61970

Lars Nordström KTH Royal Institute of Technology

Department of Electrical Engineering [email protected]

1 INTRODUCTION

On the re-regulated market, a utility is put under pressure both from its owners expecting return on investments and regulatory agencies keeping a watchful eye on the level of tariffs. In addition to these new business constraints, a number of factors are imposing new challenges. In the industrialized part of the world, the power grids were built during the first half of the 20th century. Although replacements and expansions are made continuously, the bulk of the equipment is reaching the age for which it was originally designed. This aging is apparent both in primary as well as in secondary equipment. Additionally, the workforce set to maintain the power grid is aging, resulting in utilities loosing key-competence as people move on to retirement, or is forced to leave as part of cost-cuttings.

It is natural that under these circumstances a utility will focus on improving its power system maintenance processes, the goal being to prolong the life-time of equipment, thereby providing better return on employed capital. Equally obvious is that the utilities will strive to make their maintenance processes cost efficient and to a large degree automated so as not to become dependant on single individuals. The answer to both these requirements is increased use and integration of Information Systems (IS) in support of the maintenance processes. This development is reported on in part, in [1]. At the same time, the power industry, especially power distribution, is still only in the early stages of implementing modern methods and tools for asset management such as condition based maintenance and Reliability-centered maintenance (RCM) see for example [2].

1.1 Utility Information Systems Traditionally the Information Systems currently in use at most utilities are not tailored for direct support of power systems maintenance. At a high level of abstraction, the systems in use can be divided into four groups, see [3] or for a similar taxonomy [4].

SCADA – Supervision, Control and Data Acquisition systems used for monitoring and control of the power network and its components. Typically this contains data such as Line, Breaker, Station etc.

Business Support Systems similar to those found in other industries including support for payroll, accounts receivable, general ledger as well as purchasing and inventory control. This group of system normally manages data such as Project, Time-sheet, Cost center etc.

2

Customer Management Systems consisting of support systems for sales, marketing and billing. The most basic support being a database of existing customers as well as functionality for creating invoices. Typically this group of systems contain data such as Customer, Contract and Meter.

Finally the Geographic Information System being the central repository for information about the network and its components with a focus on its geographic location. This group of system typically manages data like Breaker, Line, and Station etc.

Unfortunately the data and functionality needed to support condition base maintenance is spread across several different systems and groups of systems. Information Systems used for equipment diagnostics are also highly specialized and built by the diagnostic equipment vendors in conjunction with the diagnostic equipment. This means that results from diagnostics may not be readily available to integrate with for example an equipment inventory.

Additionally integration of, and implementation of new Information Systems is very often plagued with budget or schedule overruns as well as performance and/or functionality problems [5]. In the power industry there exist a series of initiatives to remedy these problems. Among the notable are EPRI’s CCAPI [6] initiative now included in the IEC's standardized CIM model for information exchange, see [7]. The IEC 61968 series goes a step further and strives to define Information Exchange specifically for Distribution Management Systems [4]. For an overview of the relation between these initiatives and others, please see [8]. These initiatives are focused on modeling data and information in order to facilitate the integration of existing and new IS into an enterprise wide Utility IS architecture.

Additionally, the IEC has recently released an encompassing standard for communication and control of substations - the IEC 61850, see [9] for an introduction and overview. The purpose of this standard is to facilitate the integration of Intelligent Electronic Devices, or IEDs from different vendors for substation automation. The data models contained in these standards are further described in section 3 below.

1.2 Research Approach The scope of the work presented here in is to investigate how maintenance data is represented and considered, if at all, in the major IEC standards related to Information systems, i.e. 61968/70 and 61850. The focus has been on power system component data needed for condition based maintenance. The underlying idea is to investigate to which extent that the data needed for condition based maintenance is represented in the various standards.

The approach for the work has been to study literature on condition based maintenance in order to identify which data is stored about conducted inspections and measurements. This has then been cross checked with the IEC standards to verify whether the condition based data is represented, and if so where, in the standards.

1.3 Scope of the work The scope of the study has been limited to condition based maintenance of power cables and specifically to the data storage aspects of partial discharge (PD) and dielectric response measurements to determine insulation quality in such cables. This scope has been selected in order to allow a sufficiently detailed treatment of the subject.

3

2 CONDITION BASED MAINTENANCE

Condition based maintenance is a wide encompassing term which implies that maintenance, in terms of repairs or replacement is done based on the condition of the component. In [10] the term is defined as: An equipment maintenance strategy based on measuring the condition of equipment in order to assess whether it will fail during some future period, and then taking appropriate action to avoid the consequences of that failure.

From the above definition we understand that information needs to be stored about the condition of the equipment and that this information needs to be accessible for future analysis. Additionally from [11] we have that “The full potential of condition and maintenance information cannot always be realized by using traditional techniques of data handling and analysis. There are often underlying trends or features of the data that are not evident from the usual analysis techniques. Such detail and trends can be important for the assessment of the equipment operation and there are increasing demands from operators and asset managers to fully exploit the capabilities of this data in order to optimize the utilization of high voltage electrical plant”

2.1 Required Information Structure To keep complete records of condition of high voltage cables, an information system needs to consider three types of data. First, Equipment data such as the identity and location of the cable section and related information. Second Activity data needs to be stored on the measurements done, when they where done and which methods were used. Third, Condition data such as the actual results of the measurements and the conclusions drawn on equipment status. This taxonomy is in line with [11] and [12], with the exception of a fourth category of data, Norms, which we here include in the equipment data.

Equipment data consists of static data about the component in question, e.g. a medium voltage cable, its location, where it is connected, when it was commissioned, the manufacturer, year of manufacture, technical specifications and so on. An information model must be able to store these data but also be able to represent relations between cables, joints and terminations as well as other cables. In other words, the data model must allow run-time updates.

Activity data consist of information about inspections and measurements made. The data stored includes type of measurement, when it was done, who did it, which sensors where used and so on. Of course, the information model must allow the activities to be related to equipment and vice versa.

Condition data is the actual measurement results, both in terms of measured quantities, but also the resulting measured value(s) and any conclusions drawn. The condition data is related both to the equipment and to the Activity.

In the following presentation of the standards and the ensuing analysis, these three terms will be used to describe the types of data needed.

2.2 Diagnosing Power cables For High Voltage cables the condition of the equipment is best determined by checking the status of the insulation, see [11]. For insulation diagnostics, two types of methods are most common,

4

Partial Discharge measurement and dielectric response measurement in time and frequency domains [13].

2.2.1 Data for Partial Discharge measurements Partial discharge measurement is the most common diagnostic method for power cables. Especially since techniques for measurement of PD with the cable on-site are available. There exist several different PD measurement techniques that can be either, electrical, optical or acoustic. Of these the electrical methods are most common. The most commonly used sensors are capacitive, inductive and capacitive-inductive types. There exists a large variety of electrical PD measurement methods differing in voltage and frequencies used and how these are varied or kept fixed during the measurements.

From [11] and [14] we have it that for Partial Discharge measurements the following parameters is of interest: Inception & Extinction Voltages, PD Magnitude, PD Intensity, PD Pattern i.e. harmful or less harmful, PD Location, i.e. in cable or in accessories and Mapping i.e. concentrated or scattered.

2.2.2 Data for Dielectric Response Measurements Just as for PD measurements, there exist several methods for dielectric response measurements. Dielectric response (DR) is an advanced tool and still requires more research and development to verify its applicability to different types of cables and degradation phenomena. The dielectric response changes when the insulation is degraded, and by measuring with different voltages and frequencies the status of the insulation can be investigated. It has been shown that use of Very Low Frequencies is an appropriate measurement technique for identification of water trees in XLPE insulated cables especially when combined with loss factor measurements.

2.2.3 Further considerations A complicating factor from an asset management perspective is that a circuit connecting two points may very well consist of several sections of cable connected with joints. These cable sections may also be of varying age and type. This implies that a complete record of the condition of a series of cable sections connecting two points will require data to be stored about several cable instances and joints.

2.3 Summary Summarizing the above measurement data we have for the three data categories defined above.

2.3.1 Equipment Data In addition to the obvious necessary information such as manufacturer, year of commission, location etc. Two groups of data are important. First thresholds and limits that the cable has been designed for and are permissible operating conditions. Second, data about how the cable is connected to other cable segments needs to be stored.

2.3.2 Activity Data The activity data that must be stored, in addition to obvious data such as time of measurement, and who performed it, is data about which measurement type that was used, which system was used and how it was calibrated.

5

2.3.3 Measurement Data Which measurement data that needs to be stored is based on which method is used for the diagnostic. For example for electric PD measurements using capacitive sensors the data is different than from dielectric response measurements at VLF combined with loss factor measurements.

3 STANDARDISED INFORMATION MODELS

This section is introduced with an overview of the standards included in this study. Following this introduction is an in depth presentation of how power system equipment is represented in the data models of IEC 61968/70 and 61850 respectively. The focus of these presentations is on data for condition based maintenance.

3.1 Information Modeling Information models and the modeling methods are at the core of the IEC standards. The models provide an image of the analogue world (power system process, switchgear) which is to be represented in the Information System. By standardizing the model, information exchange mechanisms can be specified which in turn are what the system vendors need to implement in order to be compliant with the standard.

3.1.1 The IEC 61850 standard The 61850 standard covers communication and automation of substations. As a consequence switchgear and power transformers have been extensively modelled while the standard contains less details regarding equipment outside substations, i.e. cables and over head lines and generating equipment. Still however the standard does include some details about the data related to conducting equipment although not as complete as the model contained in IEC 61968.

A core component in the 61850 standard is the logical node, which represents a data and functionality, similar to an object in object oriented analysis. The logical nodes are implemented in the IEDs and represent parts of, or complete real world objects. These real world objects need not necessarily be power system components but can also be abstract entities such as measurements. A logical node may be implemented across several IEDs or be contained within one IED. In total the 61850 standard defines 98 logical nodes, organized in 13 groups, see Table 1 below. Of the 98 logical nodes, some 50% have been extensively modeled.

Group Indicator Logical node groups

A Automatic Control

C Supervisory control

G Generic Function References

I Interfacing and Archiving

L System Logical Nodes

M Metering and Measurement

P Protection Functions

R Protection Related Functions

6

S a) Sensors, Monitoring

Ta) Instrument Transformer

Xa) Switchgear

Ya) Power Transformer and Related Functions

Za) Further (power system) Equipment a)

LNs of this group exist in dedicated IEDs if a process bus is used. Without a process bus, LNs of this group are the I/Os in the hardwired IED one level higher (for examplein a bay unit) representing the external device by its inputs and outputs (process image – see Figure B.5 for example).

Table 1 The 13 groups of logical nodes defined in the IEC 61850 standard [15]

3.1.2 The IEC 61968 & 61970 standards. The Common Information Model (CIM) standardized by the IEC [7] is built upon the CCAPI project initiated by EPRI. The objective of the CCAPI was to facilitate the integration of IT applications developed independently by different vendors. The initial focus was on EMS applications, but the scope has grown and through the development of the IEC 61968 series [4] of standards now cover most aspects of IS integration at utilities. Both these standards are built around the CIM which in turn provides a model for a complete power system using notation in UML, mainly in the form of Class Diagrams.

The CIM is a model that contains all major objects at a utility and in its grid. The model includes classes and attributes for the objects and perhaps most importantly the relationships between the objects. For convenience the model is partitioned into several packages but it is actually one single connected class diagram. In essence the CIM is data-driven. By modeling the utility and its grid using Class diagrams the CIM provides a comprehensive view of the attributes and relations of data that will be managed in the utility’s IS.

Technically, the CIM and its extensions for Distribution Management are developed by separate working groups in the IEC TC 57. The work is however well coordinated, and can be considered one collective modeling effort. In addition to the work within IEC, a number of extensions to address shortcomings of the CIM are being proposed by researchers. For example in [16] some aspects on more detailed requirements on equipment information for maintenance are proposed in terms of extensions to the CIM.

3.2 Representation of High Voltage cables The following two sections describe how power cables are represented in the two standards.

3.2.1 Power Cables in IEC 61850 In IEC 61850 information about power cables are contained in the Logical Node Group Z for further power system equipment. Power cables are represented by one single logical node with the acronym ZCAB [17]. The attributes of the ZCAB logical node are defined in [15], see Table 2 below for easy reference

7

Attribute Name Attr. Type Explanation T M/O

LNName Shall be inherited from Logical-Node Class (see IEC 61850-7-2) Data Common Logical Node Information LN shall inherit all Mandatory Data from Common Logical Node Class M EEHealth INS External equipment health O

EEName DPL External equipment name plate O

OpTmh INS Operation time O

Table 2 ZCAB logical node attributes as defined in IEC 61850 [15]

As is shown in Table 2 above very little information about power cables can be represented by the attributes in the logical node. This is perhaps not surprising given the standards focus on substation equipment. For a description of what the data attributes contain, please see Annex A. The Mandatory data inherited from the Common Logical Node can be found in Annex B.

The 61850 standard contains some other logical nodes of interest. These are however not specifically related to power cables, but can potentially be used by an IED manufacturer to store condition based data. For example the RFLO logical node, see Table 3 below, could very well be used to store information about a line segment. It does however not contain all the information needed.


LNName Shall be inherited from Logical-Node Class (see IEC 61850-7-2) Data Common Logical Node Information LN shall inherit all Mandatory Data from Common Logical Node Class M

OpCntRs INC Resetable operation counter O

Measured values FltZ CMV Fault Impedance M

FltDiskm MV Fault Distance in km M

Status Information FltLoop INS Fault Loop O

Settings LinLenKm ASG Line length in km O

R1 ASG Positive-sequence line resistance O

X1 ASG Positive-sequence line reactance O

R0 ASG Zero-sequence line resistance O

X0 ASG Zero-sequence line reactance O

Z1Mod ASG Positive-sequence line impedance value O

Z1Ang ASG Positive-sequence line impedance angle O

Z0Mod ASG Zero-sequence line impedance value O

8

Z0Ang ASG Zero-sequence line impedance angle O

Rm0 ASG Mutual resistance O

Xm0 ASG Mutual reactance O

Zm0Mod ASG Mutual impedance value O

Zm0Ang ASG Mutual impedance angle O

Table 3 The RFLO logical node which contains data related to faults in a circuit

Similarly, the logical node MMXU, see Table 4below, for measurements can be used to store data from measurements. However this node is also not specifically intended for diagnostic measurements, but rather holds general information for operative purposes.


LNName Shall be inherited from Logical-Node Class (see IEC 61850-7-2) Data Common Logical Node Information LN shall inherit all Mandatory Data from Common Logical Node Class M EEHealth INS External equipment health (external sensor) O

Measured values TotW MV Total Active Power (Total P) O

TotVAr MV Total Reactive Power (Total Q) O

TotVA MV Total Apparent Power (Total S) O

TotPF MV Average Power factor (Total PF) O

Hz MV Frequency O

PPV DEL Phase to phase voltages (VL1VL2, …) O

PhV WYE Phase to ground voltages (VL1ER, …) O

A WYE Phase currents (IL1, IL2, IL3) O

W WYE Phase active power (P) O

VAr WYE Phase reactive power (Q) O

VA WYE Phase apparent power (S) O

PF WYE Phase power factor O

Z WYE Phase Impedance O

Table 4 The MMXU logical node for measurements

The standard also contains a set of logical nodes related to diagnostics, however these are specialized for switchgear (3 logical nodes) and transformers (two logical nodes). These logical nodes do contain attributes such as for example the acoustic level of a partial discharge in a breaker.

3.2.2 Power Cables in IEC 61968/970 The 61968 and 61970 standards are extensive and cover an even larger scope than the 61850 standard. Since the 61970 and 61068 have different scope, the same physical resources, such as a power cable is modeled as several separate instances. For instance, a connection between two

9

points is modeled both as a Line for electric purposes and as a cable or over-head line for asset management purposes. For this study, only the asset management perspective is of interest. In IEC 61968, power cables are contained within in the LinearAssetHierarchy [18] shown in Figure 1 below.

Figure 1 The CableAsset Model in the Linear Asset hierarchy

The 61968 standard also contain classes to represent information about the inspection and maintenance activities. These are contained in the WorkInspectionMaintenance package, this model is shown in figure 2 below.

10

Figure 2 The WorkInspectionMaintenance model.

The 61970 standard provides Measurement package which contains models with data that can be used to represent results of measurements such as partial discharge diagnostics. The model is however very generic and intended for operational purposes, i.e. SCADA/EMS application, similar to the case for the measurements Logical node in IEC 61850, see 3.2.1 above.

4 ANALYSIS

The analysis has been done by verifying to which degree and format the different types of data defined in section 2.1 is available in the two standards. Please note that the purpose is not to select one of the standards above the other, but rather to identify potential overlap or areas of condition based data that have not received proper attention in the standards. Also note that it is not implied that the standards should contain the data specified in section 2.1.

11

4.1 Equipment Data For static equipment data both standards contain a basic subset of attributes that can be used to store information about the cable section. The 61968 model does contain slightly more detailed information about cable characteristics. Both however lack attributes to store information about values for which the cable has been design, i.e. limits or thresholds which shall not be exceeded.

In the 61968 standard, the relation between cable sections can be modeled, by using the relation with CircuitSections and Lines, see figure 1 above. This is however not possible with the Logical Nodes in 61850. The relation can of course be created in the system anyway, but this would require the addition of non-standardized relations between the logical nodes.

4.2 Activity Data Activity data is not available in the 61850 logical nodes, which is unsurprising since such information is not suitable to store at the IED level. The 61968 models provide sufficient entities and attributes to store information about performed inspections/diagnostics and the results and conclusions drawn from the results.

4.3 Measurement data The weakest point for both standards is the storage of measurement data. Both standards provide a generic measurement object that contain some attributes related to measurements. However, none of these are specifically tailored for PD or DR measurements. Very likely this is because many different methods exist and a standard that includes all necessary parameters will be too detailed.

4.4 Concluding remarks Clearly the standards investigated do not have sufficient attributes included to store detailed information about PD and DR measurements. This is especially true for measurement data which, depending in measurement method can be very extensive.

The IEC 61968 standard does however cater for a large share of the CBM data needs. The CableAssetModel combined with the WorkInspectionMaintenace model can be used to store the conclusions drawn from PD or DR measurements. Since specialized condition assessment tools like PD measurement equipment, are delivered as stand alone software packages. It can be claimed that it is not necessary for these applications to be standards compliant as long as they provide interfaces to external systems. These interfaces may very well be made compliant with the information exchange mechanisms which these standards also specify.

Additionally, the fact that a specific data point is included, or not, in a standard does not preclude vendors from providing that data in their system. As long as additional data is marked as additional to those specified in the standard and an interface is available to query it the data will be accessible and can be made available.

4.5 Further Work This study does not include the activities done within Cigrés working group D1.17 on HV Asset Condition Assessment Tools, Data Quality - Expert Systems. A more detailed study on the relation between the work done in these working groups and the results gained in this study is a suitable next step.

12

REFERENCES

[1] P.Verho, K. Nousiainen, "Advanced use of information systems provides new possibilities for asset management of high voltage devices," in Proc. NORD-IS 03 Nordic Insulation Symposium

[2] J. Endrenyi et.al. "The present status of Maintenance Strategies and the impact of Maintenance on Reliability”," IEEE Trans. Power Systems, vol. 16, No 4, pp. 638-646, Nov.,2001.

[3] C-G Lundqvist, J. Jacobsson, “Enhancing Customer Services by efficient integration of modern IT-systems for Asset Management and Network Operation, In Proc of Powercon 2002, Kunming, China, October 14-16, 2002.

[4] IEC 61968-1 Application integration at electric utilities –System interfaces for distribution management – Part 1: Interface architecture and general requirements, IEC 61968-1, 1st Ed, 2003-10.

[5] Standish Group, The CHAOS report, The Standish Group International, Inc, 2003, London, UK.

[6] Common Information Model CIM 10 Version, Report no 10019765, Final report Nov 2001, EPRI.

[7] IEC 61970-301 Energy management system application program interface (EMS-API) – Part 301: Common Information Model (CIM) Base, IEC 61970-301, 1st Ed, 2003-11.

[8] IEC TR 62357 Power system control and associated communications – Reference architecture for object models, services and protocols, IEC TR 62357, 1st Ed, 2003-07.

[9] IEC 61850-1 Communication networks and systems in substations – Part 1: Introduction and overview. International standard, IEC, Geneva Switzerland, 2003.

[10] http://www.maintenanceresources.com/ReferenceLibrary/MaintenanceManagement/KeyTerms.htm, Access 6 August 2005.

[11] B. Quak, E. Gulski, J.J. Smit, F.J. Wester, E.R.S. Groot “Database Support for Condition Assessment of Distribution Power Cables” in Conference Record of the 2002 IEEE International Symposium on Electrical Insulation, Boston, MA USA, April 7-10,2002.

[12] E. Gulski, F.J. Wester, B. Quak, .J. Smit, F.de Vries, M.B. Mayoral, “Datamining for Decision Support of CBM of Power Cables” in Proceedings of CIRED 17th International Conference on Electricity Distribution Barcelona, 12-15 May 2003.

[13] P. Hyvönen, B. Oyegoke, M. Aro, “Advanced diagnostic test and measurement methods for power cable systems on-site”, Technical report number TKK-SJT-49, Helsinki University of Technology, High Voltage Institute, Espoo, Finland 2001

[14] B. Oyegoke, P. Hyvönen, , M. Aro, “Partial Discharge Measurement as Diagnostic Tool for Power Cable Systems”, Technical report number TKK-SJT-45, Helsinki University of Technology, High Voltage Institute, Espoo, Finland 2001, ISBN 951-22-5394-1

[15] IEC 61850-7-4 Communication networks and systems in substations – Part 7-4: Basic communication structure for substation and feeder equipment – Compatible logical node classes and data classes. International standard, IEC, Geneva Switzerland, 2003.

13

[16] X. Dong, Y. Liu, F.A. LoPinto, K.P. Scheibe, S.D. Sheetz, Information Model for Power Equipment Diagnosis and Maintenance. In Proceedings of Power Engineering Society Winter Meeting, 2002. IEEE, 2002.

[17] IEC 61850-5 Communication networks and systems in substations – Part 5: Communication requirements for functions and device models, International standard, IEC, Geneva Switzerland, 2003.

[18] http://www.cimuser.org, revision CIM10r6 accessed August 6th 2005.

14

ANNEX A This annex lists the Common Data Attributes used in the IEC 61850 standard. N.B. Please ignore references to tables and documents within these tables.

DPL class Attribute Name

Attribute Type FC TrgOp Value/Value Range M /O/C

DataName Inherited from Data Class (see IEC 61850-7-2) DataAttribute

configuration, description and extension vendor VISIBLE STRING255 DC M

hwRev VISIBLE STRING255 DC O

swRev VISIBLE STRING255 DC O

serNum VISIBLE STRING255 DC O

model VISIBLE STRING255 DC O

location VISIBLE STRING255 DC O

cdcNs VISIBLE STRING255 EX AC_DLNDA_M

cdcName VISIBLE STRING255 EX AC_DLNDA_M

dataNs VISIBLE STRING255 EX AC_DLN_M

Services

As defined in Table 45

Table A.1 The Device name Plate Data attribute

INS class Attribute Name

Attribute Type FC TrgOp Value/Value Range M /O/C

DataName Inherited from Data Class (see IEC 61850-7-2) DataAttribute

status stVal INT32 ST dchg M

q Quality ST qchg M

t TimeStamp ST M

substitution subEna BOOLEAN SV PICS_SUBST

subVal INT32 SV PICS_SUBST

subQ Quality SV PICS_SUBST

subID VISIBLE STRING64 SV PICS_SUBST

configuration, description and extension

d VISIBLE STRING255 DC Text O

dU UNICODE STRING255 DC O

cdcNs VISIBLE STRING255 EX AC_DLNDA_M

cdcName VISIBLE STRING255 EX AC_DLNDA_M

dataNs VISIBLE STRING255 EX AC_DLN_M

Services

As defined in Table 13

15

ANNEX B Common Logical Nodes from which the Logical node inherit generic information.


LNName Shall be inherited from Logical-Node Class (see IEC 61850-7-2) Data Mandatory Logical Node Information (Shall be inherited by ALL LN but LPHD)

Mod INC Mode M

Beh INS Behaviour M

Health INS Health M

NamPlt LPL Name plate M

Optional Logical Node Information

Loc SPS Local operation O

EEHealth INS External equipment health O

EEName DPL External equipment name plate O

OpCntRs INC Operation counter resetable O

OpCnt INS Operation counter O

OpTmh INS Operation time O

Data Sets (see IEC 61850-7-2) Inherited and specialised from Logical Node class (see IEC 61850-7-2)

Control Blocks (see IEC 61850-7-2) Inherited and specialised from Logical Node class (see IEC 61850-7-2)

Services (see IEC 61850-7-2) Inherited and specialised from Logical Node class (see IEC 61850-7-2)

_____________________________________________________________________________

ON-LINE CONDITION MONITORING OF HIGH VOLTAGE CIRCUIT-BREAKERS

Mr. Shui-cheong Kam

Queensland University of Technology, Australia [email protected]:

1. Introduction Transients occur in power systems due to a large variety of causes. Circuit-breaker operation, faults, harmonic resonances and lightning all produce transients with different characteristics. Precursors of many different types of failures of power system equipment produce fast transients on power systems are as follows:

a) Identification of degradation and impending failure of power system equipment from fast transient over voltage

b) Intermittent faults between phase and ground may not cause circuit breakers to operate but may cause transient voltages such as trees briefly touching power lines blown by wind

c) Polluted insulators when wet may cause transient discharges d) Circuit-breakers originally considered to be restrike free are now known to restrike

during opening e) Occasionally on switching ferro-resonance may occur producing characterically

deformed system over-voltage There has been considerable interest in transients occurring in power systems [1, 2] because transients have caused operational problems with computers and other electronic equipment [3]. Previous research has been aimed at detecting the magnitude and duration of transients on power systems with a view to estimating the “quality” of the electricity supply [4, 5]. Many researchers have made use of computers as condition monitoring equipment and applied modern techniques such as wavelet analysis in the detection of transient phenomena [6, 7] and artificial neural networks (NNs) for classification of the type of events causing the disturbance [8, 9]. Lots of work has been done on transient disturbances over many years. Nowadays there is an interest in quantifying the distortion and avoiding their effect on power supplier waveforms. This field is referred as “Power Quality”. There has been considerable research work in this area but little attempt to interpret transient waveforms to provide information about the condition of power system equipment. Prognostic condition assessment which allowed most power equipment maintenance to be performed during regularly scheduled service rather than on an emergency basis after failure would greatly reduce the total cost. The needs in the power industry are similar to those for monitoring the main engines on Navy ships.

2 Methodology This project is examining the characteristics of transient phenomena occurring in power systems and explore the possibility of automatically processing real waveforms to obtain information about power system equipment condition. Examples that are considered include circuit-breaker restriking when switching shunt capacitor or reactors. To achieve this goal, we first create a database of three phase capacitor bank transient waveforms and relate this to real life data with

__________________________________________________________________________AM Course Report – Shui-cheong Kam Page 1

_____________________________________________________________________________

and without restrike to build up a database of typical waveforms. This database will then be used in the design of new methods of restrike detection and classification. Database will initially be populated with data from ATP (Alternative Transients Program) simulation which will be made for switching with restrikes and no restrike. Distinctive features of transient waveforms from the database will be determined for various conditions and pre-failure conditions using advanced computational techniques such as wavelet analysis, artificial neural network (NN) modeling, parallel NN and statistical methods as the proposed tools. It is also intended to test the algorithms on data obtained with a wide range of transients produced by phenomena other than switching. Opportunities to extend the work by examining the restrike and detection and classification of power system equipment deterioration caused by transient, is a more realistic situation that can be studied. With confidence has been developed in the effectiveness of the methodology, it is intended to explore the possibility of applying the procedures to other power system phenomena: ferroresonance, arcing, and conductor clashing…etc.

The writer has investigated capacitor switching and restriking features, using Wavelet Transform for feature extraction and a Self-organising Map neural model for data mining/visualization. The results of the high performance computing environment with the desktop computing environment are correlated. This PhD programme is aimed at initially developing diagnostic and prognostic algorithms for specific equipment operations to improve asset management, and then to develop a parametric estimation (sensitivity analysis) model and a power system equipment monitoring system that would locate and categorize impending failures by on-line analysis of transients.

3. Objectives of the Program of Research and Investigation The overall aim for the next three years full-time or equivalent of the doctoral research program is to investigate techniques for improving asset management by estimating power system equipment condition from transient disturbances waveforms.

An example of the estimating power system equipment condition from transient disturbance is that waveforms from transient phenomena tell us the information about circuit breaker delayed closing time, arcing and energy level or other information, which tells us whether the circuit breaker is healthy or has deterioration. This is the motivation of this research project. The other motivation is to improve asset management of power systems by on-line detection and analysis of power system transient phenomena. The outcome of the research is the techniques including an artificial NN expert system with a parametric estimation (sensitivity analysis) model and a power system equipment monitoring system for improving asset management to detect transient phenomena caused by power switching equipment deterioration.

The specific aim for the next three years or equivalent of the doctoral research program is to:

• develop initially diagnostic and prognostic algorithms for a specific operation such as capacitor switching

• develop a parametric estimation (sensitivity analysis) model and a power equipment monitoring system system that would locate and categorize impending failures by on-line analysis of transients


_____________________________________________________________________________

4. Literature Review This literature reviews includes knowledge discovery approach, electrical faults, transient analysis, detection and classification problems on power quality disturbance with an objective to develop an innovative research for a problem and appropriate methods the ideas engaged from other researchers. Special attention will be given to assumptions, problems encountered and methods to the problems that were encountered. The parameters and various neural models will be investigated to determine the research direction for developing diagnostic and prognostic algorithms for power system equipment with a parametric estimation (sensitivity analysis) model for improving asset management. The algorithms will be used to detect and analyse the waveforms and the artificial NN models for a quantitative trend index to indicate the power system equipment deterioration caused by transient phenomena for power system equipment failure prediction.

It is important to distinguish faults from other switching events, because switching operations may cause transients similar to fault induced transients. If we can identify the fault type, we can locate a specific fault and clear it. A transient is initiated whenever there is a sudden change of circuit conditions; this most frequently occurs when a switching operation or a fault takes place. A variety of methods have been considered and used for reducing transient inrush currents on capacitor bank switching. The surge impedance can be increased by the intentional addition of inductance in the form of a reactor. Resistors switching can be added to damp the oscillation. One approach is to use synchronous switching which means closing the switching device at a selected point in the cycle. This is done, ideally, when there is no voltage between the contacts. It requires careful sensing, precise and consistent mechanical operation of the switch and independent operation of the poles of the three phases. However, there is always the problem of restrike, the tendency for the intercontact gap to breakdown and establish current before the contacts physically touch. A phenomena occurs that if the natural frequency is high (if L or C or both are very small), the voltage across the switch contacts will rise very quickly and if this rate of application of the system recovery voltage should exceed the rate of buildup of dielectric strength in the medium between the contacts, the breaker will be unable to hold off the voltage and a so-called restriking will occur. This usually results in the switch carrying fault current for at least another half cycle which is described as the restriking voltage [10].

Artificial neural networks (NNs) have been applied successfully in detection and classification of power quality recognition problem and electrical fault location. Part of analysis of this research needs artificial NNs to classify power system events. The data obtained from modeling is fed to an artificial NN in order to classify the power system events that occur under any circumstances.

The following review will be covered:

• Knowledge discovery approach • Transient disturbances on power systems • Transients indicating with system failures and power system equipment deterioration • Diagnostic (classifer) techniques for classifying transient disturbances • Prognosis (projection and trenching) techniques for prediction of power system

equipment failure


_____________________________________________________________________________

5 Conclusion There has been extensive previous work on the detection of power system transient using wavelets and neural network. Based on the analysis of transient provided by very simple models and certainly there has been no attempt (except for van Karel with arcing fault) to investigate the arcing fault for classification and detection of disturbance. Most of research work are focused on education for customers about the ramifications of power power quality, new developments in instrumentation and network analysis are generally acknowledged as promosing factors towards solutions for power quality problems [81]. Most of power quality research work has been concerned with detecting and classifying transient disturbances in order to ascertain the “Power Quality” before appropriate mitigation action can be taken. In order to assure consumers that their electricity supply is free of disturbance as well as equipment sensitivity study, there has been extensive previous work on the detection of power system transient disturbances and fault location using wavelets and neural network. There is very limited research work for detecting of transient phenomena caused by power system equipment deterioration. Opportunities to extend the work by examining the detection and classification of power system equipment deterioration caused by transient is more realistic situation using advanced computational techniques such as wavelet analysis and artificial NN modeling.

Transients will be calculated for a number of well-known types of power system equipment maloperations using a powerful transient simulation program [3] that has been developed by the power systems research community over more than thirty years. This program has been demonstrated to give realistic and accurate solution of most types of complex transients in three-phase system. It is proposed to simulate those events occurring in power systems that are associated with power system equipment deterioration or failure that cause transients in the network. Events that will be investigated may include: restriking in power system equipment, disconnecting capacitor banks and three-phase reactors, temporary short term transients caused by brief contacts with trees and other objects, short duration voltage sags due to temporary arcing, voltage depression followed by fuse or recloser operation, and maloperation of motors and distributed generators if found necessary.

The literature review has investigated the parameters for detection and classification of the transient disturbances that most of the work on faults locator and classification algorithms concentrates on time and frequency domain while less attention is paid to restriking voltage. Previous research has been aimed at detecting the magnitude and duration of transients on power systems with a view to estimating the “quality” of the electricity supply. Therefore, there exists a possibility of developing diagnostic and prognosis algorithms for power system equipment including artificial NN models with wavelet analysis.

Based on the Wavelet analysis and artificial intelligence system are identified as tools for power quality applications [24], the following research tasks are proposed:

a) Initially we intend to determine if the approach can be used to identify the cause of capacitor and shunt reactor restriking when circuit breaker disconnects. This is a phenomenon that occurs infrequently that can cause degradation of circuit breakers.

b) The initial part of the research plan is to make a wide range of capacitor simulation to build up a suitable database with three phase system for suitability of operation and relate with and without restriking.


_____________________________________________________________________________

c) Exploring the possibility of automatic detection and classification of equipment deterioration from the characteristics of transient phenomena appearing on power networks. It is proposed to research the use of suitable analysis tool such as wavelet to extract features related to characteristic time between events.

d) Development of techniques for automatic recognition of events occurring on power distribution systems such as occurrence of faults, or switching of lines or other equipment.

e) Detection of abnormal conditions such as circuit breaker failures due to restriking voltage.

f) Development of fast algorithms for event detection and new techniques for classifying types of disturbances.

g) Application of these techniques to give network asset management by for example building up a history of abnormal events.

h) Feature extraction. i) Development of a parametric estimation (sensitivity analysis) model to indicate the

power system equipment deterioration caused by transient with Wavelet Transforms. Different NN will be explored to select appropriate error measure for different types of power system equipment.

6. Summary of Research and Results

Literature review for recent research on the power quality problem is directed to investigate techniques for estimating power system quipment condition from transient waveforms. The power quality problem is defined as any power problem manifested in voltage, current or frequency deviations that result in failure or misoperation of customer equipment.

The following problem is identified from the literature review:

• Is it possible to improve asset management of power systems by on-line detection and analysis of power system transient phenomena?

Using advanced computational techniques such as Wavelet Transform, Artificial NN, Parallel NN and Statistical Methods to solve the problems is used to estimate power system equipment condition from transient disturbances. The following sections will explore the investigation path undertaken within the research field with justifications and results to date.

6.1 Modeling of Power System Transient Disturbances Many researchers have used different algorithms and models for distribution lines study of transients for faults locating and classifying problems. The results of these researchers can be used to determine the parameters and simulate data for artificial NN training and models development. The efficiency and accuracy of each of the NN models will be assessed and finally the most suitable models for data simulation will be selected.

For parameters investigation and simulation data production each model will be analysed using ATP-EMTP. In this program ATPDraw is a graphical preprocessor with which the user can build up electric circuit. Based on the graphical drawing of the circuit, ATPDraw generates the ATP-EMTP file in the appropriate format. This output file then will be executed using ATP-EMTP software and the results will be saved in a given file, which will be used for analysis.


_____________________________________________________________________________

6.2 Investigating the Features Extraction Schemes for Power System Disturbances

Different features extraction schemes will be explored and an appropriate one such as the following method:-

• Obtaining the original transient signals using Teager’s operator [96] & [97]. • Down-sampling the signatures. • Obtaining the normalised auto-correlation functions of the primary functions. • Preparing feature patterns in a format suitable for input to a neural network classifier.

6.3 Design of New Methods of Restrike Detection and Classification with Diagnostic and Prognostic Algorithms Diagnostic and prognotic algorithms for power system equipment including artificial NN expert system will be developed with the available current transformers. Hardware will be developed and installed in a local substation. Software will be installed using the method as stated on 6.6. Quantification of severe restriking is accomplished by comparing the Minimum Quantisation Error (MQE) of a newly acquired waveform with a predetermined threshold. The alarm threshold is determined using the Uniformly Most Powerful Test without prior statistical information on the restrike signature. Numerical results show that properly selected threshold for MQEs ensures a high rate of severe restrike detection, following procedures developed by Kang [18, 98]. Alternatively, severe restriking is modeled using ATP. Digital data from simulations are applied in MATLAB. Results from different conditions are presented for a range of high voltage network conditions for the SOM training data. Equipment deterioration index k is defined to measure the difference between the discrepancy/errors for severe restriking and the discrepancy/errors normal restriking, which is used for a quantitative trend index for the circuit breaker condition.

ydiscrepancerrorsnormalEydiscrepancerrorssevereEk

/)(/)(

= (1)


_____________________________________________________________________________

Inputs:

Signal Processing Techniques such as Wavelet-Transform

Transient Data Format Conversion Waveforms from

power systems (simulation or measurement)

With MATLAB Based Feature Extraction for Detection

Diagnostic & Prognosis Algorithms for Classification and Power System Equipment Failure

Outputs: Detection and Selection of the Disturbance with Power System Equipment Deterioration Index

Figure 1. Detection, classification and estimation of a power system equipment deterioration index flowchart

Capacitor restrike modeling can be done in many different ways. The problem is the data required for the model. There are very complex restrike models, but manufacturers normally do not have the data to utilize these models. A simple model can be simulated by using a voltage controlled (flashover) switch or simply by putting a voltage controlled switch in “parallel” with a switch representing the Circuit Breaker. However the ATP rules do not allow switches to be directly paralleled- a small resistance (eg 1.0e-6 Ohms) must be inserted between the switch nodes. A switch is added in parallel to the original switch (using low value resistors) and set them up such that the first switch is closed at the start of the simulation, and opens at 20 ms, de energising the capacitor. The second switch is set up as a flashover switch (voltage controlled switch) which is arranged to flashover when the voltage across it reaches 15,000V. Restriking occurs because the trapped charge results in an elevated voltage across the switch. Usually Transient Recovery Voltage is also involved, and the peak voltage occurs at the first voltage peak after the initial current interruption occurred, i.e. after 10ms. If the restrike voltage is lower it can of course occur earlier, sometime after 5 ms.

The wavelet transform is utilized to extract additional features for differentiating normal capacitor switching and capacitor switching restrike.

Voltage starts to change as an additional feature is applied to capacitor switching, other than as stated in [77]. As shown in [99] Daubechies wavelets. Db5 have been found to produce good results. The scale/level is application dependent. In this case 2 scale/level is enough for this application.


_____________________________________________________________________________

The software method uses a SOM as a classifier which classifies the operating state of a circuit breaker into a capacitor switching state and a restrike voltage state. The artificial NN classifier is trained using a data set, which is generated by the Alternative Transients Program (ATP) program. The trained SOM will be then, used for on-line condition monitoring. The complete description of the parallel NN methodology is not within the scope of this project report.(Please refer to [100]). Nevertheless, it is important to discuss the basic design procedure, which involves the following steps:

• Feature selection • Architecture of SOM and its training algorithm • Performance evaluation

After the feature vectors have been obtained, feature vector normalization is applied to separate the vectors further to improve recognition performance. These vectors are used to train an artificial NN. Artificial NNs are slow when handling large input vectors. This is why feature extraction techniques are used to extract a limited number of features from the original signals as the inputs to the artificial NN as follows:

There were two sets of 150 training vectors for developed each test in the training set. The training results are listed in Tables 1. & 2.

Table 1. Desktop Computer Results

TYPE Rectangular Hexagon

Final quantization error for capacitor switching (normal restriking)

0.294 0.504

Final quantization error (severe restriking) 0.326 0.612

From equation (1), Equipment index for Rectangle Type 109.1294.0326.0

==k

Equipment index for Hexagon Type 191.1504.0612.0

==k

Table 2. High Performance Computer Results

TYPE Single CPU Four CPUs

Starting epoch-discrepancy 60 iteration for capacitor switching (normal restriking)

From 644.8617 to 48.57735

From 612.7516 to 19.41228

Starting epoch-discrepancy 60 iteration (severe capacitor switching)

From 644.8617 to 52.36634

From 612.7516 to 23.56772

From equation (1), Equipment index 078.157735.4836634.52

==k

Comparing the results from desktop computer and HPC, we can conclude that the HPC results are better than desktop computer for consistency in output results even different random numbers are generated with both single CPU and four CPUs, where as desktop computer generates different results each time. This is because HPCs do the jobs faster and do it more accurate within defined iterations (eg. number of loops). In other words, HPCs converge to the result more faster and more accurately. It should be pointed out that due to the diversity of cases in power disturbances, the neural model could be further trained and tested by field recorded


_____________________________________________________________________________

data. A Matlab code Codes for NNs (MLP, RBF, SOM, SVM and etc.) will be tested. Parallel SOM Fortran Codes will be tested under high performance computers. for neural network and parallel NN learning for selecting the appropriate error measure since they may perform differently over different ranges [87]. Modeling and simulation, pattern generation and design of the neural network based algorithm for a circuit breaker, as well as simulating results with and without parallel NN will be the next immediate tasks.

6.4 Controlled Experiments with Signatures and Field Data Using established models, an investigation will be conducted into the current diagnostic and prognostic techniques that model parameters for the current methods exhibit. From this investigation opportunities to over these restrictions with artificial NN software will be established. Based on these possibilities, the latest neural model software development for electrical engineering applications will be explored. Once the model is validated under simulations with online data, then the application to other online data will be commenced. The other online data will contain unknown parameters, and so the success of the new methods on this new data will not be straight forward to evaluate. The most promising means for evaluation is to use cross-validation of the model with the derived techniques in field evaluation. The performance criteria outlined previously in section 6.5, will form the basis of the evaluation in the controlled experiements.

6.5 Prototype Algorithms Development and Test with Different Methods Simulated data will be used to verify the model. Regression analysis of desired output and actual output will be performed to check the accuracy of the model. Therefore, once the final validation of the new model is established, considerations of implementation will be the research focus. The computational time required by portable/notebook computers is one consideration. Another is the amount of necessary data to provide the model enough information to be reliable. Both will be considered. Handling of the pre-processing or post-processing of the data as well as the noisy data and missing data will be also taken into the implementation. Different methods will be evaluated including neural network and statistical methods. Different NNs will be explored to select an appropriate error measure for different type of power system equipment The parametric estimation (sensitivity) model will be based on optimization principle or global optimization sensitivity analysis, which is derived from the book “Simulation Based Optimization”. The following variables will be used for the diagnostic and prognostic algorithms to determine: the time-to-failure estimation of power system equipment (e.g. severe restrikes for circuit breakers), correlating measured transient waveforms with the database of known waveforms.

1) feature extraction methods 2) a matter of degree defined for the word ‘severe’ 3) error measures from neural network (NN) software computation 4) ranges of the error measures for each type of NN with different performance measure

such as SOM 5) statistical quality control algorithms such as CUSUM 6) different software and computer hardware

6..6 Field Trials and Effectiveness Assessment


_____________________________________________________________________________

It is required to design a quantitative trend index to indicate the deterioration of power system equipment condition caused by transient phenomena. The parameters such as amount of data required, appropriate feature selection methods and appropriate NN model from the waveforms. It is likely that changes in the above parameters will provide information about the deterioration of power system equipment conditions. Wavelet Transforms will be explored as a time-scale technique one of the most appropriate method for effective processing and analysis of transient waveform. A number of wavelet based algorithms will be developed to determine the index on the basis of power system equipment deterioration that may cause changes in the amplitudes and time delays between transient phenomena from field evaluation [54] .

6..7 Develop a Condition Monitoring System for On-line Analysis of Circuit-breakers Once the model is validated under simulations with online data, then the application to other online data will be commenced. The other online data will contain unknown parameters, and so the success of the new methods on this new data will not be straight forward to evaluate. The most promising means for evaluation is to use cross-validation of the model with the derived techniques in field evaluation. The performance criteria outlined previously in section 6.5, will form the basis of the evaluation. Then a condition monitoring system will be developed that will locate and categorise impending failures by on-line analysis of transients.

6.8 Vadition of the Circuit Breaker Model with Diagnostic and Prognostic Algorithms For modelling air and SF6 circuit breakers normally Mayer-Cassie equations are being used. The problem for the particular circuit breaker (CB) is to determine the time constants in these equations. Modelling of the vacuum circuit breaker depends on what we need, a model to simulate the switching small inductive/capacitive currents or switching of high currents. Models are completely different. As to the first one, the arc voltage is +/- 20 V, so the model can be developed by IF orders in Models to observe when the arc reignits and when it extinguishes. The criteria is to compare TRV with the dielectric recovery of the VCB. The arc quenching is simulated by the critical di/dt and the current zero. Moreover we need the chopping currents that determines the first reignition.

For a high current modeling, the problem is to determine the post-zero current. There are also a few models derived fromthe so called Andrew-Vary equation.

Normally for the determination of the TRV caused for circuit breakers the Mayr and Cassie models are preferred instead of physical models, however as the behaviour of the arc conductivity under different conditions is different, the Mayr model is used for small currents (reactors and capacitors bank interruption) and the Cassie model for large currents (fault interruption). The characteristic for SF6, air and oil are given for the time constant, arc voltage and the power loss constant depending on the model. Data obtained from real CB operations could be useful for the model validation. There are modifications of the latter models or different ones like the kopplin and Urbanek models, unfortunately there is not an arc model included in the ATPDRAW library, however it is possible to create an user model in TACS or MODELS.


_____________________________________________________________________________

6..9 Conclusion of Results At this point in the research program a valid form of potentially improving asset management by developing diagnostic and prognostic algorithms for power system equipment in order to improve asset management. Based on the literature review the problem has been solved by previous researchers on feature extraction with Wavelet Analysis, auto correlation and normalisation before input to NN for training and testing, and the last state are trending and projection. Emerging technologies such as ATP modelling and parallel NN learning are introduced for the NN performance improvement as the distinguishing contribution from others and applying this diagnostic and prognostic algorithms for power system equipment condition monitoring.

The problem for the research project is waveforms recognition to identify the power system equipment deterioration for asset management condition monitoring. This is the pattern recognition or condition diagnosis problem. In solving this problem there are two approaches: mathematical and non-mathematical equations. It is impossible to derive all equations from the power system equipment. NN is more appropriate to solve this problem because of the learning and non-mathematical modeling but including all formulas. Although some people use fuzzy logic for the uncertainly or getting all the exact ruling from the power systems. NN is the ideal candidate to solve this problem. But NN requires a long computation time for training processes, therefore, parallel NN learning algorithms are implemented according to the Markov processes for decision making.

.A quantitative trend index has been developed to indicate the power system equipment deterioration caused by transient phenomena from field evaluation and will be validated with power equipment from site evaluation. The immediate task involves the evaluation of the neural model with simulated data and field recorded data. Then field evaluation will be the last part of the diagnostic and prognostic algorithms for power system equipment with on-line data.

7. References [1] K. J. Van Rensburg, “Analysis of Arcing Faults On Distribution Lines for Protection and

Monitoring,” in School of Electrical & Electronic Systems Engineering. Brisbane, Australia: Queensland University of Technology, 2003.

[2] A. Hussain, M. H. Sukairi, A. Mohamed, and R. Mohamed, “Automatic detection of power quality disturbances and identification of transient signals,” presented at Signal Processing and its Applications, Sixth International, Symposium, 2001.

[3] W. Jun and T. K. Saha, “Simulation of power quality problems on a university distribution system,” presented at Power Engineering Society Summer Meeting, 2000. IEEE, 2000.

[4] D. O. Koval, R. A. Bocancea, K. Yao, and M. B. Hughes, “Frequency and duration of voltage sags and surges at industrial sites-Canadian National Power Quality Survey,” presented at Industry Applications Conference, 1997. Thirty-Second IAS Annual Meeting, IAS ‘97., Conference Record of the 1997 IEEE, 1997.

[5] E. Styvaktakis, M. H. J. Bollen, and I. Y. H. Gu, “Expert system for voltage dip classification and analysis,” presented at Power Engineering Society Summer Meeting, 2001. IEEE, 2001.

[6] R. Flores, “Signal Processing Tools for Power Quality Event Classification,” Chalmers University of Technology, Goteborg 2003.


_____________________________________________________________________________

[7] J. Chung, E. J. Powers, W. M. Grady, and S. C. Bhatt, “An automatic voltage sag detector using a discrete wavelet transform and a CFAR detector,” presented at Power Engineering Society Summer Meeting, 2001. IEEE, 2001.

[8] E. Styvaktakis, M. H. J. Bollen, and I. Y. H. Gu, “Classification of power system transients: synchronised switching,” Power Engineering Society Winter Meeting, 2000. IEEE, vol. 4, pp. 2681-2686 vol.4, 2000.

[9] L. Reznik and M. Negnevitsky, “A neuro-fuzzy method of power disturbances recognition and reduction,” presented at Fuzzy Systems, 2002. FUZZ-IEEE’02. Proceedings of the 2002 IEEE International Conference on, 2002.

[10] A. Greenwood, Electrical Transients in Power Systems, Second Edition ed: John Wiley & Sons, Inc., 1991.

[11] S. Santoso and J. D. Lamoree, “Power quality data analysis: from raw data to knowledge using knowledge discovery approach,” presented at Power Engineering Society Summer Meeting, 2000. IEEE, 2000.

[12] M. M. Begovic and P. M. Djuric, “Power system disturbance monitoring using spectrum analysis,” presented at Circuits and Systems, 1996. ISCAS ‘96., ‘Connecting the World’., 1996 IEEE International Symposium on, 1996.

[13] S. J. Huang, C. L. Huang, and C. T. Hsieh, “Application of Gabor transform technique to supervise power system transient harmonics,” Generation, Transmission and Distribution, IEE Proceedings-, vol. 143, pp. 461-466, 1996.

[14] U. A. Khan, S. B. Leeb, and M. C. Lee, “A multiprocessor for transient event detection,” Power Delivery, IEEE Transactions on, vol. 12, pp. 51-60, 1997.

[15] S. T. Mak, “Application of a differential technique for characterization of waveform distortions,” presented at Power Engineering Society Winter Meeting, 2000. IEEE, 2000.

[16] O. C. Montero-Hernandez and P. N. Enjeti, “A fast detection algorithm suitable for mitigation of numerous power quality disturbances,” presented at Industry Applications Conference, 2001. Thirty-Sixth IAS Annual Meeting. Conference Record of the 2001 IEEE, 2001.

[17] I. Y. H. Gu, M. H. J. Bollen, and E. Styvaktakis, “The use of time-varying AR models for the characterization of voltage disturbances,” presented at Power Engineering Society Winter Meeting, 2000. IEEE, 2000.

[18] P. Kang, “On-line Condition Assessment of Power Transformer On-load Tap-Changers Transient Vibration Analysis Using Wavelet Transform and Self Organising Map,” in School of Electrical and Electronic Systems Engineering. Brisbane: Queensland University of Technology, Australia, 2000, pp. 247.

[19] G. T. Heydt and K. J. Olejniczak, “The Hartley series and its application to power quality assessment,” presented at Industry Applications Society Annual Meeting, 1991., Conference Record of the 1991 IEEE, 1991.

[20] M. Moechtar, T. C. Cheng, and L. Hu, “Transient stability of power system-a survey,” presented at WESCON/’95. Conference record. ‘Microelectronics Communications Technology Producing Quality Products Mobile and Portable Power Emerging Technologies’, 1995.

[21] A. Poeltl and K. Frohlich, “Two new methods for very fast fault type detection by means of parameter fitting and artificial neural networks,” presented at Power Engineering Society 1999 Winter Meeting, IEEE, 1999.

[22] Z. Ye and B. Wu, “Simulation of electrical faults of three phase induction motor drive system,” presented at IEEE 32nd Annual Power Electronics Specialists Conference, 2001., 2001.


_____________________________________________________________________________

[23] Z. Ye and B. Wu, “Simulation of electrical faults of three phase induction motor drive system,” presented at Power Electronics Specialists Conference, 2001. PESC. 2001 IEEE 32nd Annual, 2001.

[24] W. R. Anis Ibrahim and M. M. Morcos, “Artificial intelligence and advanced mathematical tools for power quality applications: a survey,” Power Delivery, IEEE Transactions on, vol. 17, pp. 668-673, 2002.

[25] S. M. Halpin, “An improved simulation algorithm for the determination of motor starting transients,” presented at Industry Applications Society Annual Meeting, 1994., Conference Record of the 1994 IEEE, 1994.

[26] E. S. Lee and A. S. Herbert, Jr., “Fault isolation within power distribution systems,” presented at Telecommunications Energy Conference, 1991. INTELEC ‘91., 13th International, 1991.

[27] S. Ertem and Y. Baghzouz, “A fast recursive solution for induction motor transients,” Industry Applications, IEEE Transactions on, vol. 24, pp. 758-764, 1988.

[28] A. D. Stokes and W. T. Oppenlander, “Electric arcs in open air,” Journal of Physics D: Appl. Phys, vol. 24, pp. 26-35, 1991.

[29] M. Akke and J. S. Thorp, “Improved estimates from the differential equation algorithm by median post-filtering,” presented at Developments in Power System Protection, Sixth International Conference on (Conf. Publ. No. 434), 1997.

[30] T. Segui, P. Bertrand, H. Guillot, P. Hanchin, and P. Bastard, “Fundamental basis for distance relaying with parametrical estimation,” Power Delivery, IEEE Transactions on, vol. 16, pp. 99-104, 2001.

[31] Z. M. Radojevic, V. V. Terzija, and N. B. Djuric, “Numerical algorithm for overhead lines arcing faults detection and distance and directional protection,” Power Delivery, IEEE Transactions on, vol. 15, pp. 31-37, 2000.

[32] M. Fikri and M. A. H. El-Sayed, “New algorithm for distance protection of high voltage transmission lines,” Generation, Transmission and Distribution [see also IEE Proceedings-Generation, Transmission and Distribution], IEE Proceedings C, vol. 135, pp. 436-440, 1988.

[33] A. C. Parsons, W. M. Grady, E. J. Powers, and J. C. Soward, “Rules for locating the sources of capacitor switching disturbances,” presented at Power Engineering Society Summer Meeting, 1999. IEEE, 1999.

[34] D. D. Sabin, T. E. Grebe, D. L. Brooks, and A. Sundaram, “Rules-based algorithm for detecting transient overvoltages due to capacitor switching and statistical analysis of capacitor switching in distribution systems,” presented at Transmission and Distribution Conference, 1999 IEEE, 1999.

[35] E. Styvaktakis, M. H. J. Bollen, and I. Y. H. Gu, “Classification of power system transients: synchronised switching,” presented at Power Engineering Society Winter Meeting, 2000. IEEE, 2000.

[36] P. P. Pericolo and D. Niebur, “Discrimination of capacitor transients for position identification,” presented at Power Engineering Society Winter Meeting, 2001. IEEE, 2001.

[37] M. Kezunovic and Y. Liao, “Fault location estimation based on matching the simulated and recorded waveforms using genetic algorithms,” presented at Developments in Power System Protection, 2001, Seventh International Conference on (IEE), 2001.

[38] S. Santoso, J. D. Lamoree, and M. F. McGranaghan, “Signature analysis to track capacitor switching performance,” presented at Transmission and Distribution Conference and Exposition, 2001 IEEE/PES, 2001.


_____________________________________________________________________________

[39] H. D. Hagerty and W. G. Barie, “Switched capacitors control feeder regulation with thyristor motor starter,” presented at Petroleum and Chemical Industry Conference, 2002. Industry Applications Society 49th Annual, 2002.

[40] D. F. Peelo and E. M. Ruoss, “A new IEEE Application Guide for Shunt Reactor Switching,” Power Delivery, IEEE Transactions on, vol. 11, pp. 881-887, 1996.

[41] Z. Ma, C. A. Bliss, A. R. Penfold, A. F. W. Harris, and S. B. Tennakoon, “An investigation of transient overvoltage generation when switching high voltage shunt reactors by SF<sub>6</sub> circuit breaker,” Power Delivery, IEEE Transactions on, vol. 13, pp. 472-479, 1998.

[42] H. Ito, “Controlled switching technologies, state-of-the-art,” presented at Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES, 2002.

[43] Takemoto, “Surge Propagation Characteristics on Submarine Repeatered Line,” IEEE Transactions on Communications, vol. 26, pp. 1421-1425, 1978.

[44] H.-S. Song, H.-G. Park, and K. Nam, “An instantaneous phase angle detection algorithm under unbalanced line voltage condition,” presented at Power Electronics Specialists Conference, 1999. PESC 99. 30th Annual IEEE, 1999.

[45] J. Pienaar and P. H. Swart, “Ferro-resonance in a series capacitor system supplying a submerged-arc furnace,” presented at Africon Conference in Africa, 2002. IEEE AFRICON. 6th, 2002.

[46] P. J. Moore, E. J. Bartlett, and M. Vaughan, “Fault location using radio frequency emissions,” presented at Developments in Power System Protection, 2001, Seventh International Conference on (IEE), 2001.

[47] G. Brauner and C. Hennerbichler, “Voltage dips and sensitivity of consumers in low voltage networks,” presented at Electricity Distribution, 2001. Part 1: Contributions. CIRED. 16th International Conference and Exhibition on (IEE Conf. Publ No. 482), 2001.

[48] D. C. Robertson, O. Camps, Mayer; J. S. , and W. B. Gish, “Wavelets and electromagnetic power system transients,” IEEE Transactions on Power Delivery, vol. 11, pp. 1050-1056, 1995.

[49] O. Poisson, P. Rioual, and M. Meunier, “Detection and measurement of power quality disturbances using wavelet transform,” presented at Harmonics And Quality of Power, 1998. Proceedings. 8th International Conference on, 1998.

[50] C. Xiangxun, “Wavelet-based measurement and classification of power quality disturbances,” IEEE Transactions on Power Delivery, vol. 17, pp. 38-39, 2002.

[51] C. H. Lee and S. W. Nam, “Efficient feature vector extraction for automatic classification of power quality disturbances,” Electronics Letters, vol. 34, pp. 1059-1061, 1998.

[52] C.-M. Chen and K. A. Loparo, “Electric fault detection for vector-controlled induction motors using the discrete wavelet transform,” presented at American Control Conference, 1998. Proceedings of the 1998, 1998.

[53] G. T. Heydt, P. S. Fjeld, C. C. Liu, D. Pierce, L. Tu, and G. Hensley, “Applications of the windowed FFT to electric power quality assessment,” Power Delivery, IEEE Transactions on, vol. 14, pp. 1411-1416, 1999.

[54] P. Kang and D. Birtwhistle, “Condition assessment of power transformer onload tap changers using wavelet analysis and self-organizing map: field evaluation,” Power Delivery, IEEE Transactions on, vol. 18, pp. 78-84, 2003.

[55] M. Kezunovic and Y. Liao, “A novel software implementation concept for power quality study,” Power Delivery, IEEE Transactions on, vol. 17, pp. 544-549, 2002.

[56] I. R. M. Kezunovic, C.W. Fromen, D. R. Sevcik, “Expert System Reasoning Streamlines Disturbance Analysis,” IEEE Computer Applications in Power, pp. 15-19, 1994.


_____________________________________________________________________________

[57] E. Styvaktakis, “Automatic Power Quality Analysis,” in Department of Electric Power Engineering and Department of Signals and Systems. Goteborg: Chalmers University of Technology, 2002, pp. 218.

[58] M. Kezunovic, X. Xu, and Y. Liao, “Advanced software developments for automated power quality assessment using DFR,” IEEE Computer Applications in Power, pp. 1-6, 2002.

[59] M. Wang, G. I. Rowe, and A. V. Mamishev, “Real-time power quality waveform recognition with a programmable digital signal processor,” presented at Power Engineering Society General Meeting, 2003, IEEE, 2003.

[60] S. Santoso, W. M. Grady, E. J. Powers, J. Lamoree, and S. C. Bhatt, “Characterization of distribution power quality events with Fourier and Wavelet Transforms,” IEEE Transactions on Power Delivery, vol. 15, 1998.

[61] S. S. W. M. G. E. J. P. J. L. S. C. Bhatt, “Characterization of Distribution Power Quality Events with Fourier and Wavelet Transforms,” IEEE Transactions on Power Delivery, vol. 15, 1998.

[62] J. Chung, E. J. Powers, W. M. Grady, and S. C. Bhatt, “Power disturbance classifier using a rule-based method and wavelet packet-based hidden Markov model,” Power Delivery, IEEE Transactions on, vol. 17, pp. 233-241, 2002.

[63] M. Kezunovic and Y. Liao, “The use of genetic algorithms in validating the system model and determining worst-case transients in capacitor switching simulation studies,” presented at Harmonics and Quality of Power, 2000. Proceedings. Ninth International Conference on, 2000.

[64] S. Vasilic and M. Kezunovic, “Fuzzy ART neural network algorithms for classifying the power systems faults,” IEEE Computer Applications in Power, pp. 1-9, 2002.

[66] P. K. Dash, S. K. Panda, A. C. Liew, and K. S. Lock, “Tracking power quality disturbance waveforms via adaptive linear combiners and fuzzy decision support systems,” presented at Power Electronics and Drive Systems, 1997. Proceedings., 1997 International Conference on, 1997.

[67] J. S. Lee, C. H. Lee, J. O. Kim, and S. W. Nam, “Classification of power quality disturbances using orthogonal polynomial approximation and bispectra,” Electronics Letters, vol. 33, pp. 1522-1524, 1997.

[68] M. H. J. Bollen, M. R. Qader, and R. N. Allan, “Stochastical and statistical assessment of voltage dips,” presented at Tools and Techniques for Dealing with Uncertainty (Digest No. 1998/200), IEE Colloquium on, 1998.

[69] L. D. Zhang and M. H. J. Bollen, “A method for characterizing unbalanced voltage dips (sags) with symmetrical components,” Power Engineering Review, IEEE, vol. 18, pp. 50-52, 1998.

[70] M. Wang, P. Ochenkowski, and A. Mamishev, “Classification of power quality disturbances using time-frequency ambiguity plane and neural networks,” presented at Power Engineering Society Summer Meeting, 2001. IEEE, 2001.

[71] W. Kexing, S. Zhengxiang, C. Degui, W. Jianhua, and G. Yingsan, “Digital identification of voltage sag in distribution system,” presented at Power System Technology, 2002. Proceedings. PowerCon 2002. International Conference on, 2002.

[72] E. J. Bartlett and P. J. Moore, “Analysis of power system transient induced radiation for substation plant condition monitoring,” in Generation, Transmission and Distribution, IEE Proceedings-, vol. 148, 2001, pp. 215-221.

[73] E. J. M. Bartlett, P.J., “Analysis of power system transient induced radiation for substation plant condition monitoring,” in Generation, Transmission and Distribution, IEE Proceedings-, vol. 148, 2001, pp. 215-221.


_____________________________________________________________________________

[74] H. N. Nagamani, S. N. Moorching, Channakeshava, and T. Basavaraju, “On-line diagnostic technique for monitoring partial discharges in capacitor banks,” presented at Solid Dielectrics, 2001. ICSD ‘01. Proceedings of the 2001 IEEE 7th International Conference on, 2001.

[75] P. Kang, D. Birtwhistle, and K. Khouzam, “Transient signal analysis and classification for condition monitoring of power switching equipment using wavelet transform and artificial neural networks,” presented at Knowledge-Based Intelligent Electronic Systems, 1998. Proceedings KES ‘98. 1998 Second International Conference on, 1998.

[76] C. H. L. S. W. Nam, “Efficient feature vector extraction for automatic classification of power quality disturbances,” Electronics Letters, vol. 34, pp. 1059-1061, 1998.

[77] S. Santoso, W. M. Grady, E. J. Powers, J. Lamoree, and S. C. Bhatt, “Characterization of distribution power quality events with Fourier and wavelet transforms,” Power Delivery, IEEE Transactions on, vol. 15, pp. 247-254, 2000.

[78] J. Huang, “A Simulated Power Quality Disturbance Recognition System,” School of Computing and Information Technology, University of Western Sydney, Las Vegas TR No. CIT/18/2003, June 22-26 2003.

[79] A. J. V. Miller and M. B. Dewe, “The application of multi-rate digital signal processing techniques to the measurement of power system harmonic levels,” Power Delivery, IEEE Transactions on, vol. 8, pp. 531-539, 1993.

[80] A. Chandrasekaran and A. Sundaram, “Unified software approach to power quality assessment and evaluation,” IEEE Computer Applications in Power, pp. 404-408, 1995.

[82] X. X. M. Kezunovic, Y. Liao, “Advanced Software Developments for Automated Power Quality Assessment Using DFR,” IEEE Computer Applications in Power, pp. 1-6, 2002.

[83] Y. H. Song, A. T. Johns, and Q. Y. Xuan, “Neural network based techniques for distribution line condition monitoring,” presented at Reliability of Transmission and Distribution Equipment, 1995., Second International Conference on the, 1995.

[84] B. Perunicic, M. Mallini, Z. Wang, and Y. Liu, “Power quality disturbance detection and classification using wavelets and artificial neural networks,” presented at Harmonics And Quality of Power, 1998. Proceedings. 8th International Conference on, 1998.

[85] F. Mo and W. Kinsner, “Probabilistic neural networks for power line fault classification,” presented at Electrical and Computer Engineering, 1998. IEEE Canadian Conference on, 1998.

[86] T. Funabashi, A. Poeltl, and K. Frohlich, “Two new methods for very fast fault type detection by means of parameter fitting and artificial neural networks&rdquo,” Power Delivery, IEEE Transactions on, vol. 15, pp. 1344-1345, 2000.

[87] G. Vachtsevanos and P. Wang, “Fault prognosis using dynamic wavelet neural networks,” presented at AUTOTESTCON Proceedings, 2001. IEEE Systems Readiness Technology Conference, 2001.

[88] S.-L. Hung, C. S. Huang, C. M. Wen, and Y. C. Hsu, “Nonparameetric identification of a Building structure from experimental Data Using Wavelet Neural Network,” Computer-Aided Civil and iNfrastructure Engineering, vol. 18, pp. 356-368, 2003.

[89] C. R. Parikh, M. J. Pont, N. B. Jones, and F. S. Schlindwein, “Improving the performance of CMFD applications using multiple classifiers and a fusion framework,” Transactions of the Institute of Measurement and Control, vol. 25, pp. 123-144, 2003.

[90] P. C. Pendharkar and J. A. Rodger, “Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption,” Decision Support Systems, vol. 36, pp. 117-136, 2003.


_____________________________________________________________________________

[91] U. Seiffert, “Artificial Neural Networks on Massively Parallel Computer hardware,” ESANN’2002 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 24-26 April, pp. 319-330, 2002.

[92] D. Ernst, M. Glavic, and L. Wehenkel, “Power Systems Stability Control: Reinforcement Learning Framework,” IEEE Transactions on Power Systems, vol. 19, pp. 427-435, 2004.

[93] M. Stace and S. M. Islam, “Condition monitoring of power transformers in the Australian State of New South Wales using transfer function measurements,” presented at Properties and Applications of Dielectric Materials, 1997., Proceedings of the 5th International Conference on, 1997.

[94] S. F. Hiebert and R. B. Chinnam, “Role of artifcial neural networks and wavelets in on-line reliability monitoring of physical systems,” IEEE Transactions on Neural Networks, pp. 369-374, 2000.

[95] E.-W. Lee, J.-G. Kim, T.-S. Kim, and S.-C. Lee, “A study on the reduction method and the analysis of VCB switching surge for high voltage induction motor,” Transactions of the Korean Institute of Electrical Engineers, vol. 43, pp. 761-769, 1994.

[96] R. B. Dunn, T. F. Quatieri, and J. F. Kaiser, “Detection of transient signals using the energy operator,” presented at Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on, 1993.

[97] J. F. Kaiser, “On a simple algorithm to calculate the ‘energy’ of a signal,” presented at Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on, 1990.

[98] P. Kang and D. Birtwhistle, “Self-organsing map for fault detection,” ANNIE ‘99 Smart Engineering Design, St Louise, 1999.

[99] X. Xu and M. Kezunovic, “Automated feature extraction from power system transients using Wavelet Transform,” IEEE Computer Applications in Power, pp. 1994-1998, 2002.

[100] J. E. Vitela, U. R. Hanebutte, J. L. Gordillo, and L. M. Cortina, “Comparative performance study of parallel programming models in a neural network training code,” International Journal of Modern Physics, vol. 13, pp. 429-452, 2002.

28 July 2005


RELIABILITY OF PROTECTIVE SYSTEMS: RELAYING PHILOSOPHY AND COMPONENT REALIBILITY

Gabriel Olguin ABB Corporate Research [email protected]

Introduction Reliability evaluation is an important and integral part of the planning, design and operation of all engineering system; from the smallest and simplest one to the largest and most complex system. There are many definitions of reliability and the term itself means different things for different people. A broad and frequently used definition of reliability says: “Reliability is the probability of a device performing its purpose adequately for the period of time intended under the operation conditions encountered” [1]. In the context of power systems, reliability is the knowledge field within power engineering that treats the ability of the power system to perform its intended function [2]. Even though restricted to power systems, this is still a rather wide definition since “the intended function” can be interpreted in many ways. In particular when referring to reliability of protective system (a power system component) the definition of reliability needs to be more precise. Since the main objective of a protective system is to isolate the faulted area, there are two contrasting ways for the protective system to fail to perform its intended function. The relay may trip when it should not or may not trip when it should. We will review a number of relevant concepts regarding protective relaying that will help us to conceptualize the reliability of protective systems. The assessment of reliability as such is not new since engineers have always tried to operate systems free from failures. In the past however reliability was mainly achieved from qualitative and subjective judgment. Engineering judgment will always be necessary but formal numerical techniques are nowadays available to help in the task of reliability assessment. In practice, system reliability concerns the development of methods to predict the long term expected performance of the system, in particularly in power system reliability, in terms of number and duration of interruptions. Prediction should be understood here as a long term guess of the expected performance of the system. No reliability analysis will come to a conclusion like “an interruption of service will occur at 20:00 in the feeder number 12 of substation 10”. In turn a reliability analysis may conclude that “there is a high probability that the number of interruptions affecting feeder 12 of substation 10 exceeds 4 interruptions per year”. The simplest prediction method is based on extending the historical behavior of the system. This approach basically assumes that what has happened will happen again. Other more elaborated techniques model each power system component as a stochastic model and interconnect them to model the functional behavior of the system. The system under study is split into stochastic components. The choice of the components is rather arbitrary obeying basically to purpose of the study. A complete HV/MV substation could be a single component for the purpose of reliability analysis of power supply to a particular consumer. On the other hand one single protective relay could be modeled by several stochastic components.

1


Before presenting some analytic and numerical techniques aimed at performing reliability assessment, some basic but important concepts regarding protective relaying will be introduced. Protective Relaying Relaying is the field of electric power engineering concerned with the principles of design and operation of equipment that detect abnormal power system conditions and initiate corrective actions as quickly as possible in order to return the power system to its normal state [3]. These intelligent equipments are called relays or protective relays and the quickness of their response is an essential element of protective relaying systems. The function of protective relaying is to cause the prompt removal from service of any element of a power system when it suffers a short circuit or it starts to operate in any abnormal manner that might cause damage or interfere with the effective operation of the rest of the system [5]. The relaying equipment is aided in this task by circuit breakers that are capable of disconnecting the faulty element when they are called to do so by the relaying equipment. Fusing is employed when relays and circuit breakers are not economically justifiable. Circuit breakers are usually located so that each power system component (generator, transformer, bus, transmission line, etc.) can be completely disconnected from the rest of the system. Circuit breakers must be able to momentary carry the maximum short circuit current available at the point of installation. They may even able to withstand closing on such a short circuit condition. Relaying must operate not only to protect the system from short circuit faults but also from other abnormal conditions that may result in damage to the system. In particular, the protection of rotation machines considers a number of relays (or a unique multifunction relay) aimed at protect the machine against several abnormal conditions such as over and under frequency, over and under excitation, vibrations, loss of prime mover, non-synchronized connection, unbalanced currents, sub-synchronous oscillations, etc. In some cases the relay task is to signalize the existence of a failure or abnormal condition by activating an alarm, in others cases the relays command a circuit breaker action. In all these protective functions relays, signal transducers and circuit breakers are interconnected to provide quick alarm and/or complete removal of the protected equipment. Another function of protective relaying is to provide indication of the location of and type of failure. Such data will assist in expediting the repair. To be effective the relating practice must observe fundamental principles such as protection zones, primary and back up protection, sensitivity, selectivity, dependability and security. All these concepts and principles and other relevant protective relaying concepts are defined and discussed in the following paragraphs. Protective relaying definitions Failure versus fault: failure is the termination of the ability of an item to perform its required function whereas faults are short circuits caused by dielectric breakdown of the insulation system. A failure does not need to be fault, but a fault usually leads to a failure. Faults can be categorized as self-clearing, temporary, and permanent. A self-clearing fault extinguishes itself without any external intervention. A temporary fault is a short circuit that will clear after the faulted component (typically an overhead line) is de-energized and reenergized. A permanent fault is a short circuit that will persist until repaired by human intervention [4].

2

Protective relaying is the term used to signify the science as well as the operation of protective devices, within a controlled strategy, to maximize service continuity and minimize damage to property and personnel due to system abnormal behavior. The strategy is not so much that of protecting the equipment from faults, as this is a design function, but rather to protect the normal system and environment from the effect of a system component which has become faulted [6]. Note that this definition explicitly sates that the operation of the relaying system must follow a controlled and predefined strategy. Relays are devices designed to respond to input conditions in a prescribed manner and, after specified conditions are met, to cause the contact operation or similar abrupt change in associated electric control circuits. Inputs are usually electrical but may be mechanical, thermal, or other quantities. Limit switches and similar simple devices are not relays. A relay may consist of several units, each responsive to a specific input, with the combination of units providing the desired overall performance characteristic of the relay [7], [8]. Electromechanical relay is a relay that operates by physical movement of parts resulting from electromagnetic, electrostatic, or electrothermic forces created by the input quantities. In contrast a static relay is a relay or relay unit in which the designed response is developed by electronic, solid-state, magnetic or other components without mechanical motion [8]. Digital relaying (also called microcomputer relaying or numerical relaying) is a relaying system in which a processor or microcomputer is used to implement the protection functions. A digital relay consists of the following main parts: processor, analog input system, digital input system, independent power supply. The main difference in principle between digital and electromechanical and static relays is in the way input signal are processed. The input signals (currents and voltages from measurement transformers) are analog signals. They are directly imposed to electromagnetic windings or electronic circuits in non-numerical relays. In digital or numerical relays, the analog currents and/or voltages are converted into digital signals by using analog to digital converters (A/D) before being processed by the processor. Analog and digital filters are used to adjust the signals and protect digital components [9]. Reach of a relay is the extent of the protection afforded by a relay in terms of the impedance or circuit length as measured from the relay location. The measure is usually to a point of fault, but excessive loading or system swings may also come within reach or operating range of the relay [8]. Zone of protection is that segment of a power system in which the occurrence of assigned abnormal condition should cause the protective relay system to operate [8]. Primary protection zone is a region of primary sensitivity where primary relays should operate for prescribed abnormalities within that zone. Backup relays are relays outside a given primary protection zone, located in an adjacent zone, which are set to operate for prescribed abnormalities within the primary protection zone and independently of the primary relays [6]. Local backup protection is a form of backup protection in which the backup protective relays are at the same station as the primary protective relays. Remote backup protection is a form of backup protection in which the protection is at a station other than that at which has the primary protection [8]. Backup should not be confused with redundancy. Sensitivity in protective systems is the ability of the system to identify an abnormal condition that exceeds a nominal pickup or detection threshold value and which initiates protective action when the sensed quantities exceed that threshold [6]. Selectivity of a protective system is a general term describing the interrelated performance of relays, breakers, and other protective devices. Complete selectivity is obtained when a minimum amount of equipment is removed from service for isolation of a fault or other abnormality [8].

3

Reliability of a relay or relay system is a measure of the degree of certainty that the relay or relay system will perform correctly. It denotes certainty of correct operation together with assurance of against incorrect operation from all extraneous causes. Therefore reliability of protective systems involves two aspects: first, the system must operate in the presence of a fault that is within its zone of protection and, second, it must refrain from operating unnecessarily for faults outside its protective zone or in absence of fault [8]. Dependability of a relay or relay system is the facet of reliability that relates to the degree of certainty that the relay will operate correctly [8]. Security of a relay or relay system is the facet of reliability that relates to the degree of certainty that a relay or relay system will not operate incorrectly [8]. Most protection systems are designed for high dependability meaning that a fault is always cleared by some relay. As a general rule it can be said that relying system becomes less secure as it is more dependable. In other words the more dependable the protective relaying, the less secure its operation. In present-day designs, there is a bias towards making them more dependable at the expense of some degree of security. Consequently, a majority of relay system mal-operations are found to be the result of unwanted trips caused by insecure relay operations [3]. Redundancy of a relaying system is the quality a relaying system that allows a function to operate correctly, without degradation, irrespective of the failure or state of one partition, since another portion performs the same function [8]. Redundancy should not be confused with backup. Incorrect relay operation refers to any output response or lack of output response by the relay that, for the applied input quantities, is not correct. Failure mode is a process of failure of equipment that causes a loss of its proper function [8]. Relays present basically two failure modes. Failure to trip in the performance of a relay or relay system is the lack of tripping that should have occurred considering the objectives of the relaying system design. False tripping in the performance of a relay system is the tripping that should not have occurred considering the objectives of the relaying system design [8]. Introduction to Reliability Analysis Reliability is the probability of a device performing its purpose adequately for the period of time intended under the operating conditions encountered. The reliability assessment of a system means the calculation of a set of performance indicators. An example of a reliability indicator is the average number of times per year that certain equipment is not available due to failures. System reliability analysis is principally the analysis of a large set of unwanted system states that might occur in the future. The result of this state analysis is then used to calculate the various performance indicators. The reliability assessment starts with the creation of relevant system events. Not every event is equally likely to happen. The reliability assessment creates events that will bring the system in an unhealthy system state which is then analyzed. A particular engineering system, for example a relay, may be simple or complex. A complex system consists of many different functional subsystems in which each subsystem can be divided into elements, components or even sub-subsystems. In any case the reliability of the system depends on the reliability of the elements, components, and subsystems. This section introduces a few basic methodologies for reliability assessment of engineering systems. The treatment is brief since most of the material presented here is readily available in the referred literature [1], [6], [10].

4

Stochastic Model for Reliably Assessment In order to assess the reliability of an engineering system, the system has to be represented by a number of stochastic components. Each component of the system is treated as one single object in the reliability analysis. Examples of components may be a motor, a line, a diode, a protection device, a voltage or current transformer, a digital to analog converter, etc. A component may exhibit different “component states”, such as being available, being in reparation, being open, being closed, being stand-by, etc. For some reliability assessments all these possible states may have to be accounted for. Normally a reduction is made to a few of these states. For instance a simple two-state model could consider the operative and inoperative states. A three-state model could further distinguish between repairs (forced outages) and maintenance (planned outages). Then the model would consider the following three states: operative, planned-maintenance and forced outage. In particular in the assessment of reliability of protective relaying the multi-failure modes need to be properly modeled. A single relay exhibits more than two failure modes which lead to more than three states. It can fail shorted or fail open. Whether this relay-failure causes the failure of the relaying system in which the relay is installed depends on the circuit configuration and the design of the relaying system. Complex protective systems have many failure modes. A simple circuit breaker can fail to open on command, to close on command, to break a current, to make a current, etc. A stochastic component is a component with two or more states which have a random state-duration and for which the next state is selected randomly from the possible next states [11],[12]. A stochastic component changes abruptly from one state to another at unforeseen moments in time. The state transitions occur instantaneously and unpredictably. If we would monitor a four-state stochastic component over a long period of time we could get a graph as the one depicted in Figure 1. Because the behavior of the component is stochastic, another graph will appear even if we would monitor an absolute exact copy of the component under exactly the same conditions. For stochastic component models, the state duration (time in that state) and the next state are stochastic quantities.

Xo

X1

X2

X3

to t1 t2 t3 t4 t5tn

Figure 1: Example of monitored states of a stochastic component

5

The basic quantity in reliability assessment is the duration D (time) for which a component stays in a given state. This duration is a stochastic quantity, as its precise value is unknown. The word “precise” should be understood in the sense that although we do not know the value of the stochastic quantity, we know the possible values it might have. For example, the time until the next unplanned trip of a generator is unknown (otherwise it would a planned trip), but nobody would expect a trip everyday or that the generator does not present any failure in ten years. This is a wide range for practical purposes, but for actual generators a much smaller range of expected time to failure can be justified by historic data. The basic question about a stochastic quantity is the range of its expected values (or outcomes). Which outcome can be expected and with what probability? Both the outcome probability Pr(D) and its range can be described by a single function; the Cumulative Density Function, which is designed by FD(τ).

)Pr()( ττ ≤= DFD (1) The probability of a negative duration is zero and the probability of duration less than infinite is one. This is:

0)0Pr()0( =≤= DFD (2) 1)Pr()( =∞≤=∞ DFD (3)

The Probability Density Function (PDF), which is designed by fD(τ), is the derivative of the cumulative density function FD(τ). The PDF gives a first insight into the possible values for D, and the likelihood of it taking a value in a certain range.

)()( ττ

τ DD Fddf =

(4)

τττττ

τ ∆∆+≤≤

=→∆

)Pr(lim)(0

Df D

(5)

∫∞

=0

1)( ττ dfD

(6)

The survival function (SF), RD(τ), is defined as the probability that the duration D will be longer than τ. It is the complement of FD(τ).

)(1)Pr()( τττ DD FDR −=>= (7) For a specific component, D is the lifetime and the survival function gives the probability for the component functioning for at least a certain amount of time without failures. The hazard rate function (HRF), hD(τ), is defined as the probability density for a component to fail, for a certain time τ, given the fact that the component is still functioning at τ. This is:

)()(Pr(

lim)(0 τ

ττ

τττττ

τD

DD R

fDDh =

∆>∆+≤≤

=→∆

(8)

The hazard rate function is an estimate of the unreliability of the components that are still functioning without failures after a certain amount of time. An increasing HRF signals a decreasing reliability. The expected value of a function g of a stochastic quantity D is defined as

∫∞

=0

)()())(( τττ dfgDgE D

(9)

The expected value of D itself is its mean ED which is defined as

6

∫∞

⋅==0

)()( ττ dfDDEE DD

(10)

The variance VD is defined as the second central moment 222 ))(()())](([ DEDEDEDEVD −=−= (11)

The Exponential Distribution plays a special role in reliability evaluation. The negative exponential distribution is probably the most widely known and used distribution in reliability evaluation of systems. It is defined by

λτλτ −= efD )( (12)which makes that

λτλτ −−= eFD 1)( (13)λτ =)(Dh (14)

λ1

=DE

(15)

21λ

=DV

(16)

Haz

ard

Rat

e Fu

nctio

n

Time

Normal oparating or useful life

Wear-outWear-in

Figure 2: Typical hazard rate for electric component as function of time

The most important factor for it to be applicable is that the hazard rate function should be constant. It is frequently used in system reliability evaluation and there are a number of reasons for that.

• Using non-exponential distribution makes that most reliability evaluation techniques currently available can no longer be used.

• Even the studies that are able to use non-exponential distribution (e.g. Monte Carlo simulation) often still use exponential distribution, because of the lack of data.

• The lack of experience with non-exponential distribution makes that the results of such study are hard to interpret.

• In actual systems there is a mixture of components with different ages. The system is not built at once but grows over the time, incorporating new lines, equipment, etc. Preventive

7

maintenance is performed on components at different times and some components are replaced after faults.

• Most of the components in use are in their so-called useful operating time, where the failure rate is reasonably constant. See Figure 2.

Calculation Methods When component models and their functional interrelation have been decided, it is time to choose a calculation method. There are several calculation methods available in books and technical papers [1],[4],[10],[11],[12],[13],[14]. They can be divided into two basic categories: 1) analytical and 2) simulation methods. The analytical methods are well developed and have been used for years. These methods represent the system by mathematical models (blocks) and evaluate the reliability using mathematical solutions. The exact mathematical equation can become quite complicate that some approximations are needed when the system is too complex or involves too many components. A range of approximate techniques has been developed to simplify the required calculations. These techniques aim at calculating the expected value of the index and not its statistical distribution. The expected value or mean value, however, does not provide any information on the variability of the indicators. The distribution probability on the other hand provides both a pictorial representation of the way the indicators vary and important information on significant outcomes, which, although they occur infrequently, can have very serious effects on the system [13]. In order to obtain an appreciation of the variability of the outcomes, Monte Carlo simulation technique can be applied. This technique does not perform any analytical calculations, but instead simulates the stochastic behavior of the system. The main advantage of Monte Carlo method is that any component model, with any distribution, and any system detail can be included in a Monte Carlo simulation. However, it may be questionable whether the distribution probability associated with the component failure, component restoration and scheduled maintenance, etc. are known sufficiently in detail to apply them in a reliability study. If not, this advantage disappears. On the other hand, the main disadvantage of the simulation approach is that the more reliable the components of the system are, the greater the effort required for assessing the reliability of the system. In the following section a brief review of the basic methods for reliability evaluation is presented. Network Modeling or Reliability Block Diagrams The reliability of any physical system depends on its configuration, in other words on the arrangement and connection of the various components that make up the system design. The physical arrangement leads to a particular functional logic that describes a given function and its associated failure modes. The logic can be described and analyzed in several ways and equations can be written to compute the reliability of that logic. It should be emphasized that it is the logic that is being analyzed rather than physical arrangement. Network modeling, also referred to as reliability block diagramming, is the simplest method to represent the particular logic of a system. The method can only be used for basic component arrangements but it is still powerful enough to describe and analyze the reliability of simple systems. Blocks representing devices’ functions are connected in parallel or in series to represent the stochastic behavior of the system’s functionality. Components are said to be in series if they all must work for the system success or alternately if only one needs to fail for the system failure. On the other hand, components are said

8

to be in parallel if only one needs to be working for the system success or alternatively if all must fail to cause the system failure. According to the functional connection of the components the reliability of the whole system is found by applying two rules taken from probabilities principles:

• The success probability of a system consisting of several components in series is equal to the multiplication (or convolution) of the individual success probabilities.

• The failure probability (the complement of the success probability) of a system consisting of several components in parallel equal the multiplication (or convolution) of the individual failure probabilities.

In the modeling process the block diagram is not normally identical to the physical arrangement of components in the system. The operational or functional reliability logic must be translated in terms of series or parallel connections so that the model represents the reliability behavior of the network.

1 3 4

2

Figure 3: Reliability evaluation by network model; a simple example As a simple example consider the system shown in Figure 3. Two transformers (1 and 2) are connected in parallel to transport energy from one (3) extreme to another (4). The extreme points represent buses of the substation, which are also subject to failures. The probability of failure in buses is Qb, while each transformer has a probability of failure equal to Qt. Then the probability of success Ps of this simple system is

QbQtQtQbPs ⋅−−−⋅−= )1)(1(11 (17)In this case it has been supposed that any of the two transformers can supply the total load. This same model can be applied to another system as far as the model represents the functionality of the system. The model could for example be used to analyze the reliability of a relay where blocks 3 and 4 would be the relay’s contacts and 1 and 2 full redundant coils aimed at actuating a power circuit breaker. Protective relaying however presents some complexities that make it other techniques more suitable for analyzing it. To deal with systems where parallel and series rules are not enough, additional techniques have been developed. A commonly used method is the minimum cut set. The technique aims at describing the reliability of systems with complex functional networks. A minimum cut set is a set of components of the system whose failure leads to the failure of the entire system. The failure of block 3 in figure 3 leads to the failure of the entire system, the same can be said of block 4. Components in a given cut set are in parallel because they all must fail to cause the system fail. It is not trivial to identify minimum cut sets in stochastic networks. The following minimum cut sets can be identified from Figure 3: 3, 4,1,2. After identifying the minimum cut sets, these are connected in series, because the system fails if any one of the cut set fails. Evaluation of the whole system is then performed combining series and parallel rules.

9

Discrete Markov Chains The Markov approach can be applied to the random behavior of systems that vary discretely (chains) or continuously (process) with respect to the time and space. This random variation is known as a stochastic process. Not all stochastic process can be modeled by Markov chains. In order to the Markov approach be applicable, the behavior of the system must be characterized by lack of memory [13],[15]. This is, the future state of the system is independent of all past states except the immediately preceding one. In addition the process must be homogenous which means that the probability of making a transition from one state to another is the same at all times [15]. In simple words, the Markov chain approach is applicable to those systems whose behavior can be described by a probability distribution function that is characterized by a constant hazard rate, i.e., exponential distribution. Only if the hazard rate is constant does the probability of making a transition between two states remains constant at all points of time [1]. If the probability is function of time or the number of discrete steps, then the process is non-homogeneous and non-Markovian [15]. In the particular case of reliability evaluation, the space is normally represented as a discrete function since this represents the discrete and identifiable states in which the system and its components can reside, whereas time may either be discrete (chain) or continuous (process).

3/4

1

2

1/2

1/2

1/4

Figure 4: A two-state Markov chain model

The basic concepts of Markov modeling can be illustrated by considering a simple two-state system. Each state represents a particular condition of the system under study. Figure 4 shows a two-state system, where the probabilities of remaining in or leaving a particular state in a finite time are also shown. These probabilities are assumed to be constant for all times. This is a homogeneous Markov chain since the next state only depends on the present state and the movement between states occurs in discrete steps. Since the system can only be either in state 1 or 2, the sum of probabilities of remaining in and leaving must add one. For the simple two states shown in Figure 4, the probability of remaining in state 1 is 0.5 and is equal to the probability of leaving that state. On the other hand the probability of leaving state 2 is 0.25 whereas the probability of staying at state 2 is 0.75.

10

The formal definition of a Markov chain is given [15]. Let P be a (k,k)-matrix with elements Pi,j. A random process (X0,X1, …..) with finite state space S=(s1, ….. sk) is said to be a Markov chain with transition matrix P, if for all n, all i,j ∈ (1,…k) and all i0, … in-1 ∈ (1,…k) we have

),...Pr(001 inijn sXsXsX ===+

(18)

)Pr( 1 injn sXsX === +

jiP ,= For our two-state system, matrix P is given by

4/34/12/12/1

=P

(19)

The matrix P is known as the stochastic transitional matrix. It represents the transitional probabilities between states for one step of the Markov chain. In most reliability evaluation problems the initial condition or state is known. The initial condition of the Markov chain is known as the initial distribution and is represented by µ(0). The initial distribution is a vector of k elements each one representing the probability of the chain being at states i,….k. In our simple two-state system and supposing the initial state is 1, then µ(0) = (1 0). From Markov theory is known that a future distribution can be calculated from the initial distribution and the transitional matrix.

nn P)0()( µµ = (20)The aim of a reliability study is to find the reliability as time extends into the future. In mathematical terms we are interested in the stationary distribution of the Markov chain, this is µ(n), for n→∞. A stationary distribution there exits for any Markov chain and is unique if the chain is irreducible. However the convergence of the Markov chain to the stationary distribution is not guaranteed, unless the Markov chain is irreducible and a-periodic. A Markov chain is said to be irreducible if all states can (directly or indirectly) be reached from all others. A Markov chain is said to be a-periodic if all its states are a-periodic which implies that it is possible to return to the state in one transition. In practical reliability studies the Markov chain used are irreducible and frequently a-periodic. An efficient way to obtain the stationary distribution is based on the fact that once the stationary distribution has been reached, any further multiplication of the stochastic transitional matrix does not change the values of the limiting state probabilities. So, if µ(*) is the stationary distribution and P is the transitional matrix, then:

P(*)(*) µµ = (21) Where µ(*) is the vector of k unknown variables. The equation system described by (21) is redundant and for solving it we need to replace one of the k equations for an additional expression. Because the stationary probabilities sum up 1, the additional expression is:

1..... (*)(*)2

(*)1 =+++ kµµµ (22)

The transient behavior of the Markov chain is very dependent on the starting state, but the limiting values of the state probabilities are totally independent of the starting sate. However, the rate of convergence to the limiting probabilities can be dependent on the initial conditions and is very dependent on the probabilities of making transitions between states. From a reliability point of view the major demerit for this technique is the large number of states that are needed to model a complex system. The number of states increases exponentially with the number of studied factors. Many simplifying assumptions are needed to make the model manageable.

11

Continuous Markov Chain Processes In the field of reliability assessment of power systems, most references to Markov modeling refer to the continuous Markov process. Like Markov chain, the Markov process is described by a set of states and transitions characteristics between these states. However, state transitions in Markov process occur continuously rather than at discrete time intervals. In continuous Markov processes, state transitions are presented by transition rates and not by conditional probabilities as in Markov chains. Markov processes are easily applied to power system reliability since the failure rates are equivalent to state transition rates. As long as failure and repair rates are constant, continuous Markov modes are applicable. However becomes impracticable for large systems due to the large number of states involved. Markov processes are solved in a manner similar to Markov chains except that differential equations are utilized.

0Operative

1Failed

λ

ν

Figure 5: A two-state Markov process model

Figure 5 shows a Markov model for a two-state component. Lambda λ is the failure rate while ν is the repair rate. Usually the repair rate is designed with the symbol µ but we have taken ν to avoid confusion with the previous meaning given to µ. For the Markov model to be applicable both λ and ν must be constant. The failure rate λ is the reciprocal of the mean time to failure, with the times to failure counted from the moment the component begins to operate to the moment the component fails. Similarly the repair rate ν is the reciprocal of the mean time to repair. It can be shown that the probabilities of the component being found in state 0 and 1, given that the systems started at time t=0 in the state operative, is given by the following equations.

νλλ

λνν νλ

+⋅

++

=⋅+− tetP

)(

0 )(

(23)

νλλ

λνλ νλ

+⋅

−+

=⋅+− tetP

)(

1 )(

(24)

In reliability terms P0(t) is the availability, while P1(t) is the unavailability. The existence and uniqueness of the stationary distribution is subject to the same conditions as in the case of Markov chains. In the simple case of a reparable component of two states the limiting probabilities can be evaluated from (23) and (24) by letting t→∞.

λνν+

=)(0 tP

(25)

λνλ+

=)(1 tP

(26)

12

The number of sates increases as the number of components and the number of states in which each component can reside increase. For system with n components each one with 2 possible states the total number of states is 2n. The total size can therefore become unmanageable for large systems. One possible solution involves state truncation by neglecting states of very low probability of occurrence. Another involves approximate mathematical expressions that define the equivalent failure and repair rates of parallel and series components. An interesting application of Markov process to the reliability of protective system is referred in [6], Chapter 28, page 1230. According to the reference, the concept of unreadiness to refer to the condition that the protective system fails to respond when called to operate in presence of a fault was first introduced by Singh and Patton in 1980. This is a condition probality because depends on the occurrence of fault to be detected by the protective system. The authors suggest that the unreadiness probability can be estimated from field data.

respond upon to calledbeen has protection de timesofnumber respond tofailed has protection the timesofnumber

=C (27)

A Markov transition diagram representing the protection of a particular plant would be as shown in Figure 6.

Plant UP Plant DOWN

Figure 6: An state Markov process model for a protective system

The following states are depicted in Figure 6.

• State 1: the plant and the protective system are both operative (UP). • State 2: the plant is inoperative (DOWN) and the protective system UP. • State 3: the plant is UP and the protection DOWN. • State 4: both the plant and the protective system are DOWN. • State 5: the plant is UP and the protective system is being inspected.

5 plant UP

protection INSP

1 plant UP

protection UP

3 plant UP

protection DOWN

2 plant DOWN

protection UP

4 plant DOWN

protection DOWN

θp

θp

νp

λp

λs

Prot. UP

νs

νs

Prot. DOWN

λs

13

Tra tat a failure rate λp and at an

wing assumptions are considered appropriate. ainty.

of the failures of the plant. The m l

nsi ions between states occur according to the following rates: The protective system leaves the operative condition (UP state) inspection rate θp. The plant leaves the UP state at a power system failure rate λs. Plant repairing occurs at a rate νs. In addition, the follo

• Testing detects the failure of the protective system with cert• Testing of the protective system does not cause plant failure. • Failures of the protective system are statistically independent li iting probabilities pi corresponding to each state can be determined for the exponentia

distribution:

( )[ ])()(

1sppsppsp

spps

νθννθλλθλθνν

+++++ (28a)

p =

12 pps

s

νλ

= (28b)

13 pppp

p

λθλ+

= (28c)

( ) 14 ppsps

ps

λθνλλ+

= (28d)

)()(

5spp

pspppλθνλλθθ

+++

= (28e)

s expected the sum of limiting probabilities equals 1.

rms of the probability of staying at states AThe unreadiness probability can now be expressed in te1 and 3.

31

3pC =pp +

(29)

This in term of the given transition rates results:

psp

pCλ

= (30) λλθ ++

The only variable in this result that the engineer has much control over is the inspection rate θp.

onte Carlo Simulation simulation attempts to model the system behavior precisely as it occurs

The other two parameters are statistical failure rates of the plant and protection system and not easily changed. The inspection rate is the inverse of the mean time between inspections. A greater inspection rate θp (more frequent inspections) will reduce the probability of unreadiness. Note that the equation shows that for a non-inspected protective system (θp=0), the probability of unreadiness is just a function of the failures rates of protective system and plant. The plant failure rate is grater than the failure rate of the protective system, therefore the unreadiness probability decreases with an increasing plant failure rate. MA sequential Monte Carloin reality. Monte Carlo computer simulation utilizes pseudorandom number to model stochastic event occurrences. These pseudorandom numbers constitute a sequence of values, which,

14

although they are deterministically generated, have all the appearance of being independent uniform random variables (0,1). One method utilized for generating pseudorandom numbers is the multiplicative congruential method [11]. An initial value x0, called seed, is used to start the sequence and then recursively computes successive values of xn.

)mod(1 mxax nn −⋅= (31)In (31) a and m are given positive integers. The value of xn is the remainder of the quotient between axn-1 and m. Thus xn is either 0, 1, …, m-1. The quantity xn/m is taken as an approximation to the value of an uniform (0,1) random variable. After some finite value, at most m, of generated numbers a value must repeat it self. Once this happen the whole sequence will begins to repeat. Therefore the constants a and m have to be chosen so that this repetition occurs after a large number of simulations. For a 32-bit word computer it has been shown that m=231-1 and a=75 results in desirable properties. Random numbers generated by the computer are uniformly distributed in (0,1). To get the stochastic behavior of the reliability parameters (repair and failure time for example) these random numbers must be converted into the adequate distribution. This can be done by the method of inverse transform algorithm. However this method presents some limitation for the normal distribution, due to the difficulty to invert the cumulated distribution function. In this case the central limit theorem can be used to give the following method. Suppose we are interested in simulating a sequence of numbers with normal distribution, mean η and variance σ2. Then, generate twelve uniformly distributed numbers U1, U2, ….U12, sum them and subtract six, multiply the result by σ and sum it the mean value, the sequence presents normal distribution.

)6(12

1∑=

−+=l

li Ux ση

(32)

When applied for the purpose of reliability assessment, the Monte Carlo simulation typically analyzes system behavior for a specific period of time. An artificial history that shows the changes in component states is generated in chronological order (sequential simulation) by means of each simulation. The system reliability indicators are obtained from the artificial history. Because each simulation will produce “different” results many simulations are needed (and wished) to average the system indicators and study their distributions. An algorithm that could be applied to evaluate the reliability of a general protection system consisting of transducers, relays and circuit breakers consists of the following steps.

1. Set a simulation time. 2. Generate a random number for each component in the protection system (transducers,

relay, circuit breakers) and convert it into mean time to failure corresponding to the probability distribution of the component parameter.

3. Determine the component with minimum mean time to failure. This is the faulted component.

4. Cumulate the simulation time by summing mean time to failure of faulted components. If cumulative time is less than simulation time, continue. Otherwise output results.

5. Determine whether the protection function can still be performed given the existence of the faulted component. If the protection function cannot be performed output a protection system failure. If the protection function can be performed with the faulted component, modify the logic function of the protection system to represent the new condition.

6. Return to 2.

15

Sequential simulation is not needed if contingencies are mutually exclusive and the system behavior does not depend upon past events. If so, contingencies can be probabilistically selected and simulated in any arbitrary order. This kind of simulation is refereed as nonsequential Monte Carlo simulation. Generally speaking, nonsequential simulation is less computational intensive than sequential simulation, because simulation rules are simpler since contingencies are assumed not to interact with each other. In nonsequential simulation, contingencies are randomly selected from a pool of possible events based on contingency probabilities. A given contingence can be selected more than once. The selected contingencies are then simulated in any order, assuming they are all mutually exclusive. Like sequential simulation, a nonsequential Monte Carlo simulation is repeated many times to produce a distribution of results. Events and Fault Trees One method for evaluating the probability of failure of a system or component is the use of event trees and/or fault trees. Event trees are particularly suitable for the assessment of reliability of stand by and mission oriented systems [1]. A fault/event tree is a pictorial representation of all possible causes leading to a given failure/event. They provide a convenient method of graphically visualizing causal relations between events. This is done by constructing building blocks that are displayed as either gate symbols or event symbols. It is named tree because the graphical representation gradually fans out like braches of a tree. In the fault tree method a particular failure condition is considered and a tree is constructed that identifies the various combinations and sequences of other failures that lead to the failure being considered [1]. The method can be used for both qualitative and quantitative evaluation of system reliability. When used for quantitative evaluation, the causes of the system failure are gradually broken down into an increasing number of hierarchical levels until a level is reached at which reliability data is sufficient for a quantitative assessment to be made. The appropriate data is then inserted into the tree and combined together using the logic of the tree to give the reliability assessment of the complete system being studied. Some of the logical gate symbols used in fault and event trees are shown in Table 1 [6]. Table 1: Fault tree gate symbols

Gate name Causal relation AND Output event occurs if all input events occur

simultaneously OR Output event occurs if any of the input events occurs

INHIBIT Input produces output when conditional event occurs PRIORITY AND Output event occurs if all input events occur in the order

from left to right EXCLUSIVE OR Output event occurs if one, but not both, of the input

occur m OUT OF n Output event occurs if m out of n input events occur

The other graphical aid used in fault and event trees is the event symbol. Some common event symbols are shown in Table 2.

16

Table 2: Event symbols Event name Meaning

Circle Basic event with sufficient data Diamond Undeveloped event Rectangle Event represented by a gate

Oval Conditional event used with the inhibit gate House House event. Either occurring or not occurring

Triangles Transfer symbol Used together, the gate symbols and event symbols permit the engineer to describe how the occurrence of a particular failure mode or event of a given system can occur. Event trees (also known as success trees) are analogous to fault trees (the mathematical dual). In and event tree any event can be considered as the top event. There many types of protection systems, each one with its particularities. It is therefore not possible to develop a unique general model for all possible protection systems and each protection system must be modeled following its structure and functional design. However, there are common elements that can be included in a simple model from which more elaborated models can be built. Consequently, the following discussion relates to the simple protection system shown in Figure 7. The figure shows a protection system represented by functional blocks. These blocks can be related to most protection systems in which the fault detector (power system fault) includes appropriate transducers (current and/or voltage), the relay includes operating and restrain coils, the trip signal contains the trip signal device and associated power supply and the breaker is the actual device which isolate the power system faulted component [10]. It should be noted that although simple the block diagram could represent an overcurrent protection or a complex generator differential protection.

Breaker Fault detector Relay Trip signal

B FD R TS

Figure 7: Block diagram of a general protection system

Evaluation of failure to operate Consider a particular power system component (for example a power transformer) that is protected by two breakers B1 and B2. Assume that both breakers are operated by the same fault detector FD, relay R and trip signal device TS. Consider the event “the power system component has failed” then the event tree is as shown in Figure 8. The figure shows the sequence of events together with the outcomes of each event path. Note that since the success of the system is given by the opening of both breakers, only the outcome 1 leads to the successful operation of the protection system. The evaluation of the outcome probabilities is a simple exercise after the event tree has been deduced:

• Identify the paths leading to the required outcome • Calculate the probability of occurrence of each path by multiplying the corresponding

event probabilities in each path (event probabilities are discussed below)

17

• Calculate the outcome probability by summing the probabilities of paths leading to the outcome

Outcome EVENT FD R TS B1 B2 # Opens Fail to open O 1 B1,B2 O F 2 B1 B2 O O 3 B2 B1 O F F 4 B1,B2 O F 5 B1,B2 FAULT F 6 B1,B2 F 7 B1,B2

Figure 8: Event tree of a protection system, “O” means operates and “F” fails To illustrate consider the data contained in Table 2 where the probability of each path of the event tree shown in Figure 8 is shown.

Table 2: Paths probabilities for event tree in Figure 7 Path Path’s probability based on 1 year inspection interval [10]

1 0.950990 2 0.009606 3 0.009606 4 0.000097 5 0.009801 6 0.009900 7 0.010000

The outcomes probabilities are therefore: Probability B1 not opening = ∑ probabilities paths 3 to 7 = 0.03940 Probability B2 not opening = ∑ probabilities paths 2, 4 to 7 = 0.03940 Probability B1 and B2 not opening = ∑ probabilities paths 4 to 7 = 0.02980 The event probabilities needed to evaluate the paths probabilities in Table 2 are the probability that each device (FD, R, TS, B) will not operate when required. These are time-dependent probabilities and, as shown before, are affected by the time period between when they were last checked and the time when they are required to operate. The time at which the device is required to operate is a random variable. The probability of failing to respond when required can be represented by the average unavailability of the device between consecutive inspections or tests.

18

For a single device, where the time to failure distributes exponentially, and inspected on average every Tc time units, the average unavailability can be calculated by using the expression (33).

)1(1)1(1

0

c

cT

c

tT

c

eT

dteT

U λλ

λ−− −=−= ∫ (33)

Which for the condition λTc <<1 can be approximated to

2cTU ⋅

=λ (34)

For example if the failure rate of every device in Figure 6 (FD, R, TS, B) is 0.02 f/yr, then the average unavailability as function of the inspection time Tc is as shown in Table 3.

Table 3: Device unavailability based on a constant failure rate 0.02 f/yr Inspections intervals 3 months 6 months 12 months 36 months Unavailability (33) 0.002495838 0.004983374 0.0099336653 0.0294088930 Unavailability (34) 0.0025 0.005 0.01 0.03 The paths probabilities in Table 2 are then easily calculated by multiplying the corresponding event probabilities. Consider for example an inspection period of 12 months, then the probability of path 7 which implies the failure of the fault detector FD is 0.01. Similarly the probability of path 6 (success of FD and failure of relay R) is (1-0.01)*0.01=0.0099. Conclusions The reliability of protective system is extremely important for the secure operation of a power system. The protection of electrical systems is based on the principal of measuring field values (currents, voltages, etc) and disconnecting the faulted circuit or component when certain conditions are met. Reliability of protective system is based on 1) dependability and 2) security. Dependability is a measure of the confidence that the protective system will operate when it is required; security is a measure of the confidence that the relaying system will not operate unnecessary. Most protective systems in power systems are highly dependable. Consequently most mal-functions of protective relaying are operations when not required. This report presented a brief review of techniques and concepts applicable to reliability assessment of protective systems. It has been shown that several techniques are available to assess the reliability of protective systems. The lack may be in the availability of appropriate data required to feed such methods. References

[1] Billinton, R.; Allan, R. “Reliability Evaluation of Engineering Systems: Concepts and Techniques”. Pitman Advanced Publishing Program, London1985.

[2] Bhattacharya, K.; Bollen, M.H.J.; Daalder, J.E. “Operation of Restructured Power Systems”, Kluwer’s Power Electronics and Power Systems Series, Series Editor: M.A. Pai. 2001.

19

[3] Horowitz, S.H.; Phadke, A.G. “Power System Relaying”, Second edition Research Studies Press LTD, England 2003.

[4] Brown, R. “Electric Power Distribution Reliability”. Marcel Dekker Inc, Power Engineering Series, New York 2002.

[5] Mason, R.C. “The art and Science of Protective Relaying”, John Wiley & Sons Inc. New York 1956.

[6] Anderson, P.M. “Power System Protection”, IEEE Press Series on Power Engineering, 1998.

[7] ANSI/IEEE C37.90-1998 “IEEE Standard for Relays and Relay Systems Associated with Electric Power Apparatus”, IEEE 1989.

[8] IEEE Std C37.100-1992 “IEEE Standard definitions for Power Switchgear” IEEE 1992. [9] Wang, F. “On Power Quality and Protection”, Licentiate thesis Department of Electric

Power Engineering, Chalmers University of Technology, 2001. [10] Billinton , R., Allan, R. N. ”Reliability Evaluation of Power Systems”, Pitman

Advanced Publishing Program, 1984. [11] Bollen, M. “Understanding Power Quality Problems; Voltage Sags and

Interruptions”. IEEE Press Series on Power Engineering, New York 2000. [12] Van-Casteren, J. “Power System Reliability Assessment Using the Weibull-

Markov Model”. Licentiate thesis, Electric Power Engineering Department, Chalmers University of Technology, Gothenburg, Sweden 2001.

[13] Billinton, R., Wang, P. “Teaching Distribution System Reliability Using Monte Carlo Simulation”. IEEE Transaction on Power Systems, June 1998.

[14] Meeuwsen, J. “Reliability Evaluation of Electric Transmission and Distribution Systems”. Power Engineering PhD Thesis. Delft University of Technology, Holland, 1998.

[15] Häggström, O. “Finite Markov Chains and Algorithmic Applications”. Department of Mathematic Statistics, Chalmers University of Technology, 2001.

20

asset management in power systems - paper collection - 2005 - a ph.d. course

Documents