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Generalized Minimum Variance Neuro Controller for Power System Stabilization

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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

This paper presents a power system stabilizer design that uses a generalized minimum variance-inverse dynamic neuro controller, which is the combination of the inverse dynamic neural model, the generalized minimum variance, and the neuro compensator. An inverse dynamic neural model represents the inverse dynamics of the system. The inverse dynamic neural model is trained to provide control input into the system, which makes the plant output reach the target value at the next sampling time. Once the inverse dynamic neural model is trained, it does not require retuning for cases with other types of disturbances. In this paper, a generalized minimum variance control scheme is adapted to prevent unstable system performance caused by non-minimum phase characteristics. In addition, a neural compensator is designed to compensate for modeling errors. The proposed control scheme is tested in a multimachine power system.

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Author information

Authors and Affiliations

  1. Dept. of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

    Hee-Sang Ko

  2. Dept. of Electrical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA

    Kwang Y. Lee

  3. Faculty of Electrical and Electronic Engineering, Cheju National University, Cheju, 690-756, Korea

    Min-Jae Kang & Ho-Chan Kim

Authors
  1. Hee-Sang Ko
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  2. Kwang Y. Lee
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  3. Min-Jae Kang
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  4. Ho-Chan Kim
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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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Ko, HS., Lee, K.Y., Kang, MJ., Kim, HC. (2006). Generalized Minimum Variance Neuro Controller for Power System Stabilization. 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_194

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  • DOI: https://doi.org/10.1007/11760023_194

  • 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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