• DocumentCode
    790235
  • Title

    Distribution system service restoration using the artificial neural network approach and pattern recognition method

  • Author

    Hsu, Y.-Y. ; Huang, H.-M.

  • Author_Institution
    Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
  • Volume
    142
  • Issue
    3
  • fYear
    1995
  • fDate
    5/1/1995 12:00:00 AM
  • Firstpage
    251
  • Lastpage
    256
  • Abstract
    Service restoration of a distribution system is investigated by using artificial intelligence. The purpose is to reach a proper restoration plan for the unfaulted zone after a fault has been identified and isolated. To reduce the outage period and improve service reliability, the restoration plan must be devised in a very short period. In the paper, two approaches using artificial intelligence, i.e. the artificial neural network (ANN) approach and the pattern recognition method, are developed to determine the restoration plan in a very efficient manner. The effectiveness of the proposed approaches is demonstrated by the restoration of electricity service following a fault in a distribution system in Taipei, Taiwan. It is concluded from the example that a proper restoration plan can be reached very efficiently using the proposed approaches. Therefore, it can be used by distribution system operators to reach a restoration plan
  • Keywords
    artificial intelligence; digital simulation; distribution networks; neural nets; pattern recognition; power system analysis computing; power system control; power system planning; power system restoration; Taiwan; artificial intelligence; artificial neural network; computer simulation; distribution system; electricity; outage period; pattern recognition method; restoration planning; service reliability; service restoration;
  • fLanguage
    English
  • Journal_Title
    Generation, Transmission and Distribution, IEE Proceedings-
  • Publisher
    iet
  • ISSN
    1350-2360
  • Type

    jour

  • DOI
    10.1049/ip-gtd:19951713
  • Filename
    388358