Predictive maintenance for wind turbines: A physics-driven reinforcement learning strategy with economic-reliability collaborative optimization

Wind turbine maintenance optimization faces challenges in balancing economic efficiency with operational reliability under environmental uncertainty. Traditional maintenance approaches exhibit limitations in adaptive decision-making, leading to increased operational costs and reliability risks. This study develops a physics-informed reinforcement learning framework that integrates established domain knowledge with adaptive decision algorithms. The approach embeds physical principles—including Weibull wind dynamics and multi-stage degradation models—into a reinforcement learning architecture, while introducing bidirectional temperature-degradation coupling for enhanced failure prediction. A high-fidelity simulation environment enables policy training through Proximal Policy Optimization, capturing complex interactions between environmental variability and equipment deterioration. The framework was validated through case study implementation using northern China wind farm operational data. Results demonstrate zero-failure operation over simulated 19-year lifecycles, with economic performance improvements of 109.3 % and 54.5 % compared to conventional periodic and threshold-based maintenance strategies. By integrating physical constraints with intelligent algorithms, the method achieves adaptive maintenance decisions based on multi-dimensional state information.