改进贝叶斯网络在变压器故障诊断中的应用OA

In view of the low accuracy of transformer fault diagnosis,a transformer fault diagnosis method based on ISMA(Improved Slime Mold optimization Algorithm)and optimized BN(Bayesian Network)is proposed.The hill-climbing algorithm searches the oriented maximum support tree to obtain the initial structure of the Bayesian network,that is,the initial population.The reverse learning strategy and SCA(Sine Cosine Algorithm)are intro-duced into the improved slime mold optimization algorithm to increase population diversity,update population loca-tion,and avoid the population falling into local optimal.The characteristics of transformer fault state are selected by the improved code-free ratio method,and the structure of Bayesian network is optimized by the improved slime mold optimization algorithm to improve the accuracy of transformer fault diagnosis based on Bayesian network.Different kinds of test functions are used to verify that the improved slime mold optimization algorithm has the excellent per-formance of fast convergence speed and high convergence accuracy.The simulation results show that the accuracy of the training set and the test set of the ISMA-BN diagnostic model is up to 98.2%and 97.14%,respectively,which has certain research value.