Research on Fault Diagnosis Method of Qil-immersed Transformel for Power Transmission and Distribution with Access of Large-scale Distributed Energy Resources
Aiming at fault diagnosis problems of transmission and distribution equipment brought by the access of large amount of distributed energy resources such as photovoltaic for the safe operation of new power systems,a fault diagnosis method of oil-immersed transformer with the access of large amount of distributed energy resources is put forward.DS evidence theory based on multi-source data fusion is proposed to construct a deep autoencoder fault diagnosis algorithm,which takes multi-source feature data as input.DS evidence theory is used to fuse the output results of stacked auto encoder model,and fault diagnosis is analyzed and studied based on the fusion results.Taking transformer maintenance data of a province in 2019 for example,the influence of multi-source data on results and the accuracy of fault analysis are evaluated.Simulation results show that multi-source data fusion can improve the accuracy of fault diagnosis.Compared with K-nearest neighbor and back propagation neural network,the proposed data fusion model has a higher fault accuracy.
distributed energy resourcespower transmission and distributiondata fusionfault diagnosis
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