A fault location and identification technology for power distribution networks based on autonomous unmanned aerial vehicle inspections

Traditional methods of distribution network inspections face challenges such as low efficiency, overreliance on individual expertise, and low identification rates. UAV(unmanned aerial vehicle), as a new type of inspection platform, have been widely used in power inspection activities, a specialized technology for autonomous intelligent inspection of distribution networks is required. This paper utilizes an object detection method based on Convolutional Neural Networks (CNN) to simulate training and learning from UAV inspection photos, achieving localization of distribution equipment components. It uses deep learning algorithms to autonomously identify specific defect locations, enabling automatic discrimination of inspection faults.