Computational Intelligence Applications to Power Systems (Intelligent Systems, Control and Automation: Science and Engineering, 15)

Yong-Hua Song; Johns, Allan; Aggarwal, Raj

ISBN 10: 0792340752 / ISBN 13: 9780792340751

Editore: Springer, 1996

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Dati bibliografici

• Titolo: Computational Intelligence Applications to Power Systems (Intelligent Systems, Control and Automation: Science and Engineering, 15)
• Editore: Springer
• Data di pubblicazione: 1996
• Lingua: Inglese
• ISBN 10: 0792340752
• ISBN 13: 9780792340751
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

This book represents a thoroughly comprehensive treatment of computational intelligence from an electrical power system engineer's perspective. Thorough, well-organised and up-to-date, it examines in some detail all the important aspects of this very exciting and rapidly emerging technology, including: expert systems, fuzzy logic, artificial neural networks, genetic algorithms and hybrid systems.
Written in a concise and flowing manner, by experts in the area of electrical power systems who have had many years of experience in the application of computational intelligence for solving many complex and onerous power system problems, this book is ideal for professional engineers and postgraduate students entering this exciting field. This book would also provide a good foundation for senior undergraduate students entering into their final year of study.

Contenuti

1. Expert Systems: An Introduction. 2. Intelligent Alarm Processor. 3. Intelligent Fault Diagnosis in Power Systems. 4. Decision Support for Reactive Power and Voltage Control. 5. Fuzzy Logic: An Introduction. 6. Fuzzy Stability Control for Power Systems. 7. Fuzzy Hydroelectric Generation Scheduling. 8. Neural Networks: An Introduction. 9. Neural Networks Based Electrical Load Forecasting. 10. Kohonen Networks for Power System Static Security Assessment. 11. Adaptive Autoreclosure Techniques. 12. Genetic Algorithms: An Introduction. 13. Genetic Algorithm in Power System Optimization. 14. Integration of Fuzzy Logic, Neural Networks and Genetic Algorithms.