Research on the Smart Operations and Maintenance Platform for Fishery-photovoltaic Stations Robots Based on Reinforcement Learning

Abstract

To verify the effectiveness of a reinforcement learning-based intelligent operation and maintenance platform for fishery-solar complementary photovoltaic power plants, a 1:1 scale simulation environment was constructed. Three comparative experiments were conducted: traditional manual inspections, traditional robotic inspections, and the proposed platform. Performance was evaluated using metrics such as inspection coverage and energy consumption. Simulation results show that the proposed platform achieved an inspection coverage rate of 97.8%, completing over 80% of tasks within 2 hours. This represents improvements of 19.5 and 8.3 percentage points compared to traditional manual inspections (78.3%) and traditional robotic inspections (89.5%), respectively. Energy consumption per unit area was the lowest in all four seasons, averaging 58.5 J/m², a 60.4% reduction compared to traditional manual inspections and 42.8% reduction compared to traditional robotic inspections. The reward function value stabilized after 8,000 training steps, with fluctuations of less than 5%, verifying the convergence of the algorithm. The study demonstrates that the platform is efficient and stable in complex scenarios, providing support for intelligent power plant operation and maintenance.