A Transformer Fault Diagnosis Method Based on Bayesian Network

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This paper summarizes the fault types and fault hazards of the transformer, and combs the corresponding relationship between the transformer fault types and the fault symptoms, which provides reference for the transformer fault diagnosis and state maintenance. Based on the analysis of the existing transformer fault diagnosis, the transformer fault diagnosis based on Bayesian network is mainly studied. Bayesian network has advantages in machine learning, data reasoning and so on. It has been widely applied in the field of fault diagnosis. In this paper, the QMR model of transformer fault diagnosis is established by using the principle of Bayesian network and the relationship between fault type and fault feature, and the model is used to diagnose the specific fault. Then the paper further analyzes the result of fault diagnosis in order to prove the correctness and validity of Bayesian network.