Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (3): 43-47.doi: 10.16180/j.cnki.issn1007-7820.2021.03.008

Previous Articles     Next Articles

Research on Transformer Fault Diagnosis Based on Bayesian Network

TONG Zhaojing1,QIN Zini1,ZHAO Yunxing2,LU Tong1,ZHENG Quan1   

  1. 1. School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China
    2. Zhengzhou Branch,Shanghai Baoye Group Corp., Ltd.,Zhengzhou 450000,China
  • Received:2019-12-19 Online:2021-03-15 Published:2021-03-10
  • Supported by:
    National Natural Science Foundation of China(U1504623)


In the traditional transformer fault diagnosis ratio method, there are problems that the ratio code is too absolute and early latency failures cannot be found in time. In view of these problems, the improved non-coding ratio method and the type of transformer fault are used as the Bayesian Network nodes, and the improved search strategy and scoring function are employed to establish a transformer fault diagnosis model based on Bayesian network. The on-line fault diagnosis system of transformer based on LabVIEW is established, which realizes the functions of data acquisition, fault reasoning and historical information inquiry. Finally, the experiment proves that the system can not only effectively improve the efficiency of transformer fault diagnosis, but also detect the fault in advance to extend the service life of the transformer, and provide strategic support for the safe and stable operation of the transformer.

Key words: transformer, Bayesian network, search strategy, scoring function, fault diagnosis, LabVIEW

CLC Number: 

  • TP183