Integrating Local Adaptation and Global Optimization for Off-Grid Wind Energy Systems

The increasing global demand for sustainable energy solutions has highlighted the critical role of wind energy in achieving energy sustainability and reducing carbon emissions. Off-grid wind energy systems, particularly those utilizing wind turbines, are integral to renewable energy adoption in remote areas. However, optimizing the efficiency of these systems remains a significant challenge due to highly variable and dynamic environmental conditions. To address these challenges, this study proposes a novel hybrid algorithm that integrates local adaptation and global optimization techniques. Specifically, the Perturb & Observe (P&O) method is combined with the Grey Wolf Optimizer (GWO), leveraging the rapid local adaptation capabilities of P&O and the robust global search efficiency of GWO. This hybrid approach enables real-time optimization of the duty cycle in power electronic converters connected to Permanent Magnet Synchronous Generators (PMSG) under fluctuating environmental conditions. By bridging the gap between local adaptation and global optimization, the proposed method enhances power output, improves system stability, and demonstrates superior performance compared to traditional optimization techniques. This contribution represents a practical advancement in wind turbine optimization, offering a scalable and efficient solution for off-grid wind energy systems and paving the way for more reliable and sustainable renewable energy applications in real-world scenarios.