Detection of Potential Faults in the Electricity Distribution Network Using Unmanned Aerial Vehicles and Thermal Cameras Through Deep Learning Methods

Abstract

In this article, a software-based unmanned aerial vehicle (UAV) prototype has been developed for exploration purposes, replacing the current manual inspection of energy transmission lines (ETLs) by human teams. The UAV's capabilities address the limitations of human viewing angles, ensuring more efficient exploration activities. Real-time exploration is now achieved through data collected with UAVs and processed using Convolutional Neural Networks (CNNs) and image processing techniques. The UAV’s path and detected objects are autonomously processed in the Cartesian coordinate system. The autonomous conductor tracking system provides bidirectional tracking of conductors in the distribution network through color space range and Faster R-CNN conductor detection. Incorporating a proportional-integral-derivative (PID) design, the UAV is capable of operating in adverse weather conditions. An additional overheating warning system protects equipment in the electricity distribution network (EDN) from potential issues related to uneven load distribution, economic life, and labor errors. Through modeling studies with convolutional neural networks and field data, impressive F-1 scores have been achieved for detecting various anomalies: 77% for trees in contact with the line, 67% for concrete equipment exceeding its economic life, 88% for bird nests, 55% for cracked porcelain insulators, 89% for foreign objects, and 97% for nonuse of original fuses.