Self-adjusting Residual Network Diagnosis Model for Substation Equipment Thermal Defects Based on Infrared Image

A self-adjusting residual network diagnosis model for substation equipment defects based on infrared images, is proposed. In this model, substation equipment defect state diagrams are used as the data source to solve the problems of inaccurate diagnosis caused by the sim-ilar appearance in some devices, low discrimination in various defect states, and excessive parameters in some models. Firstly, the residual network infrastructure is optimized by the convolution kernel decomposition technology to reduce the number of model parameters. Secondly, the multi-scale convolution feature fusion method is adopted to fuse the features of low layers and deep layers so as to improve the recognition accuracy of the defect state diagrams. Finally, a Bayesian optimization algorithm based on constraint improvement is proposed. Under the constraints of verification set and network volume, the self-adjustment of hyper parameters, such as the number of convolution kernels and the depth of the network, is realized to obtain a lightweight diagnostic model with optimal performance. The results show that the state recognition accuracy of improved model reaches 94.53%, which is about 3% higher than that of Alexnet and Resnet. It can provide a reference for fault diagnosis of power equipment. © 2020, High Voltage Engineering Editorial Department of CEPRI. All right reserved.