Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems

Provides power system engineers with basic knowledge of heuristic optimization techniques
Several heuristic tools have evolved in the last decade that facilitate solving optimization problems that were previously extremely challenging or even impossible to solve. Now, based on a successful tutorial given by the editors at IEEE Power Engineering Society conferences in New York and Toronto, Modern Heuristic Optimization Techniques explores how developing solutions with these tools offers two major advantages: shortened development time and more robust systems.
Composed of two parts, the book begins with an overview of modern heuristictechniques, including the fundamentals of evolutionary computation, genetic algorithms,evolutionary programming and strategies, particle swarm optimization, ant colony search algorithm, differential evolution, simulated annealing, tabu search, and hybrid systems of evolutionary computation. Next, it covers specific applications of heuristic approaches to power system problems, such as security assessment, optimal power flow, power system scheduling and operational planning, power generation expansion planning, reactive power planning, transmission and distribution planning, network reconfiguration, power plant control, power system control, and hybrid systems of heuristic methods.
Complemented with scores of drawings, charts, graphs, and tables that help bring the material to life, Modern Heuristic Optimization Techniques is the only book of its kind to provide a comprehensive treatment of the subject in a manner that is accessible to students and practitioners alike.

Spis treści:
Preface.
Contributors.
Part 1: Theory of Modern Heuristic Optimization.
1. Introduction to Evolutionary Computation (David B. Fogel)
2. Fundamentals of Genetic Algorithms (Alexandre P. Alves da Silva and Djalma M. Falcao)
3. Fundamentals of Evolution Strategies and Evolutionary Programming (Vladimiro M iranda)
4. Fundamentals of Particle Swarm Optimization Techniques (Yoshikazu Fukuyama)
5. Fundamentals of Ant Colony Search Algorithms (Yong–Hua Song, Haiyan Lu, Kwang Y. Lee, and I.K. Yu)
6. Fundamentals of Tabu Search (Alcir J. Monticelli, Ruben Romero, and Eduardo Nobuhiro Asada)
7. Fundamentals of Simulated Annealing (Alcir J. Monticelli, Ruben Romero, and Eduardo Nobuhiro Asada)
8. Fuzzy Systems (Germano Lambert–Torres)
9. Differential Evolution, an Alternative Approach to Evolutionary Algorithm (Kit Po Wong and Zhao Yang Dong)
10. Pareto Multiobjective Optimization (Patrick N. Ngatchou, Anahita Zarei, Warren L. J. Fox, and Mohamed A. El–Sharkawi)
11. Trust–Tech Paradigm for Computing High–Quality Optimal Solutions: Method and Theory (Hsiao–Dong Chaing and Jaewook Lee)
Part 2: Selected Applications of Modern Heuristic Optimization in Power Systems.
12. Overview of Applications in Power Systems (Alexandre P. Alves da Silva, Djalma M. Falcao, and Kwang Y. Lee)
13. Application of Evolutionary Technique to Power System Vulnerability Assessment (Mingoo Kim, Mohamed A. el–Sharkawi, Robert J. Marks, and Ioannis N. Kassabalidis)
14. Applications to System Planning (Eduardo Nobuhiro Asada, Youngjae Jeon, Kwang Y. Lee, Vladimiro Miranda, Alcir J. Monticelli, Koichi Nara, Jong–Bae Park, Ruben Romero, and Yong–Hua Song)
15. Applications to Power System Scheduling (Koay Chin Aik, Loi Lei Lai, Kwang Y. Lee, Haiyan Lu, Jong–Bae Park, Young–Hua Song, Dipti Srinivasan, John G. Vlachogiannis, and I.K. Yu)
16. Power System Controls (Yoshikazu Fukuyama, Hamid Ghezelayagh, Kwang Y. Lee, Chen–Ching Liu, Yong–Hua Song, and Ying Xiao)
17. Genetic Algorithms for Solving Optimal Power Flow Problems (Loi Lei Lai and Nidul Sinha)
18. An Interactive Compromise Programming–Based Multiobjective Approach to FACTS Control (Ying Xiao, Young–Hua Song, and Chen–Ching Liu)
19. Hybrid Systems (Vladimiro Miranda)
Index.

Nota biograficzna:

Kwang Y. Lee, PhD, is a Professor and Chair of Electrical and Computer Engineering at Baylor University (Texas). He is a Fellow of the IEEE. He was an associate editor of IEEE Transactions on Neural Networks and is an Editor of IEEE Transactions on Energy Conversion. He was also a member of the board of directors of the International Conference on Intelligent System Applications to Power Systems (ISAP).
Mohamed A. El–Sharkawi, PhD, is a Professor of Electrical Engineering at the University of Washington. He is a Fellow of the IEEE, founder of the International Forum on the Application of Neural Networks to Power Systems (ANNPS), and cofounder of the International Conference on Intelligent System Applications to Power Systems (ISAP).

Okładka tylna:

Provides power system engineers with basic knowledge of heuristic optimization techniques
Several heuristic tools have evolved in the last decade that facilitate solving optimization problems that were previously extremely challenging or even impossible to solve. Now, based on a successful tutorial given by the editors at IEEE Power Engineering Society conferences in New York and Toronto, Modern Heuristic Optimization Techniques explores how developing solutions with these tools offers two major advantages: shortened development time and more robust systems.
Composed of two parts, the book begins with an overview of modern heuristictechniques, including the fundamentals of evolutionary computation, genetic algorithms,evolutionary programming and strategies, particle swarm optimization, ant colony search algorithm, differential evolution, simulated annealing, tabu search, and hybrid systems of evolutionary computation. Next, it covers specific applications of heuristic approaches to power system problems, such as security assessment, optimal power flow, power system scheduling and operational planning, power generation expansion planning, reactive power planning, transmission and distribution planning, network reconfiguration, power plant control, power system control, and hybrid systems of heuristic methods.
Complemented with scores of drawings, charts, graphs, and tables that help bring the material to life, Modern Heuristic Optimization Techniques is the only book of its kind to provide a comprehensive treatment of the subject in a manner that is accessible to students and practitioners alike.