Deep learning for component fault detection in electricity transmission lines

Abstract Component fault detection and inventory are one of the most significant bottlenecks facing the electricity transmission and distribution utility establishments especially in developing countries for delivery of efficient services to the customers and to ensure proper asset audit and management for network optimization and load forecasting. For lack of technology and data, insecurity, the complexity associated with traditional methods, untimeliness, and general human cost, electricity assets monitoring, and management have remained a big problem in many developing countries. In view of this, we explored the use of oblique UAV imagery with high spatial resolution and fine-tuned deep Convolutional Neural Networks (CNNs) for automatic faulty component inspection and inventory in an Electric power transmission network (EPTN). This study investigated the capability of the Single Shot Multibox Detector (SSD), a one-stage object detection model on the electric transmission power line imagery to localize, detect and classify faults. Our proposed neural network model is a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults through a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision (mAP) of 89.61%. All the developed SSD-based models achieve a high precision rate and low recall rate in detecting faulty components, thus achieving acceptable balance levels of F1-score and representation. We have established in this paper that combined use of UAV imagery and computer vision presents a low-cost method for easy and timely electricity asset inventory, especially in developing countries. This study also provides the guide to various considerations when adopting this technology in terms of the choice of deep learning architecture, adequate training samples over multiple fault characteristics, effects of data augmentation, and balancing of intra-class heterogeneity.