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Risk Assessment of Cascading Outages in Power Systems Using Fuzzy Neural Network

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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 provides a strategy at the system level to assess and mitigate power system cascading outages considering the probabilities of hidden failure in protection system, which affect the risk of cascading outages. Some risk indices are used to assess the risk of cascading outages. The fuzzy neural network (FNN) is used to obtain the risk indices and to propose a solution that can decrease the system cascading outage risk under limited budget. The IEEE 118-bus system is used to illustrate the methodology.

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References

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Author information

Authors and Affiliations

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

    Wei-Hua Chen, Quan-Yuan Jiang, Zhi-Yong Wang & Yi-Jia Cao

Authors
  1. Wei-Hua Chen
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  2. Quan-Yuan Jiang
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  3. Zhi-Yong Wang
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  4. 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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Chen, WH., Jiang, QY., Wang, ZY., Cao, YJ. (2006). Risk Assessment of Cascading Outages in Power Systems Using Fuzzy Neural Network. 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_205

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  • DOI: https://doi.org/10.1007/11760023_205

  • 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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