Intelligent System Applications In Power Engineering- Evolutionary Programming & Neural Networks

Cutting-edge research indicates that evolutionary programming is set to emerge as the dominant optimisation technique in the fast-changing power industry. Combining theory and practice, Intelligent System Applications in Power Engineering capitalises on the potential of neural networks and evolutionary computation to resolve real-world power engineering problems such as load forecasting, power system operation and planning optimisation. Unlike existing optimisation methods, these novel computational intelligence techniques provide power utilities with innovative solutions for improved performance. Features include:
* Introduction to evolutionary programming and neural networks serving as a foundation for later discussion of the benefits of hybrid systems
* Practical application of evolutionary programming to reactive power planning and dispatch for speedy, cost-effective increases in transmission capacity plus generator parameter estimation
* Examination of economic dispatch, power flow control in FACTS and co-generation scheduling and fault diagnosis for HVDC systems and transformers
* Consideration of power frequency and harmonic evaluation to maximise supply quality
* Employment of distance protection, faulty section estimation and calculation of fault clearing time for transient stability assessment
Graduate students in electric power engineering will value Lai s broad coverage of the applications of evolutionary programming and neural networks in the field. This unique reference will be a boon to engineers, computer application specialists, consultants and utility managers wishing to understand the benefits intelligent systems can bring to the power industry.

Loi Lei Lai graduated from?Aston University in Birmingham with a BSc and a PhD. He was awarded a DSc by City University London. He is also an honrorary graduate of City University. In 1984, he was appointed Senior Lecturer at Staffordshire Polytechnic. From 1986 to 1987, he was a Royal Academy of Engineering Industrial Fellow to both GEC Alsthom Turbine Generators Ltd and the Engineering research Centre. He is currently Head of Energy Systems Group and Chair in Electrical Engineering at City University London. In the last decade, Professor Lai has authored/co-authored 200 technical publications. He has also written a book entitled Intelligent System Applications in Power Engineering - Evolutionary Programming and Neural Networks and, in 2001, edited the book Power System Restructuring and Deregulation - Trading, Performance and Information Technology, both published by John Wiley?& Sons, Ltd. He was award the IEEE Third Millennium Medal and won the IEEE Power Engineering Society, United Kingdom and Republic of Ireland (UKRI), chapter, Outstanding Engineer Award in 2003. In 1995, he received a high-quality paper prize from the International Association of Desalination, USA and in 2006 he was awarded a Prize paper by the IEEE Power Generation Committee. He is a Fellow of the IEEE and the IET (Institution of Engineering and Technology).
Among his professional activities, he is a Founder and was the Conference Chairman of the international Conference on Power Utility Deregulation, Restructuring man of the International Conference on Power Utility Deregulation, Restructuring and Power Technologies (DRPT) 2000, co-sponsored by the IEEE (now IET) and Power Technologies (DRPT) 2000, co-sponsored by the IEE (now IET) and IEEE. He reviews grant proposals regularly for the EPSRC, Australian Research Council and Hong Kong research Grant Council. In 2001, he was invited by the Hong Kong Institution of Engineers to be Chairman of an Accreditation Visit Team to accredit the BEng (Hons) degree in Electrical Engineering. Since 2005, Professor Lai has been invited as a judge for the Power/Energy Category in the IET Innovation in Engineering Awards. He was also Student Recruitment Office of the IEEE UKRI Section Executive Committee. He is a member of the Intelligent Systems Subcommittee in Power System Analysis, Computing and Economic Committee, IEEE Power Engineering Society; a Member of the Executive Team of the Power Trading and Control Technical and Professional Network, IET; an Editor of the IEE Proceedings - Generation, Distribution and Generation (now IET Generation, Distribution and Generation); an Editorial Board Member of the International Journal of Electrical Power & Energy Systems published by Elsevier Science Ltd, UK; International Advisor, Hong Kong Institution of Engineers (HKIE) Transactions and an Editorial Board Member of the European Transactions on Electrical Power published by John Wiley & Sons, Ltd. He was a research Professor at Tokyo Metropolitan University, is also Visiting professor at Southeast University Nanjing and Guest Professor at Fudan University, Shanghai. He has also been invited to deliver keynote addresses and plenary speeches to several major international conferences sponsored by the IET and IEEE.

Object-Oriented Analysis, Design and Development of Artificial Neural Networks.

Evolutionary Computation.

Hybrid Evolutionary Algorithms and Artificial Neural Networks

An Evolutionary Programming Approach to Reactive Power Planning

Optimal Reactive Power Dispatch Using Evolutionary Programming.

Application of Evolutionary Programming to Transmission Network Planning.

Application of Evolutionary Programming to Generator Parameter Estimation.

Evolutionary Programming for Economic Dispatch of Units with Non-Smooth Input-Output Characteristic Functions.

Power Flow Control in Facts Using Evolutionary Programming.

Multi-Time-Interval Scheduling for Daily Operation of a Two-Co-Generation System with Evolutionary Programming.

Application of Evolutionary Programming to Fault Section Estimation.

Neural Networks for Fault Diagnosis in HVDC Systems.

An Ann Approach to the Diagnosis of Transformer Faults

Real-Time Frequency and Harmonic Evaluation Using Artificial Neural Networks.

Artificial Neural Network Applications in Digital Distance Relay.

Application of Artificial Neural Networks to Transient Stability Assessment.

Application of Neural Networks and Evolutionary Programming to Short-Term Load Forecasting.

Select Bibliography

Index.