Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence

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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.

Author(s): W.H. Tang, Q.H. Wu (auth.)
Series: Power Systems
Edition: 1
Publisher: Springer-Verlag London
Year: 2011

Language: English
Pages: 202
Tags: Power Electronics, Electrical Machines and Networks; Computational Intelligence; Quality Control, Reliability, Safety and Risk; Simulation and Modeling

Front Matter....Pages i-xvii
Introduction....Pages 1-13
Evolutionary Computation....Pages 15-36
Methodologies Dealing with Uncertainty....Pages 37-54
Thermoelectric Analogy Thermal Models of Power Transformers....Pages 55-71
Thermal Model Parameter Identification and Verification Using Genetic Algorithm....Pages 73-94
Transformer Condition Assessment Using Dissolved Gas Analysis....Pages 95-104
Fault Classification for Dissolved Gas Analysis Using Genetic Programming....Pages 105-124
Dealing with Uncertainty for Dissolved Gas Analysis....Pages 125-162
Winding Frequency Response Analysis for Power Transformers....Pages 163-175
Winding Parameter Identification Using an Improved Particle Swarm Optimiser....Pages 177-183
Evidence-Based Winding Condition Assessment....Pages 185-194
Back Matter....Pages 195-199