Fault Diagnosis of Transformer Based on Capsule Network

Traditional methods for transformer fault diagnosis are difficult to fit the complex nonlinear relationship between dissolved gas and fault type accurately, and their diagnosis accuracy is limited. To improve the accuracy of transformer fault diagnosis, a method of transformer fault diagnosis based on capsule network (CapsNet) is proposed. The convolutional layers with strong feature extraction ability are used to map the dissolved gas data into the feature space to realize the automatic extraction of key features. Furthermore, the mapping relationship between feature and fault type is established by primary capsule layers and digital capsule layers. Dynamic routing algorithm and back propagation algorithm are used to complete the training process of CapsNet. The simulation results show that the fault diagnosis performance of CapsNet is better than that of the traditional methods such as convolutional neural network, multi-layer perceptron, support vector machine, extreme gradient boosting tree, and light gradient lifting machine in different input characteristics and data scales, which can effectively meet the needs of diagnosis accuracy and provide guidance for the operation pattern of relay. The research can provide references for the fault diagnosis of the transformer.
QC 20220118