rwe innogy 6/1/2014page 1 o&m modelling for large scale offshore wind farms by use of markov...

RWE Innogy 04/10/23 PAGE 1

O&M Modelling for Large Scale Offshore Wind Farms

by Use of Markov Processes

6th of February 2013, EWEA 2013

Remote Wind Farms: Strategies and Concepts

Burcu Özdirik

RWE Innogy IUE – TUHH EWEA 2013 February 2013 Burcu Özdirik

BackgroundFar-shore Wind Farm Projects and Challenges

Various Design Parameters Number and type of WTG Distance between WTGs Wear and tear on the WTG

components Various Locations of Sites

Distance to shore Weather conditions Capacity factor

Operational Particulars Logistic strategies Working regulations Scheduled maintenance concepts

Offshore Wind Projects

Triton Knoll

North Hoyle

Gwynt y Môr

Rhyl Flats

Greater Gabbard

Nordsee Ost

Innogy Nordsee 1

Tromp Binnen

Thornton Bank

Projects in operation or under constructionProjects consented or in development

Atlantic Array

Dogger Bank

Galloper

Comparability with regards to the economic efficiency of O&M is required.


System

Characteristics

Modelling Framework

Offshore Operations

Markov Modellassembly - loop

monthly - loopannual - loop

Technical Availability Demand of Resourcesresults

Modelling Approach

System

AccessibilitySystem

Reliability


Scope of WorkInfluencing factors of Offshore O&M

Economic Efficiency

of Operations

Technical Availability

of OWF

Downtime [h]

Respective Period

(e.g. 1 a = 8760 [h]

Downtime due to Unscheduled

Maintenance [h]

Downtime due to

Scheduled

Maintenance [h]

Unplanned

Outages / Failures Rep

air

Mis

sio

n

Costs

Access-

time

Travel-

time

Logistics

Weather

Port Dist.

Logistics

Repair-

time

Mobil.-

time

Component

Type of WTG

Lead-

time

Logistics

Personnel

Logistics

Spare Parts

Type of FC

Weather

waiting-

time

Port Dist.

Weather on site

Logistics

Repair-time


Cha

ract

eris

tics

of M

C

WTG

FC I

FC II

FC III

FC IV

Ma

rko

v M

od

ell

MC

MC

MC

MC

repair - time

spare parts

number of technicians

means of transportationH

s or vw lim

itation

PA

X

access-time

lead time

equipment

Speed [km

h]

mobilisation tim

e

fuel consumption

availability

lead time

Modelling ApproachElements of System and their characteristics

Reliability Parameters

Sys

tem

-Rel

iabi

lity

Ass

embl

ies

Technical System(OWF)

Sub-system

MC

MC

MC

MC

BoP

FC I

FC II

FC III

FC IV

Ass

embl

ies

Sys

tem

-Rel

iabi

lity

Sub-systemFC – Failure Class

MC – Maintenance Class


Modelling ApproachAssembly-specific Markov-Chain

FC I FC II FC III FC IV

Curtailed Load

Operation

Probability of subsequent damages Residence period is determined by

the property of ergodicity Monthly assembly-specific transition

matrices

Initial probability (probability to fail) Probability of Repair

Monthly accessibility indicator Duration of mission

Access- and Mobilisation time

Travelling time

Repair time Further delays


ResultsTechnical Availability

97% - Thornton Bank (2009) and alpha ventus 97% (2011) [3, 4, 5]

Failure data of IWES [1]

Weather data of FINO 1 [2]

Wave height limitations 1.5 m

Wind speed limitation 18 m/s

Service of 180 man-hours per WTG starting in June

General Assumptions

50 x 5 MW WTGs (rotor diameter of 128 m)

40 km distance to an operational port

Area of site is 40 km² with 8D distance between WTGs

25 service technicians

Offshore Wind Farm

200 x 5 MW WTGs (rotor diameter of 128 m)

100 km distance to an operational port

Area of site is 280 km² with 10D distance between WTGs

100 service technicians

Large Scale Far Shore Wind Farm

95%

97%


An

nu

al D

ow

nti

me

[h/a

]ResultsAverage Annual Downtime per WTG

70 h

25 h 60 h


Conclusions

Modelling results reflect the current experiences regarding operational offshore wind farms

Flexible assembly-specific modelling over the calendar year enables a mid- and long-term comparability of planned offshore wind farms

Markov chains around assembly specific operations enable the modelling of the interacting influence factors and their impact on the technical availability and the demand of resources

The model further enables evaluations with regards to

- Logistic strategies & Simultaneous use of vessels

- Impact of limiting resources

- Maintenance strategies The model is expandable in order to provide cost-benefit analysis


Thank you for your attention.Burcu Özdirik

Doctoral Candidate

Operations and MaintenanceOffshore Wind, RWE Innogy

[email protected]

Institute of Environmental Technology and Energy Economics

Technical University of Hamburg- [email protected]


References

[1] S. Faulstich, M. Durstewitz, B. Hahn, K. Knorr and K. Rohring, “Windenergie Report Deutschland 2008,” Institut für Solare

Energieversorgunstechnik (ISET), Kassel, 2008.

[2] FINO 1, [Online available: http://www.fino1.de], 2012, accessed on 05.11.2012

[3] D. Koenemann, “Erwartungen übertroffen – Ein Rückblick auf das erste Betriebsjahr 2011,” BWK – Das Energie Fachmagazin, Bd. 64, pp.28-29, 2012

[4] “alpha ventus – Pressemitteilung 2012,” [Online available: http://www.alphaventus.de, accessed on 25.11.2012

[5] “Repower Systems – Pressemitteilung 2010,” [Online available: http://www.repower.de, accessed on 25.11.2012


ResultsParameter Variation for Far-shore WF Assumptions

Speed of vessels

Distance to port

ReliabilityMaintainability

Serviceability

Distance within WF

Lead-times


Accessibility IndicatorsFINO 1 Weather Data (7 years)


Accessibility Indicators for Hs 1.5mFINO 1 Weather Data (7 years)


ResultsDemand of Vessels for the Far-shore Wind Farm


ResultsDemand of CTVs for the Far-shore Wind Farm

Additional CTVs for scheduled Maintenance


Split of WTG into Assemblies

Rotor-system (blade, bearing, hub)

Pitch-system

Yaw-system

Generator

Gearbox

Sensors

Drive Train incl. Bearings

Electrical Control


ResultsSplit of Downtime for the Far-shore Wind Farm

Far-shore WF Far-shore WF (with helicopter)


ResultsSplit of Downtime for the Far-shore Wind Farm

Far-shore WF (CTV 1.5m) Far-shore WF (CTV 2m)


ResultsSplit of Downtime for the Offshore Wind Farm

Offshore WF Offshore WF (with helicopter)


Transition Probabilities

rwe innogy 6/1/2014page 1 o&m modelling for large scale offshore wind farms by use of markov...

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concepts burcu zdirik

burcu zdirik background

burcu zdirik references

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