DA前処理付きFINNによる電力系統事故検出  [in Japanese] Application of DA-Preconditioned FINN for Electric Power System Fault Detection  [in Japanese]

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Author(s)

Abstract

This paper proposes a hybrid method of Deterministic Annealing (DA) and Fuzzy Inference Neural Network (FINN) for electric power system fault detection. It extracts features of input data with two-staged precondition of Fast Fourier Transform (FFT) and DA. FFT is useful for extracting the features of fault currents while DA plays a key role to classify input data into clusters in a sense of global classification. FINN is a more accurate estimation model than the conventional artificial neural networks (ANNs). The proposed method is successfully applied to data obtained by the Tokyo Electric Power Company (TEPCO) power simulator.

Journal

  • IEEJ Transactions on Power and Energy

    IEEJ Transactions on Power and Energy 126(3), 283-289, 2006-03-01

    The Institute of Electrical Engineers of Japan

References:  15

Cited by:  2

Codes

  • NII Article ID (NAID)
    10017276907
  • NII NACSIS-CAT ID (NCID)
    AN10136334
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    03854213
  • NDL Article ID
    7856897
  • NDL Source Classification
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL Call No.
    Z16-794
  • Data Source
    CJP  CJPref  NDL  J-STAGE 
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