Meta-Heuristics Optimization Based MPPT control of Wind Energy Conversion System with Permanent Magnet Synchronous Generator

This work aims to improve control and optimization of a renewable power system comprising a direct-drive H-Darrieus wind turbine (DDH-DWT) coupled to a permanent magnet synchronous motor (PMSM). The goal is maximizing wind energy extraction using advanced optimization methods.The control challenge is posed as an optimization problem, where the difficulty lies in finding the ideal controller parameters. To tackle this, we implement the metaheuristic Grey Wolf Optimizer (GWO) algorithm. GWO mimics wolf hunting behaviors to efficiently pinpoint global optima.Simulations verify the GWO-optimized controller's effectiveness - it increases wind power generation through improved tracking and adaptation under variable conditions. Results validate GWO's optimization capabilities for boosting renewable energy harvesting via control. The approach succeeds in boosting power output from the H-DWT system while maintaining robust performance under fluctuating wind conditions. The self-adaptive GWO algorithm provides precision monitoring and optimization capabilities.The results obtained from our investigation underscore the efficacy of the proposed control strategy in terms of its monitoring capabilities and robustness.