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Design of Self-adaptive Single Neuron Facts Controllers Based on Genetic Algorithm

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

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Abstract

With the growing application of Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM), the coordinating problem of SVC and STATCOM controllers in joint operation must be considered in modern power systems. This paper firstly establishes the nonlinear differential-algebra equations model of a single-machine infinite-bus (SMIB) power system installed with a SVC and a STATCOM and points out the possibility of the negative interactions between SVC and STATCOM controllers in this SMIB power system. Hence, a self-adaptive single neuron (SSN) control approach based on genetic algorithm is designed to eliminate the negative interactions and improve the stability of the closed-loop SMIB power system. The detailed simulation results demonstrate the effectiveness of the proposed SSN controllers.

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Author information

Authors and Affiliations

  1. College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China

    Quan-Yuan Jiang, Chuang-Xin Guo & Yi-Jia Cao

Authors
  1. Quan-Yuan Jiang
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  2. Chuang-Xin Guo
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  3. Yi-Jia Cao
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Jiang, QY., Guo, CX., Cao, YJ. (2006). Design of Self-adaptive Single Neuron Facts Controllers Based on Genetic Algorithm. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_193

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  • DOI: https://doi.org/10.1007/11760023_193

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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