Insulator Recognition and Fault Detection Using Deep Learning Approach

Abstract

Uninterrupted power supply to electric power consumers has increasingly become a global necessity. Monitoring the health of distribution network is crucial to provide quality service. Traditional monitoring methods based on on-site patrols to detect faults have increasingly become labor-intensive and time-consuming, raising demand for new and more efficient techniques. To address this issue, we propose faster-RCNN by MXNet for both detection and classification tasks. We utilize convolutional neural network (CNN) for detecting and classifying both insulator components and faulty insulator discs from images captured on overhead electric power transmission systems. Using a dataset of images acquired through UAV (unmanned aerial vehicle) captures, detection and classification is dealt with by dividing the picture content of the training set into three classes: background, insulator and the defective part of insulator. We achieve target insulator recognition and positioning with impressive precision compared to other traditional technologies. Our work could have practical integrated implementation solutions for automated inspection of overhead transmission power line insulators. The code used can be found at https://github.com/QgZhan/Insulator-Defect-Detection. � 2020 IOP Publishing Ltd. All rights reserved.