Efficient unmanned aerial vehicle inspection and management of transmission lines in modern electric power enterprises

Abstract

This study intends to address the issues of low recognition accuracy, delayed response, and insufficient efficiency of multi machine collaboration in unmanned aerial vehicle (UAV) inspections of transmission lines in extreme environments. Thus, the study proposes an intelligent operation and inspection framework that integrates multimodal perception, deep reinforcement learning, and dynamic scheduling, which is divided into three stages. In the first stage, this study proposes an UAV hardware system integrating Light Detection and Ranging (LiDAR), infrared thermal imagers, and high-resolution visual sensors to enhance data collection efficiency. In the second stage, this study then presents a Transformer-based multimodal data fusion algorithm to improve defect recognition accuracy and robustness. It also uses a deep reinforcement learning algorithm for dynamic path planning to optimize UAV inspection routes, thereby enhancing inspection coverage and energy efficiency. In the third stage, a dynamic task allocation and resource scheduling model combining Mixed Integer Programming (MIP) and heuristic rules is proposed to achieve real-time task allocation and resource optimization for multi-UAV collaborative inspection. Experimental results show that this method achieves an F1-score of 89.8% for defect recognition in extreme environments (improved by 11% compared with TransPathNet), shortens emergency response time to 45 s (improved by 28.6% compared with PPO-MultiDrone (Proximal Policy Optimization-Multi-Drone)), increases inspection coverage to 98.7% (improved by 10.7% compared with PPO-MultiDrone), reduces energy consumption by 28.4%, and achieves task completion rate and resource utilization rate of 95.6% and 91.5% respectively (Improved by 8.4% and 16.0% respectively compared to the optimal baseline Genetic Algorithm-Mask Region-based Convolutional Neural Network). This study provides a reference method for the further development of power Internet of Things defect detection.