Transformer Dissolved Gas Analysis Based on Structure-Optimized Convolutional Deep Network
In view of the low accuracy of transformer fault diagnosis,a structure-optimized convolutional long short-term memory network of transformer fault diagnosis model is proposed.Convolution neural network is used to extract the space characteristics of the hidden da-ta,and then long short-term memory network is adopted to extract the time characteristics of the hidden data.In the training of CNN's super parameters,the structure parameters of the network are calculated by the GA updates,which can effectively prevent the occurrence of the over-fitting of the training model.At the same time,it solves the problem that the training process falls into local optimum.Based on dissolved gas analysis(DGA)technology,the transformer fault data are preprocessed,so that they can be used as the model input for network training,and the output layer is used to get the fault diagnosis type by using Softmax function.At the same time,ROC and PRC are used as the evaluation criteria for the model's training performance.The results show that the diagnostic accuracy of the proposed model is higher than that of the CNN,LSTM,CNN-LSTM and GA-CNN fault diagnosis models,which verifies that the proposed method can effectively improve the performance of transformer fault diagnosis.
transformerdissolved gas in oilgenetic algorithmCNN-LSTMperformance evaluation
万方数据
维普
