Wind Power Systems Applications of Computational Intelligence
Renewable energy sources such as wind power have attracted much attention because they are environmentally friendly, do not produce carbon dioxide and other emitants, and can enhance a nation’s energy security. For example, recently more significant amoun
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Lingfeng Wang, Chanan Singh, and Andrew Kusiak (Eds.)
Wind Power Systems Applications of Computational Intelligence
With 256 Figures and 63 Tables
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Editors Dr. Lingfeng Wang Department of Electrical Engineering and Computer Science University of Toledo Toledo, OH 43606 USA E-mail: [email protected]
Dr. Andrew Kusiak Mechanical and Industrial Engineering Department University of Iowa 3131 Seamans Center Iowa City, IA 52242 USA E-mail: [email protected]
Dr. Chanan Singh Electrical and Computer Engineering Department Texas A&M University College Station, TX 77843-3128 USA E-mail: [email protected]
ISBN 978-3-642-13249-0
e-ISBN 978-3-642-13250-6
Springer Series in Green Energy and Technology
ISSN 1865-3529
Library of Congress Control Number: 2010927161 c
2010 Springer-Verlag Berlin Heidelberg
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Preface
Renewable energy such as wind power has attracted much attention due to its several merits such as environmental friendliness and enhancement of nation’s energy security. In recent years, large capacity of wind power is being integrated with conventional power grids. Therefore, it is necessary to address various challenging issues related to wind power systems, which are significantly different from the traditional generation systems. This book is intended as a resource for engineers, practitioners, and decision-makers interested in studying or using the power of computational intelligence based algorithms in handling various important problems in wind power systems at the levels of power generation, transmission, and distribution. This edited book includes the state-of-the-art studies on applications of computational intelligence, including evolutionary computation, neural networks, fuzzy logic, hybrid algorithms, multi-agent reinforcement learning, and several other approaches, to wind power systems. Chapters of original research on computational intelligence applications are included in various research areas including wind turbine control, wind turbine diagnosis, wind farm design, economic dispatch, conductor sizing, reliability analysis, power loss minimization, frequenc
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