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Interactive Trouble Condition Sign Discovery for Hydroelectric Power Plants

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Advances in Neuro-Information Processing (ICONIP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5507))

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Abstract

Kyushu Electric Power Co.,Inc. collects different sensor data and weather information (hereafter, operation data) to maintain the safety of hydroelectric power plants while the plants are running. It is very rare to occur trouble condition in the plants. And it is hard to construct an experimental power generation plant for collecting the trouble condition data. Because its cost is too high. In this situation, we have to find trouble condition sign. In this paper, we consider that the rise inclination of special unusual condition data gives trouble condition sign. And we propose a trouble condition sign discovery method for hydroelectric power plants by using a one class support vector machine and a normal support vector machine. This paper shows the proposed method is useful method as a method of risk management for hydroelectric power plants.

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Author information

Authors and Affiliations

  1. System Engineering System Laboratory, Central Research Institute of Electric Power Industry, 2-11-1, Iwado Kita, Komae-shi, Tokyo, 201-8511, Japan

    Takashi Onoda & Norihiko Ito

  2. The Power System Engineering Department, Kyushu Electric Power Co.,Inc., 2-1-82, Watanabe-Dori, Chuo-ku, Fukuoka, 810-8720, Japan

    Hironobu Yamasaki

Authors
  1. Takashi Onoda
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  2. Norihiko Ito
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  3. Hironobu Yamasaki
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Editor information

Editors and Affiliations

  1. Network Design and Research Center, Kyushu Institute of Technology,, 680-4, Kawazu, Iizuka,, 820-8502, Fukuoka, Japan

    Mario Köppen

  2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

    Nikola Kasabov

  3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street,, 1142, Auckland, New Zealand

    George Coghill

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© 2009 Springer-Verlag Berlin Heidelberg

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Onoda, T., Ito, N., Yamasaki, H. (2009). Interactive Trouble Condition Sign Discovery for Hydroelectric Power Plants. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_81

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  • DOI: https://doi.org/10.1007/978-3-642-03040-6_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03039-0

  • Online ISBN: 978-3-642-03040-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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