APPLICATION OF ARTIFICIAL INTELLIGENCE IN POWER

TRANSFORMER FAULT DIAGNOSIS

ByYANG QI-PING , LI MENG-QUN, MU XUE-YUN, WANG JUN

Presented byARATHI AJAY

S7 EEENO: 7

CONTENTS

Artificial Intelligence Artificial Neural Network(ANN) Transformer fault and Dissolved Gas Analysis Transformer Fault Diagnosis Artificial Intelligence

(TFDAI) System TFD Expert System TFDANN Illustration Conclusion Reference

WHAT IS ARTIFICIAL INTELLIGENCE?

Study and design of intelligent systems. Intelligent system – one that perceives its environment

and takes actions to maximize its chances of success. Deals with reasoning, knowledge, planning, learning,

communication etc.

ARTIFICIAL NEURAL NETWORK

• AI implemented using ANN• Emulation of biological neural system• Imitates information processing of brain• Can teach oneself and adapt non-linear relations between

input and output.

NEURAL NETWORK

Biological Artificial

BIOLOGICAL NEURON

ARTIFICIAL NEURON

1)Dendrites 2)Electrical

impulse 3)Strength of

synapse 4)Cell body 5)Axon 6)Signal of axon

Input unit Input (p) Weight (W) Summation unit and transfer function Output unit Output (a)

TRANSFORMER FAULTS

Main faults areArcing or high current breakdown – H2, C2H2Low energy sparking or partial discharges –H2, other

lower HCHot spots or localized overheating – CH4, C2H6General overheating – CO, CO2

DISSOLVED GAS ANALYSIS

To detect incipient faults in oil-filled transformers Rate of evolution of HC temperature Methods: -

1. Key Gas Method

H2- Corona, O2 & N2- no fault, CO & CO2- cellulose breakdown

CH4 & C2H6- low temp oil BD, C2H4 – High temp BD, C2H2- Arcing

2. Ratio Method

CH4/H2, C2H2/C2H6, C2H2/C2H4

TRANSFORMER FAULT DIAGNOSIS ARTIFICIAL INTELLIGENCE SYSTEM

TFD Expert System TFD ANN

TFD EXPERT SYSTEM

Knowledge Base Database Inference Engine Interpretation Mechanism Man – Machine Interface

KNOWLEDGE BASE

Six Modules: -

Gas chromatography analysis moduleNormal, Normally aged, Partially discharging,

over heated – H2,CH4,C2H6,C2H4,C12, CO,CO2

Exterior Inspection moduleExterior or Interior Imperfection – noise, oil level, oil

temperature

Oil feature test moduleGood, alert, bad – acidity, resistivity, water content,

surface tension, dielectric loss, breakdown voltage

Insulation preventive test module

Insulation Status - Measure values of DC resistance, insulation resistance, leakage currents of high, medium, low voltage of 3phase winding

Comprehensive analysis module

Gives final judgment on analyzing the above modules

Coordinator moduleCoordinates and controls TFDES. Starts with

chromatography, then exterior inspection followed by oil feature and insulation preventive test modules.

TFD ARTIFICIAL NEURAL NETWORK

Two phases : 1. Learning2. Diagnosis

Five modules: 1.Characteristic Gas Method Module BP12.Three Ratios Method Module BP23.Insulation Oil Feature Test Module BP34.Exterior Inspection Module BP45.Comprehensive Analysis Module BP5

BP1 – 6inputs, 4outputs

I/p: H2, CH4, C2H2, C2H4, C2H6, CO

O/p: Normal, over heating, corona, arcing

BP2 – 3i/p, 9 o/p

I/p: C2H2/C2H4, CH4/H2,

C2H4/C2H6

O/p: 1 normal and 8 Faults

BP3 – 6 i/p, 3 o/p

I/p: acidity, resistivity, water content, surface tension, dielectric loss, breakdown voltage

O/p: good, alert, bad

BP4 – 3 i/p, 2 o/p

I/p: noise, oil level, oil temperature

O/p: exterior imperfection, interior imperfection

BP5 –

From the results of BP1, BP2, BP3, BP4, makes final judgment

TFDAI

Input - transformer’s measured data Preliminary judgment by logical judging model Status - either normal or abnormal If abnormal, TFDES and TFDANN starts parallel Their output to Comprehensive Analysis to give final

judgment

EXAMPLETest Data of a transformer:

CO

1264

Acidity .195

Resistivity 15x1010

Water content 33.5

Surface tension 20x10-3

Voltage rank 110

Breakdown voltage 39.5

Dielectric loss 1.75

From TFDES:

Transformer Interior Abnormal

Nature of fault: High Energy Discharge

Suggestion: stop operation immediately, make interior inspection

From TFDANN:

BP1 output: Y4=1.000, conclusion – electric arc discharge

BP2 output: Y4=0.998, conclusion – high energy discharge

BP3 output: Y1=.9895, conclusion – oil can be still used

BP4 output: Y1=.9385, conclusion – interior abnormal From TFDAI:

Transformer interior abnormal

Nature – high energy discharge

Conclusion - Transformer interior winding not good

Suggestion – stop operation, make interior inspection

On site conclusion – Winding Fault

CONCLUSION ES – to imitate logical thinking of brain ANN – to imitate thinking in images of brain DGA – powerful tool in fault diagnosis

*advantage

*disadvantageo TFDAI – remedy to the disadvantageso Results – highly reliable, less training time, less

memory consumption, 80% success levels

REFERENCES

Mu Xue-Yun, Wang Jun and Yang Qi-Ping, Li Meng-Qun. “Application of AI in Power Transformer Fault Diagnosis”, International Conference on Artificial Intelligence and Computational Intelligence, 2009, 7-8 Nov, p442-445

Martin T Hagan, Howard B Dcmuth and Mark Beale, “Neural Network Design”, chapter-2,3, page-2.1-2.23 and 3.1-3.16

Deepika Bhalla, RajKumar Bansal and Hari Om Gupta, “Appication of Artificial Intelligence Techniques for Dissolced Gas Analysis of Transformers- A Review”, World Academy of Science, Engineering and Technology 62 2010.

Methods and applications of Artificial Intelligence: vol3, pg 421-424, George A Vouros

K V Satyanarayana, C H Charkradhar Reddy, T P Govindan, Manoj Mandlik and T S Ramu, “Application of Artificial Intelligence for the assessment of the Status of Power Transformers”, Indian Institute of Science, Downloaded on March 9, 2010 from IEEE Xplore.

Jessey G Smith, B Venkba Rao, Vivek Diwanji and Shivaram Kamat, “Fault Diagnosis- Isolation of Malfunctions in Power Transformers”, june 10, 2009, Tata Consultancy Sevices. Copyright © 2009 Tata Consultancy Services.

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