advanced sliding mode control tuned with a pso algorithm for wind power systems: performance and efficiency enhancement

Wind energy systems based on doubly-fed induction generators are increasingly deployed due to their efficiency and cost-effectiveness. however, ensuring high power quality, system stability, and robustness under wind variability remains a challenge. this paper addresses these issues by proposing an improved integral sliding mode controller, whose gains are optimized offline using the particle swarm optimization algorithm. the control scheme regulates generator speed, dc-link voltage, and stator currents through a combined proportional-integral-sliding control law, reducing chattering while enhancing dynamic performance. the proposed ism controller was implemented on both the rotor-side and grid-side converters of a dfig-based wind energy conversion system. simulation results under varying and extreme wind conditions confirm the superiority of the ism controller over conventional pi control. notably, the ism reduced generator speed tracking error by over 75%, achieving a root mean square error of 1.06 rad/s. it also lowered stator current total harmonic distortion to below 0.85%, improved turbine efficiency to 93.6%, and minimized electromagnetic torque ripple by more than 60%. in extreme wind conditions, the controller maintained stability and compliance with grid standards, with only minor degradation in performance. overall, the proposed ism controller demonstrates strong potential for improving power quality, reliability, and efficiency in modern wind power systems. future work will explore adaptive gain tuning and experimental validation to further enhance real-time applicability and practical deployment in field conditions.