• DocumentCode
    285638
  • Title

    Hybrid fault analysis system using neural networks and artificial intelligence

  • Author

    Fukuyama, Yoshikazu ; Ueki, Yoshiteru

  • Author_Institution
    Fuji Electric Corp. Res. & Dev. Ltd., Tokyo, Japan
  • Volume
    4
  • fYear
    1992
  • fDate
    3-6 May 1992
  • Firstpage
    1709
  • Abstract
    The system detects the fault type and approximate fault points using information about operated relays, circuit breakers, and the fault voltage/current waveform. Faulted sections are estimated in the expert system (ES) part and fault voltage/current waveform recognition is performed in the NN part. Since power systems require high reliability, the system uses a verification procedure for the result of waveform recognition obtained by NNs, based on model-based reasoning (MBR). Four different types of NNs were examined and an appropriate NN was selected for waveform recognition. This system is a combination of NN, ES, and MBR technologies for analyzing faults, and it provides functions which cannot be obtained by conventional methods
  • Keywords
    circuit breakers; expert systems; fault location; model-based reasoning; neural nets; power system analysis computing; approximate fault points; artificial intelligence; circuit breakers; expert system; fault type; fault voltage/current waveform; model-based reasoning; neural networks; operated relays; power systems; verification procedure; Artificial neural networks; Circuit breakers; Circuit faults; Electrical fault detection; Expert systems; Fault detection; Neural networks; Power system relaying; Power system reliability; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
  • Conference_Location
    San Diego, CA
  • Print_ISBN
    0-7803-0593-0
  • Type

    conf

  • DOI
    10.1109/ISCAS.1992.230346
  • Filename
    230346