Imperfect Preventive Maintenance Strategy for a Wind Turbine Gearbox with Dual Lubricating Oil Reservoir Integrating Environmental Impact and Sustainability

Wind turbine gearbox degradation driven by lubricating oil contamination represents one of the most environmentally and economically consequential challenges facing modern wind energy operations. This study proposes a dual-reservoir imperfect preventive maintenance strategy designed to extend gear train service life, reduce the carbon footprint of maintenance operations, and recover renewable energy production losses inherent to conventional intervention practices. The proposed architecture employs two alternating oil reservoirs. While one supplies the active lubrication circuit at full turbine output, the second undergoes filtration, completely decoupling the filtration operation from production continuity. When the concentration of metallic particles resulting from gear wear exceeds a predefined contamination threshold in the lubricating oil, imperfect preventive maintenance (IPM), performed in parallel with an oil change operation, is initiated; this action partially restores the gear train failure rate to an intermediate value between the degraded and as-new states. A mathematical model is derived to jointly optimize the filtration interval TF and the preventive maintenance interval TM, minimizing the average total cost per unit time over a finite operational horizon while explicitly incorporating environmental costs attributable to each filtration cycle. Numerical optimization yields the optimal filtration interval TF and preventive maintenance interval TM that minimize the total average cost per unit time over the operational horizon H. A dedicated environmental performance assessment demonstrates that the proposed strategy substantially recovers lost wind power generation, significantly reduces hazardous lubricating oil waste and lowers total CO2-equivalent emissions. This confirms the strategy’s meaningful contribution to sustainable wind energy operations. Sensitivity analyses confirm the robustness of the optimal solution across varying operational and economic conditions, providing wind farm operators with an adaptable and environmentally responsible decision-making framework.