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Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence 2011 ed. [Hardback]

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  • Formāts: Hardback, 202 pages, height x width: 235x155 mm, weight: 1080 g, XVIII, 202 p., 1 Hardback
  • Sērija : Power Systems
  • Izdošanas datums: 30-Jan-2011
  • Izdevniecība: Springer London Ltd
  • ISBN-10: 0857290517
  • ISBN-13: 9780857290519
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Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence 2011 ed.Palielināt
  • Formāts: Hardback, 202 pages, height x width: 235x155 mm, weight: 1080 g, XVIII, 202 p., 1 Hardback
  • Sērija : Power Systems
  • Izdošanas datums: 30-Jan-2011
  • Izdevniecība: Springer London Ltd
  • ISBN-10: 0857290517
  • ISBN-13: 9780857290519
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780857290519.html
Keywords:
In recent years, rapid changes and improvements have been witnessed in the field of transformer condition monitoring and assessment, especially with the advances in computational intelligence techniques. Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence applies a broad range of computational intelligence techniques to deal with practical transformer operation problems. The approaches introduced are presented in a concise and flowing manner, tackling complex transformer modelling problems and uncertainties occurring in transformer fault diagnosis.Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence covers both the fundamental theories and the most up-to-date research in this rapidly changing field. Many examples have been included that use real-world measurements and realistic operating scenarios of power transformers to fully illustrate the use of computational intelligence techniques for a variety of transformer modelling and fault diagnosis problems.Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence is a useful book for professional engineers and postgraduate students. It also provides a firm foundation for advanced undergraduate students in power engineering.

This volume provides a thorough introduction to transformer condition monitoring for the assessment of power transformers. The fundamental theories are discussed, in addition to the most up-to-date research in this rapidly changing field.
1 Introduction
1(14)
1.1 Background
1(2)
1.2 Main Aspects of Transformer Condition Monitoring and Assessment
3(3)
1.2.1 Thermal Modelling
3(1)
1.2.2 Dissolved Gas Analysis
4(1)
1.2.3 Frequency Response Analysis
5(1)
1.2.4 Partial Discharge Analysis
6(1)
1.3 Drawbacks of Conventional Techniques
6(2)
1.3.1 Inaccuracy of Empirical Thermal Models
6(1)
1.3.2 Uncertainty in Dissolved Gas Analysis
7(1)
1.3.3 Intricate Issues in Winding Deformation Diagnosis
7(1)
1.4 Modelling Transformer and Processing Uncertainty Using Computational Intelligence
8(1)
1.5 Contents of this Book
9(2)
1.6 Summary
11(4)
References
12(3)
2 Evolutionary Computation
15(22)
2.1 The Evolutionary Algorithms of Computational Intelligence
15(3)
2.1.1 Objectives of Optimisation
15(2)
2.1.2 Overview of Evolutionary Computation
17(1)
2.2 Genetic Algorithm
18(6)
2.2.1 Principles of Genetic Algorithms
19(1)
2.2.2 Main Procedures of a Simple Genetic Algorithm
20(3)
2.2.3 Implementation of a Simple Genetic Algorithm
23(1)
2.3 Genetic Programming
24(5)
2.3.1 Background of Genetic Programming
24(1)
2.3.2 Implementation Processes of Genetic Programming
25(4)
2.4 Particle Swarm Optimisation
29(5)
2.4.1 Standard Particle Swarm Optimisation
30(1)
2.4.2 Particle Swarm Optimisation with Passive Congregation
31(3)
2.5 Summary
34(3)
References
34(3)
3 Methodologies Dealing With Uncertainty
37(18)
3.1 The Logical Approach of Computational Intelligence
37(1)
3.2 Evidential Reasoning
38(10)
3.2.1 The Original Evidential Reasoning Algorithm
38(7)
3.2.2 The Revised Evidential Reasoning Algorithm
45(3)
3.3 Fuzzy Logic
48(2)
3.3.1 Foundation of Fuzzy Logic
48(1)
3.3.2 An Example of a Fuzzy Logic System
48(2)
3.4 Bayesian Networks
50(3)
3.4.1 The Bayes' Theorem
50(1)
3.4.2 Bayesian Networks
51(1)
3.4.3 Parameter Learning to Form a Bayesian Network
52(1)
3.5 Summary
53(2)
References
53(2)
4 Thermoelectric Analogy Thermal Models of Power Transformers
55(18)
4.1 Introduction
55(1)
4.2 Conventional Thermal Models in IEC and IEEE Regulations
56(4)
4.2.1 Steady-State Temperature Models
56(1)
4.2.2 Transient-State Temperature Models
57(1)
4.2.3 Hot-Spot Temperature Rise in Steady State
57(3)
4.2.4 Hot-Spot Temperature Rise in Transient State
60(1)
4.3 The Thermoelectric Analogy Theory
60(1)
4.4 A Comprehensive Thermoelectric Analogy Thermal Model
61(5)
4.4.1 Heat Transfer Schematics of Transformers
61(2)
4.4.2 Derivation of a Comprehensive Heat Equivalent Circuit
63(3)
4.5 Parameter Estimation of a Thermoelectric Analogy Model
66(2)
4.5.1 Heat Generation Process
66(1)
4.5.2 Heat Transfer Parameter
66(1)
4.5.3 Operation Scheme of Winding Temperature Indicator
67(1)
4.5.4 Time Constant Variation in a Heat Transfer Process
67(1)
4.6 Identification of Thermal Model Parameters
68(1)
4.7 A Simplified Thermoelectric Analogy Thermal Model
68(2)
4.7.1 Derivation of a Simplified Heat Equivalent Circuit
68(2)
4.7.2 Hot-Spot Temperature Calculation
70(1)
4.8 Summary
70(3)
References
71(2)
5 Thermal Model Parameter Identification and Verification Using Genetic Algorithm
73(22)
5.1 Introduction
73(1)
5.2 Unit Conversion for Heat Equivalent Circuit Parameters
74(1)
5.3 Fitness Function for Genetic Algorithm Optimisation
75(1)
5.4 Parameter Identification and Verification for the Comprehensive Thermal Model
76(9)
5.4.1 Estimation of Heat Transfer Parameters
76(1)
5.4.2 Parameter Identification Using Genetic Algorithm
77(2)
5.4.3 Verification of Identified Thermal Parameters Against Factory Heat Run Tests
79(3)
5.4.4 Comparison between Modelling Results of Artificial Neural Network and Genetic Algorithm
82(3)
5.5 Parameter Identification and Verification for the Simplified Thermal Model
85(8)
5.5.1 Identification of Parameters Using Genetic Algorithm
85(2)
5.5.2 Verification of Derived Parameters with Rapidly Changing Loads
87(3)
5.5.3 Simulations of Step Responses Compared with Factory Heat Run
90(2)
5.5.4 Hot-Spot Temperature Calculation
92(1)
5.5.5 Error Analysis
93(1)
5.6 Summary
93(2)
References
94(1)
6 Transformer Condition Assessment Using Dissolved Gas Analysis
95(10)
6.1 Introduction
95(1)
6.2 Fundamental of Dissolved Gas Analysis
96(5)
6.2.1 Gas Evolution in a Transformer
96(2)
6.2.2 Key Gas Method
98(1)
6.2.3 Determination of Combustible Gassing Rate
99(1)
6.2.4 Gas Ratio Methods
99(1)
6.2.5 Fault Detectability Using Dissolved Gas Analysis
100(1)
6.3 Combined Criteria for Dissolved Gas Analysis
101(1)
6.4 Intelligent Diagnostic Methods for Dissolve Gas Analysis
102(1)
6.5 Summary
103(2)
References
104(1)
7 Fault Classification for Dissolved Gas Analysis Using Genetic Programming
105(20)
7.1 Introduction
105(2)
7.2 Bootstrap
107(1)
7.3 The Cybernetic Techniques of Computational Intelligence
108(2)
7.3.1 Artificial Neural Network
108(1)
7.3.2 Support Vector Machine
109(1)
7.3.3 K-Nearest Neighbour
109(1)
7.4 Results and Discussions
110(12)
7.4.1 Process DGA Data Using Bootstrap
110(2)
7.4.2 Feature Extraction with Genetic Programming
112(4)
7.4.3 Fault Classification Results and Comparisons
116(6)
7.5 Summary
122(3)
References
123(2)
8 Dealing with Uncertainty for Dissolved Gas Analysis
125(38)
8.1 Introduction
125(1)
8.2 Dissolved Gas Analysis Using Evidential Reasoning
126(12)
8.2.1 A Decision Tree Model under an Evidential Reasoning Framework
127(2)
8.2.2 An Evaluation Analysis Model based upon Evidential Reasoning
129(2)
8.2.3 Determination of Weights of Attributes and Factors
131(2)
8.2.4 Evaluation Examples under an Evidential Reasoning Framework
133(5)
8.3 A Hybrid Diagnostic Approach Combining Fuzzy Logic and Evidential Reasoning
138(14)
8.3.1 Solution to Crispy Decision Boundaries
139(2)
8.3.2 Implementation of the Hybrid Diagnostic Approach
141(9)
8.3.3 Tests and Results
150(2)
8.4 Probabilistic Inference Using Bayesian Networks
152(9)
8.4.1 Knowledge Transformation into a Bayesian Network
153(3)
8.4.2 Results and Discussions
156(5)
8.5 Summary
161(2)
References
162(1)
9 Winding Frequency Response Analysis for Power Transformers
163(14)
9.1 Introduction
163(2)
9.2 Transformer Transfer Function
165(1)
9.3 Frequency Response Analysis Methods
166(2)
9.3.1 Low Voltage Impulse
166(1)
9.3.2 Sweep Frequency Response Analysis
167(1)
9.4 Winding Models Used for Frequency Response Analysis
168(1)
9.5 Transformer Winding Deformation Diagnosis
168(6)
9.5.1 Comparison Techniques
170(1)
9.5.2 Interpretation of Frequency Response Measurements
170(4)
9.6 Summary
174(3)
References
174(3)
10 Winding Parameter Identification Using an Improved Particle Swarm Optimiser
177(8)
10.1 Introduction
177(1)
10.2 A Ladder Network Model for Frequency Response Analysis
178(1)
10.3 Model-Based Approach to Parameter Identification and its Verification
179(1)
10.3.1 Derivation of Winding Frequency Responses
179(1)
10.3.2 Fitness Function Used by PSOPC
180(1)
10.4 Simulations and Discussions
180(3)
10.4.1 Test Simulations of Frequency Response Analysis
181(1)
10.4.2 Winding Parameter Identification
181(1)
10.4.3 Results and Discussions
182(1)
10.5 Summary
183(2)
References
183(2)
11 Evidence-Based Winding Condition Assessment
185(10)
11.1 Knowledge Transformation with Revised Evidential Reasoning Algorithm
185(1)
11.2 A Basic Evaluation Analysis Model
186(1)
11.3 A General Evaluation Analysis Model
187(1)
11.4 Results and Discussions
188(5)
11.4.1 An Example Using the Basic Evaluation Analysis Model
188(4)
11.4.2 Aggregation of Subjective Judgements Using the General Evaluation Analysis Model
192(1)
11.5 Summary
193(2)
References
194(1)
Appendix: A Testing to BS171 for Oil-Immersed Power Transformers 195(2)
Index 197
Dr. W.H. Tang received his BEng and MSc(Eng) degrees in Electrical Engineering from Huazhong University of Science and Technology, China, in 1996 and 2000, respectively. He obtained a PhD from The University of Liverpool, Liverpool, UK, in 2004. From 2004 to 2006 he worked as a postdoctoral research associate and a university teacher in The University of Liverpool. Since 2006, he has held a lectureship in power engineering in the Department of Electrical Engineering and Electronics in The University of Liverpool. He has published 21 refereed journal papers and presented 24 international conference papers since 2000. His research interests include transformer modelling, substation condition monitoring, computational intelligence and multiple criteria decision analysis. He has also worked on multi-agent systems, renewable energy and power systems. His research has been funded by the Engineering Physical Science Research Council, UK, and industrial companies.

Professor Q.H. Wu is the Chair of Electrical Engineering at The University of Liverpool, UK. He obtained an MSc(Eng) in Electrical Engineering from Huazhong University of Science and Technology (HUST), China, in 1981 and a PhD in Electrical Engineering from The Queen's University of Belfast (QUB) in 1987. Before joining The University of Liverpool in 1995, Professor Wu worked as senior research fellow, lecturer and senior lecturer at QUB and Loughborough University, UK, respectively. He has published 3 monographs, 150 journal papers, 20 book chapters and 180 refereed conference papers. In 1994 he was awarded the Donald Julius Groen Prize for the best paper published in the Journal of Systems and Control Engineering, Institution of Mechanical Engineers. He is a Chartered Engineer, Fellow of IET and Senior Member of IEEE. Professor Wus research interests include systems modelling and control, mathematical morphology, computationalintelligence, multi-agent systems and their applications for power system operation and control.