GIS partial discharge fault diagnosis based on improved CNN
Deep Neural Networks(DNN)is widely used for fault classification using Partial Discharges(PD)to assess the insulation level of various electrical equipment,but there is a risk of false positives for untrained PD fault data.Based on this,a deep ensemble model based on improved CNN is proposed.First-ly,UHF sensors are used to collect PD signals for seven kinds of GIS insulation defect faults in the field,and PRPD spectra are formed and analyzed.Secondly,the collected data is imported into the model for un-certainty estimation and the confidence value and the threshold value of the model is determined.The influ-ence of the scale of the CNN deep ensemble model on the classification performance is studied again.The results show that the proposed model has good detection performance for unknown partial discharge faults ex-perimental,and has certain engineering practice value.
gas insulated switchgearfault diagnosispartial discharge detectionconvolutional neural net-worksdeep integration model
万方数据
维普
