基于多维信息融合的电力变压器故障诊断方法研究OA北大核心CSTPCD

Considering that the existing transformer fault diagnosis methods are only for a single fault feature,it is difficult to make an accurate and comprehensive judgment on the actual situation of the power transformer.On the basis of multi-dimensional information fusion of power transformer,a fault diagnosis method combining the improved extreme learning machine and the improved D-S evidence theory is proposed.The output of the limit learning ma-chine is optimized by a posteriori probability mapping,and the probabilities of different labels are obtained,and the improved evidence theory is used to fuse the probability distribution matrix.The superiority of this method is veri-fied by comparing and analyzing the diagnosis methods before and after optimization.This method has higher fault i-dentification accuracy,and the accuracy rate reaches 96.50％,and can accurately identify various faults of power transformers,which can provide decision-making basis for condition maintenance.