Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence

210 Pages·2011·6.223 MB·English

by  Tang, W H;Wu, Q H

Power Systems
For furthervolumes:
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W. H. Tang (cid:2) Q. H. Wu
Condition Monitoring and
Assessment of Power
Transformers Using
Computational Intelligence
123
Dr. W.H.Tang Prof.Dr. Q.H. Wu
Department of ElectricalEngineering Departmentof Electrical Engineering
and Electronics and Electronics
The Universityof Liverpool The Universityof Liverpool
Brownlow Hill Brownlow Hill
Liverpool L693GJ, UK Liverpool L693GJ, UK
e-mail: [email protected] e-mail: [email protected]
ISSN 1612-1287 e-ISSN1860-4676
ISBN 978-0-85729-051-9 e-ISBN978-0-85729-052-6
DOI 10.1007/978-0-85729-052-6
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To our Families
Preface
Power transformers are among the most expensive and critical units in a power
system.Thenormallifeexpectanceofapowertransformerisaround40years,and
in many power systems the percentage of transformers operated more than
30 yearsisincreasingduetotheinvestmentboomafterthe1970s.Asaresult,the
failure rate of transformers is expected to rise sharply in the coming years.
Transformer failures are sometimes catastrophic and almost always include irre-
versible internal damage. Therefore, all key power transformers equipped in a
power system should be monitored closely and continuously in order to ensure
their maximum uptime. Generally, there are four main aspects of transformer
condition monitoring and assessment, including thermal dynamics, dissolved gas,
partial discharge and winding deformation, which should be monitored closely in
order to determine power transformer conditions.
In recent years, rapid changes and developments have been witnessed in the
field of transformer condition monitoring and assessment. Many research institu-
tions and utility companies have their own condition monitoring and assessment
guidelines for large power transformers. Most of such efforts are dedicated to
developing accurate transformer models and reliable transformer fault diagnosis
systems. These approaches are usually based upon empirical models, which are
sometimes inaccurate and incomplete concerning abnormal operation scenarios.
Themajordrawbacksarerootedintheinaccuracyofempiricalthermalmodels,the
lack of knowledge and evidence in dissolved gas analysis and intricate issues in
windingdeformationdiagnosis.Nowadays,owingtotheadvanceincomputational
hardware facilities and software data analysis techniques, the in-depth under-
standing of various phenomena affecting transformer operations has become
feasible. With the use of advanced computational intelligence techniques, system
operators are able to interpret correctly various fault phenomena and successfully
detect incipient faults.
This book is dedicated to advanced model-based approaches to accurate
transformer modelling and intelligent data mining techniques for reliable trans-
former fault diagnosis. It introduces three important up-to-date aspects of com-
putational intelligence techniques to handle practical problems of transformer
vii
viii Preface
condition monitoring and assessment. These techniques include the evolutionary
algorithms, the logical approaches and the cybernetic methods, which are
employed for model parameter identification, fault feature extraction and classi-
fication and dealing with uncertainties for undertaking condition assessment of
power transformers, respectively.
We wrote this book in belief that applying computational intelligence tech-
niques to transformer condition monitoring and assessment would open the pos-
sibility of obtaining the maximum practicable operating efficiency and optimum
life of power transformers, minimising risks of premature failures and generating
optimal system maintenance strategies. This book is self-contained with adequate
backgroundintroductionsunderlyinganalyticalsolutionsofeachtopicandlinksto
the publicly available toolboxes for the implementation of the introduced meth-
odologies. It deals with practical transformer operation problems by analysing
real-world measurements with a broad range of computational intelligence tech-
niques.Thisbookhaspresentedmanyexamplesofusingreal-worldmeasurements
and realistic operating scenarios of power transformers, which fully illustrate the
use of computational intelligence techniques to deal with a variety of transformer
modellingandfaultdiagnosisproblems.Wehopethatthisbookwillbeusefulfor
those postgraduates, academics researchers and engineers working in the area of
advanced condition monitoring and assessment of power transformers.
WewouldliketothankDr.AlmasShintemirovforhiscontributiontochaps.7
and11,madeduringtheperiodofhisPh.D.studyundertakenatTheUniversityof
Liverpool. We also wish to thank Dr. Kevin Spurgeon and Dr. Shan He for their
contributions, made during the period of their Ph.D. studies undertaken at The
UniversityofLiverpool,topartoftheachievementspresentedinthisbook.Special
thanks are given to Mr. Zac Richardson and Mr. John Fitch of National Grid for
supporting this work and providing useful discussions.
Special thanks also go to Anthony Doyle (the Senior Editor), Claire Prothe-
rough and Sorina Moosdorf for their professional and efficient editorial work on
this book. Our thanks are also extended to all colleagues in the Intelligence
EngineeringandAutomationResearchGroup,TheUniversityofLiverpool,forall
assistance provided, and who have not been specially mentioned above.
The University of Liverpool, June 2010 Dr. W.H. Tang
Prof. Dr. Q.H. Wu
Contents
1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Main Aspects of Transformer Condition Monitoring
and Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Thermal Modelling. . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.2 Dissolved Gas Analysis. . . . . . . . . . . . . . . . . . . . . . 4
1.2.3 Frequency Response Analysis . . . . . . . . . . . . . . . . . 5
1.2.4 Partial Discharge Analysis. . . . . . . . . . . . . . . . . . . . 6
1.3 Drawbacks of Conventional Techniques . . . . . . . . . . . . . . . . 6
1.3.1 Inaccuracy of Empirical Thermal Models . . . . . . . . . 6
1.3.2 Uncertainty in Dissolved Gas Analysis . . . . . . . . . . . 7
1.3.3 IntricateIssuesinWinding Deformation Diagnosis. . . 7
1.4 Modelling Transformer and Processing Uncertainty
Using Computational Intelligence. . . . . . . . . . . . . . . . . . . . . 8
1.5 Contents of this Book. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Evolutionary Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1 TheEvolutionaryAlgorithmsofComputationalIntelligence. . . 15
2.1.1 Objectives of Optimisation . . . . . . . . . . . . . . . . . . . 15
2.1.2 Overview of Evolutionary Computation . . . . . . . . . . 17
2.2 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.1 Principles of Genetic Algorithms . . . . . . . . . . . . . . . 19
2.2.2 Main Procedures of a Simple Genetic Algorithm . . . . 20
2.2.3 Implementation of a Simple Genetic Algorithm. . . . . 23
2.3 Genetic Programming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3.1 Background of Genetic Programming. . . . . . . . . . . . 24
2.3.2 ImplementationProcessesofGeneticProgramming. . . 25
2.4 Particle Swarm Optimisation . . . . . . . . . . . . . . . . . . . . . . . . 29
2.4.1 Standard Particle Swarm Optimisation . . . . . . . . . . . 30
ix
x Contents
2.4.2 Particle Swarm Optimisation with Passive
Congregation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.5 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3 Methodologies Dealing With Uncertainty. . . . . . . . . . . . . . . . . . . 37
3.1 The Logical Approach of Computational Intelligence. . . . . . . 37
3.2 Evidential Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2.1 The Original Evidential Reasoning Algorithm . . . . . . 38
3.2.2 The Revised Evidential Reasoning Algorithm . . . . . . 45
3.3 Fuzzy Logic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.3.1 Foundation of Fuzzy Logic . . . . . . . . . . . . . . . . . . . 48
3.3.2 An Example of a Fuzzy Logic System . . . . . . . . . . . 48
3.4 Bayesian Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.4.1 The Bayes’ Theorem . . . . . . . . . . . . . . . . . . . . . . . 50
3.4.2 Bayesian Networks. . . . . . . . . . . . . . . . . . . . . . . . . 51
3.4.3 Parameter Learning to Form a Bayesian Network. . . . 52
3.5 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Thermoelectric Analogy Thermal Models
of Power Transformers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2 Conventional Thermal Models in IEC and
IEEE Regulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.1 Steady-State Temperature Models. . . . . . . . . . . . . . . 56
4.2.2 Transient-State Temperature Models. . . . . . . . . . . . . 57
4.2.3 Hot-Spot Temperature Rise in Steady State. . . . . . . . 57
4.2.4 Hot-Spot Temperature Rise in Transient State. . . . . . 60
4.3 The Thermoelectric Analogy Theory. . . . . . . . . . . . . . . . . . . 60
4.4 A Comprehensive Thermoelectric Analogy Thermal Model. . . 61
4.4.1 Heat Transfer Schematics of Transformers . . . . . . . . 61
4.4.2 Derivation of a Comprehensive
Heat Equivalent Circuit. . . . . . . . . . . . . . . . . . . . . . 63
4.5 Parameter Estimation of a Thermoelectric Analogy Model . . . 66
4.5.1 Heat Generation Process . . . . . . . . . . . . . . . . . . . . . 66
4.5.2 Heat Transfer Parameter . . . . . . . . . . . . . . . . . . . . . 66
4.5.3 Operation Scheme of Winding
Temperature Indicator. . . . . . . . . . . . . . . . . . . . . . . 67
4.5.4 Time Constant Variation in a
Heat Transfer Process. . . . . . . . . . . . . . . . . . . . . . . 67
4.6 Identification of Thermal Model Parameters . . . . . . . . . . . . . 68
4.7 A Simplified Thermoelectric Analogy Thermal Model . . . . . . 68
4.7.1 Derivation of a Simplified Heat Equivalent Circuit. . . 68
4.7.2 Hot-Spot Temperature Calculation . . . . . . . . . . . . . . 70
Contents xi
4.8 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5 Thermal Model Parameter Identification and
Verification Using Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . 73
5.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2 Unit Conversion for Heat Equivalent Circuit Parameters. . . . . 74
5.3 Fitness Function for Genetic Algorithm Optimisation. . . . . . . 75
5.4 Parameter Identification and Verification for the
Comprehensive Thermal Model . . . . . . . . . . . . . . . . . . . . . . 76
5.4.1 Estimation of Heat Transfer Parameters . . . . . . . . . . 76
5.4.2 Parameter Identification Using Genetic Algorithm . . . 77
5.4.3 Verification of Identified Thermal Parameters
Against Factory Heat Run Tests. . . . . . . . . . . . . . . . 79
5.4.4 Comparison between Modelling Results of
Artificial Neural Network and Genetic Algorithm . . . 82
5.5 Parameter Identification and Verification for the
Simplified Thermal Model. . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.5.1 Identification of Parameters Using
Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.5.2 Verification of Derived Parameters with
Rapidly Changing Loads. . . . . . . . . . . . . . . . . . . . . 87
5.5.3 Simulations of Step Responses Compared with
Factory Heat Run. . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.5.4 Hot-Spot Temperature Calculation . . . . . . . . . . . . . . 92
5.5.5 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.6 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
6 Transformer Condition Assessment Using
Dissolved Gas Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.2 Fundamental of Dissolved Gas Analysis . . . . . . . . . . . . . . . . 96
6.2.1 Gas Evolution in a Transformer. . . . . . . . . . . . . . . . 96
6.2.2 Key Gas Method . . . . . . . . . . . . . . . . . . . . . . . . . . 98
6.2.3 Determination of Combustible Gassing Rate . . . . . . . 99
6.2.4 Gas Ratio Methods. . . . . . . . . . . . . . . . . . . . . . . . . 99
6.2.5 Fault Detectability Using Dissolved Gas Analysis . . . 100
6.3 Combined Criteria for Dissolved Gas Analysis. . . . . . . . . . . . 101
6.4 Intelligent Diagnostic Methods for Dissolve Gas Analysis. . . . 102
6.5 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104