Fault diagnosis of transformer based on residual BP neural network

The diagnostic performance of the transformer fault diagnosis method based on traditional BP neural network tends to be saturated when the network model reaches a certain depth, so it cannot further improve the diagnostic performance of the network model. In this case, deepening the depth of the network model will lead to a decline in the diagnostic performance. In addition, under the condition of small sample data, the traditional BP neural network still cannot achieve a better diagnostic accuracy. Therefore, in order to improve the diagnostic accuracy of transformer fault diagnosis and the diagnostic performance under small sample data, a transformer fault diagnosis method based on residual BP neural network is proposed. The depth of BP neural network is deepened by stacking multiple residual network modules, and the identity mapping learning of traditional BP neural network is converted into the residual learning of BP neural network. At the same time, in each residual network module, the input information of the module can be transmitted across layers within the module, so that the input information of each module can be better transmitted to the deep network, and then a better diagnosis model can be trained under the condition of small sample data. Experimental results show that, compared with the traditional deep BP neural network and the traditional shallow BP neural network, the proposed method has higher diagnostic accuracy and better diagnostic performance under small sample data. © 2020, Electric Power Automation Equipment Press. All right reserved.