Deep Learning for Component Fault Detection in Electricity Transmission Lines.

Abstract Fault identification is one of the most significant bottlenecks faced by electricity transmission and distribution utilities in developing countries to deliver efficient services to the customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In view of this, we exploited the use of oblique UAV imagery with a high spatial resolution and a fine-tuned and deep Convolutional Neural Networks (CNNs) to monitor four major Electric power transmission network (EPTN) components. This study explored 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. The fault considered in this study include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. Our adapted neural network is a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via 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 the faulty components, thus achieving acceptable balance levels of F1-score and representation. Finally, comparable to other works in literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood.