Fault diagnosis method of substation equipment based on You Only Look Once algorithm and infrared imaging

Abstract

Aiming at the problems of local temperature increase of cable, heating of insulator porcelain disc, local heating of lightning arresters and transformers in the process of abnormal heat generation fault diagnosis of power equipment. This study proposes a target detection method that YOLOv4 (You Only Look Once version 4), combined with the infrared image data of power equipment to locate and diagnose the faulty heating area of power equipment. A dataset is established, and image annotation is made by detecting infrared image data of typical electrical equipment collected by the inspection department using a thermal imaging camera. Then this study established a target detection model to calculate the degree of overlap between the power equipment area and the fault area, which is used to determine whether the power equipment to be measured has abnormal heat generation. Finally, the optimal detection model was established by analyzing 6000 test samples in the four kinds of power equipment image dataset. The detection results are compared with Faster Region Convolutional Neural Network algorithms, Single-Shot multibox Detectors, and You Only Look Once version 3. The results show that YOLOv4 has an average accuracy rate of 92.2%. The YOLOv4 model established in this study can effectively detect anomalies in the infrared image of power equipment can be effectively applied to infrared detection of power equipment in substations.

Keywords

• YOLOv4
• Infrared imaging
• Deep learning
• Fault diagnosis