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A Hybrid Genetic Algorithm/Particle Swarm Approach for Evaluation of Power Flow in Electric Network

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Advances in Machine Learning and Cybernetics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3930))

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Abstract

This paper presents an investigation of possible hybrid genetic algorithm / particle swarm optimization approaches to evaluate the flow of electric power in power transmission network. The possible schemes are presented and their performances are illustrated by applying them to the power flow problem of the Klos Kerner 11-busbar system. The performance of the hybrid algorithm in terms of reliability is further improved by applying the optimal values for both inertia weight and mutation probability which are found through parameter sensitivity analyses.

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Authors and Affiliations

  1. Computational Intelligence Applications Research Laboratory, Department of Electrical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    T. O. Ting, K. P. Wong & C. Y. Chung

Authors
  1. T. O. Ting
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  2. K. P. Wong
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  3. C. Y. Chung
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Editor information

Editors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, P.O. Box, Hong Kong, China

    Daniel S. Yeung

  2. School of Creative Media, City University of Hong Kong,, China

    Zhi-Qiang Liu

  3. Department of Mathematics and Computer Science, Hebei University, 071002, Baoding, Hebei, P.R. China

    Xi-Zhao Wang

  4. School of Electrical and Information Engineering, University of Sydney, 2006, NSW, Australia

    Hong Yan

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© 2006 Springer-Verlag Berlin Heidelberg

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Ting, T.O., Wong, K.P., Chung, C.Y. (2006). A Hybrid Genetic Algorithm/Particle Swarm Approach for Evaluation of Power Flow in Electric Network. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_95

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  • DOI: https://doi.org/10.1007/11739685_95

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33584-9

  • Online ISBN: 978-3-540-33585-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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