• DocumentCode
    694799
  • Title

    Establish Expert System of Transformer Fault Diagnosis Based on Dissolved Gas in Oil

  • Author

    Donglai Ma ; Wenjing Zhang ; Wei Yao

  • Author_Institution
    Hebei Software Inst., Baoding, China
  • fYear
    2013
  • fDate
    7-8 Dec. 2013
  • Firstpage
    681
  • Lastpage
    685
  • Abstract
    In order to avoid economic loss caused by transformer fault, it needs to monitor the transformer status in real-time, discovery and handle the transformer fault timely. Using association rule analysis method to mine fault information of transformer, analyze the reliability between the transformer fault and characteristics, revealing the correlation degree of them. Based on the analysis of transformer fault, it put forwards to represent knowledge with production method based on rules, build knowledge database using decision rules, and construct the basic expert system model, which lays the foundation for intelligent diagnosis of transformer. The model uses dissolved gas in oil, electrical parameters etc. As fault judgment, evaluate transformer condition, and enrich the knowledge base of expert system with the evaluation results.
  • Keywords
    data mining; decision making; expert systems; fault diagnosis; knowledge representation; power engineering computing; power system faults; transformers; association rule analysis method; decision rules; dissolved gas; economic loss; electrical parameters; expert system; expert system knowledge base; fault judgment; intelligent transformer diagnosis; knowledge database; knowledge representation; production method; transformer condition; transformer fault diagnosis; transformer status; Association rules; Discharges (electric); Gases; Oil insulation; Power transformer insulation; Association rules; Dissolved Gases Analysis; Expert system; knowledge base; transformer;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Cloud Computing Companion (ISCC-C), 2013 International Conference on
  • Conference_Location
    Guangzhou
  • Type

    conf

  • DOI
    10.1109/ISCC-C.2013.71
  • Filename
    6973670