• DocumentCode
    3600378
  • Title

    The Fault Diagnosis Method for Electrical Equipment Using Bayesian Network

  • Author

    Yongqiang, Wang ; Fangcheng, Lu ; Heming, Li

  • Author_Institution
    Dept. of Electr. Eng., North China Electr. Power Univ., Baoding
  • Volume
    2
  • fYear
    2009
  • Firstpage
    563
  • Lastpage
    565
  • Abstract
    Bayesian network offers a powerful map framework that can process probabilities inference. It can be used in inference and express of uncertainty knowledge. This paper introduce a new electrical equipment fault diagnosis method based on Bayesian network (BN). For example, power transformer is very important in power system as a electrical equipment. But, itpsilas very difficult to exact diagnosis the fault because power transformerpsilas complexity configuration. Now, dissolved gas analysis (DGA) is the most effective and convenient method in transformer fault diagnosis. However, the codes of DGA is too absolute, so this paper advances a new trans-former fault diagnosis method based on Bayesian network (BN). This method introduces BN method into transformer fault diagnosis and presents a new idea of finding out transformer faults rapidly and exactly. Finally, the application examples in the fault diagnosis of transformer are given which shows that this method is effective.
  • Keywords
    belief networks; chemical analysis; power apparatus; power transformers; Bayesian network; dissolved gas analysis; electrical equipment; fault diagnosis method; power transformer; uncertainty knowledge; Bayesian methods; Computer science education; Dissolved gas analysis; Educational technology; Fault diagnosis; Power engineering education; Power system analysis computing; Power system faults; Power transformers; Uncertainty; Bayesian network; electrical equipment; fault diagnosis; model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on
  • Print_ISBN
    978-1-4244-3581-4
  • Type

    conf

  • DOI
    10.1109/ETCS.2009.386
  • Filename
    4959101