Optimal Sliding Mode Control Based on Particle Swarm Optimization for Wind Turbine

The following research provides a unique technique that optimizes wind turbine control through the merging of Sliding Mode Control (SMC) and Particle Swarm Optimization (PSO). Wind turbines are complex systems subject to non-linearities and external disturbances, necessitating advanced control strategies for efficient operation. Sliding Mode Control is robust against such uncertainties, providing stability and high performance. However, determining the optimal gain for SMC remains a challenge. This study employs PSO, a powerful heuristic optimization technique, to fine-tune the SMC gain of robustness. The PSO algorithm iteratively searches for the optimal solution by simulating a social behavior pattern, which is effective in navigating the high-dimensional parameter space of the wind turbine model. The simulation findings demonstrate that the suggested SMC-PSO hybrid strategy significantly enhances the turbine's efficiency and stability under varying wind conditions, outperforming traditional methods. This optimization framework provides a robust and effective solution for maximizing the energy output of wind turbines, ensuring sustainable and efficient renewable energy generation.