Titlebook: Computational Intelligence in Power Engineering; Bijaya Ketan Panigrahi,Ajith Abraham,Swagatam Das Book 2010 Springer-Verlag Berlin Heidel

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Particle Swarm Optimization and Its Applications in Power Systems,ately, are of non-convex and sometimes discrete nature has encouraged many researchers to develop new optimization techniques to overcome such difficulties. Particle Swarm Optimization (PSO) is one of the newly developed optimization techniques with many attractive features. Early experimentations o

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Intelligent Techniques for Transmission Line Fault Classification, fuzzy logic system (FLS) are the highlighted ones. The expert systems have been criticized for requiring a great effort to build (knowledge acquisition) and maintain the knowledge base. The NNs, on the other hand, offer a simple and more robust solution to pattern classification problems due to the

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Power System Protection Using Machine Learning Technique,d and tested with zero sequence components of fundamental, 3. and 5. harmonic components of the post fault current signal to result the involvement of ground in the fault process. The polynomial and Gaussian kernel based SVMs are designed to provide most optimized boundary for classification. The cl

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Power Quality,plications of CI for PQ are continually evolving due to advanced PQ monitoring (or recording) devices. The objective of this chapter is to present these CI applications such as signal processing and artificial intelligence (AI) techniques to help understand, measure, and mitigate PQ phenomena. This

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Particle Swarm Optimization PSO: A New Search Tool in Power System and Electro Technology,on (PSO). These two techniques are:.The main procedure of the SOPSO is based on deriving a single objective function for the problem. The single objective function may be combined from several objective functions using weighting factors. The objective function is optimized (either minimized or maxim

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Application of Evolutionary Optimization Techniques for PSS Tuning,haotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. For on-line, offnominal operating conditions Sugeno fuzzy logic (SFL) based approach is adopted. On real time measuremen