Fault diagnosis and state estimation of power equipment based on fuzzy Bayesian network

With the ambiguity of fault condition and the uncertainty of panoramic state during the operation of power equipment, a Bayesian network method was improved by combining fuzzy functions for fault diagnosis and state estimation. In this network, Bayesian probability was used to measure the correlation between multi-dimensional individual indicators and different faults, and a time-varying scoring function was constructed to integrate the feature information with different timeliness and to quantify the fuzzy fault condition. In addition, all faults were graded based on the hazard, and the multiple fuzzy functions were constructed and integrated in the network to measure the fuzzy importance of continuously changing fault conditions in panoramic state estimation. The comprehensive score could be calculated, and the operating state with potential failure could be inferred. The 500kV oil-immersed power transformer was taken as an example to test the effectiveness of the proposed method, and the results showed that the application accuracy of the proposed method was much higher than the existing linear evaluation methods. © 2021, Editorial Department of CIMS. All right reserved.