An Anomaly Detection Method for Outdoors Insulator in High-Speed Railway Traction Substation

Abstract

The outdoors insulator is an important component of the high-speed railway traction substation, which is of great significance to maintain the stability of transmission line and ensure the normal operation of transmission network. Once there is a fault for the insulator, it will cause serious transmission failure and economic loss. Therefore, a method is proposed to detect the abnormal areas of outdoors insulator in high-speed railway traction substation based on object detection and generative adversarial networks. First, we employ Faster RCNN to locate the area of insulator from the input image of traction substation. Then, the image of insulator obtained from the first step is fed into our designed generative adversarial networks to generate fake image, which is a normal image of insulator. Finally, multi-scale structural similarity algorithm is used to realize the anomaly detection of insulator and visualize anomalous areas. Experiments results on Heishan traction substation show that the proposed method is effective.