UAV power line inspection strategy based on SAC algorithm
Cheng Xu, Jiaxin Wang, Yun Ding, Chun-Hou Zheng
Abstract
With the evolution of the smart grid industry, transmission line inspection faces significant challenges. Traditional manual inspection methods suffer from low safety, poor accuracy, and high costs. In recent years, unmanned aerial vehicle (UAV) inspection technology has been widely adopted due to its advantages of low cost, ease of control, and strong scalability. However, the limited battery capacity of UAVs poses a critical challenge in efficiently completing inspection tasks under energy constraints. This paper comprehensively considers both flight and communication energy consumption, constructing a complete inspection environment model, and proposes a deep reinforcement learning (DRL)-based algorithm to minimize total energy consumption while completing inspection tasks. Experimental results show that the proposed algorithm outperforms traditional Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Deep Deterministic Policy Gradient (DDPG) algorithms on real-world maps and power grid topologies.