Research on partial discharge diagnosis of distribution network overhead lines based on semi-supervised learning
In response to the problem of insufficient data annotation rate during the on-site operation and maintenance of distribution network equipment,a hybrid framework model based on semi supervised learning strategy is designed in this paper.While retaining the key information of the voltage sequence,the one-dimensional signal is converted into a multi feature map input form,trained through partially annotated data label information and data reconstruction error,and combined with soft voting method for multi feature decision fusion.The experimental test results show that under the conditions of dataset annotation rates of 30%,60%,70% and 90%,the average recognition accuracy is 91.2960%,95.564 3%,96.726 3%,and 96.991 8%,respectively.Compared with models such as ResNet and VGG based on supervised learning,the semi supervised hybrid framework model improves the accuracy by about 5%,providing a new model method for early diagnosis of partial discharge in overhead transmission lines,it can improve the maintenance and management level of overhead lines.
distribution network overhead linessemi-supervised learningpartial dischargefault dia-gnosisdeep learningsignal processing
NETL
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万方数据
