关键词: 油中溶解气体, 变压器, 故障诊断, 卷积神经网络

Abstract: Dissolved Gas Analysis (DGA) is one of the important methods for determining transformer internal faults.Aiming at the shortcomings of the traditional shallow⁃based machine learning method in transformer fault diagnosis in feature extraction and generalization ability, a transformer fault diagnosis method based on Convolutional Neural Network (CNN) was proposed. The convolution layer in the network was used for feature conversion of dissolved gas in oil, and fault sensitive features were extracted by combining with the ability of pooling layer to strengthen important features. The effects of the number of convolution kernels, the size of convolution kernels, pooling layer and network depth on the diagnostic performance of the model were studied experimentally. The models of Convolutional Neural Network, Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) were compared and analyzed by confounding matrix, ROC curve and PR curve. The experimental results show that the Convolutional Neural Network model has better diagnostic performance.

Key words: Dissolved gas in oil, Transformer, Fault diagnosis, Convolutional neural network