Insulator Surface Breakage Recognition Based on Multiscale Residual Neural Network
Lingcong She, Yadong Fan, Jianguo Wang, Li Cai, Jian Xue, Mengxi Xu
Abstract
Insulators are one of the widely used important devices in power systems, and proposing an accurate and effective online monitoring method for insulator defects is an urgent need for intelligent and informative power systems. We propose a multiscale residual neural network for insulator surface breakage recognition. The proposed multiscale convolution filter uses three different size convolution kernels for convolution filtering, and performs feature maps fusion, which can enrich the spatial correlation and channel correlation of feature maps. We investigated the effects of different learning rates on the model performance, and confirmed that the model performs best with learning rate of 10-6. Compared with VGG16, Inception_v3 and Resnet50, the multiscale residual neural network model has significant advantages in detection capability and occupies less computational resources, and the detection speed can meet the industrial real-time detection requirements. Through dark-light and noise experiments, it is demonstrated that the proposed method is robust and can effectively deal with low-quality insulator images.