Smart and Secure MPPT Control of Variable-Speed Wind Turbines Using Hybrid AI Techniques

This study introduces an enhanced MPPT strategy designed for variable-speed wind turbines operating under fast and irregular wind variations. The work combines well-known MPPT techniques, such as P&O and PID, with several intelligent optimization approaches, including fuzzy logic, ANFIS, PSO, and reinforcement learning. The hybrid framework aims to achieve faster tracking, fewer oscillations around the maximum power point, and higher stability during sudden wind disturbances. A full simulation model was built in MATLAB/Simulink and subsequently tested experimentally on the Lucas-Nülle educational wind-energy platform via a SCADA interface. The hybrid controller showed clear performance gains, with the OTC–RL combination reaching nearly 90% tracking efficiency, surpassing the traditional approaches used for comparison. The developed model also incorporated simple cybersecurity-aware monitoring to ensure reliable operation during communication disturbances, particularly during Telnet-based DoS attempts. Overall, the results demonstrate that integrating intelligent control with MPPT yields a more resilient and efficient wind-energy conversion system suitable for modern smart-grid applications.