Economically optimized maintenance of wind farms using deep reinforcement learning under stochastic degradation and power variability

Optimal maintenance scheduling is paramount to enhancing the economic viability and operational reliability of wind farms, where unplanned turbine failures and suboptimal intervention timing can result in substantial financial losses and prolonged downtime. This study develops a comprehensive, simulation-based framework to rigorously evaluate and optimize maintenance strategies under realistic stochastic degradation dynamics and variable power production profiles. Three conventional maintenance paradigms (corrective, scheduled, and threshold-based predictive maintenance) are benchmarked against an advanced deep reinforcement learning (DRL)–based policy designed to integrate prognostic information, short-term power forecasts, and maintenance crew availability to dynamically determine economically optimal intervention timing. In the proposed simulation environment, turbine degradation is modeled as an exponential failure process incorporating prognostic uncertainty, while power production is characterized as a cyclic, stochastic time series replicating the inherent variability of wind resources. Maintenance interventions incur stochastic downtimes and asymmetric costs, faithfully representing the disparate economic consequences of proactive and reactive maintenance actions. The DRL agent, trained through a combination of imitation learning and proximal policy optimization, learns an adaptive intervention policy that consistently outperforms benchmark strategies by maximizing cumulative net profit and strategically scheduling maintenance during forecasted low-production intervals to minimize opportunity costs. Extensive evaluation across multiple maintenance crew configurations demonstrates the robustness and scalability of the proposed DRL-based strategy under diverse resource constraints. The results underscore the transformative potential of intelligent, data-driven maintenance scheduling to significantly improve the profitability, resource utilization, and long-term sustainability of wind energy assets.