Application of DA-Preconditioned FINN for Electric Power System Fault Detection

• Itagaki Tadahiro

  Dept. of Electrical and Electronics Eng., Meiji University

• Mori Hiroyuki

  Dept. of Electrical and Electronics Eng., Meiji University

• Yamada Takeshi

  Tokyo Electric Power Co., Inc

• Urano Shoichi

  Tokyo Electric Power Co., Inc

Bibliographic Information

Other Title

• ＤＡ前処理付きＦＩＮＮによる電力系統事故検出
• DA マエ ショリ ツキ FINN ニ ヨル デンリョク ケイトウ ジコ ケンシュツ

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

  The Institute of Electrical Engineers of Japan

Keywords

• artificial neural network
• fuzzy inference neural network
• deterministic annealing
• FFT

Details

• CRID: 1390282679579973760
• NII Article ID: 10017276907
• NII Book ID: AN10136334
• DOI: 10.1541/ieejpes.126.283
• ISSN: 13488147
• ISSN: 03854213
• NDL BIB ID: 7856897
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7856897
• Web Site: http://www.jstage.jst.go.jp/article/ieejpes/126/3/126_3_283/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed