• DocumentCode
    1540803
  • Title

    Fast fault section estimation in distribution substations using matrix-based cause-effect networks

  • Author

    Chen, Wen-Hui ; Liu, Chih-Wen ; Tsai, Men-Shen

  • Author_Institution
    Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
  • Volume
    16
  • Issue
    4
  • fYear
    2001
  • fDate
    10/1/2001 12:00:00 AM
  • Firstpage
    522
  • Lastpage
    527
  • Abstract
    A new method for estimating fault sections in power substations is proposed. In this paper the knowledge representation and inference procedures based on the cause-effect networks (CE-Nets) and rule matrix transformation techniques are presented. The matrix based inference procedures are proposed to estimate the possible fault sections through simple matrix operations by transforming the established CE-Nets into matrix forms. The method is superior to existing production systems in the inference speed and the process of implementation. By testing on a typical Taiwan Power Company´s (Taipower) secondary substation, the experimental results show that the effectiveness of the proposed method even for the fault domains involving multiple faults and failure operations of protective devices. Moreover, it is easy to implement and transplant into different substations
  • Keywords
    fault diagnosis; inference mechanisms; knowledge representation; matrix algebra; power distribution faults; substations; Taiwan; cause-effect networks; distribution substations; failure operations; fast fault section estimation; fault domains; inference procedures; knowledge representation; matrix-based cause-effect networks; multiple fault; protective devices; rule matrix transformation; secondary substation; Artificial neural networks; Centralized control; Circuit faults; Control systems; Fault diagnosis; Intelligent networks; Knowledge representation; Power system faults; Power system reliability; Substations;
  • fLanguage
    English
  • Journal_Title
    Power Delivery, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8977
  • Type

    jour

  • DOI
    10.1109/61.956731
  • Filename
    956731