By modifying the pitch and yaw angles of the blades, the study presents a hybrid neural-fuzzy controller for wind turbines that enhances performance in variable wind conditions. The suggested system integrates fuzzy logic (for decision-making) and artificial neural networks (for learning), unlike conventional PID and fuzzy PID controllers, which have trouble with nonlinear dynamics. It employs two stages of control: pitch control at high wind speeds and yaw control at low wind speeds. The hybrid controller performs noticeably better than PID-based techniques, according to MATLAB/Simulink simulations, lowering pitch control error by 40%, stabilizing the system in 0.1 seconds instead of over 23 seconds, and maintaining power output within 1.7% of nominal levels. Additionally, it efficiently preserves proper yaw alignment, improving the reliability of power generation. The study's findings suggest that neural-fuzzy controllers can significantly increase wind turbine lifespan, stability, and efficiency. It also suggests that their practical application be customized for turbine and site conditions.
Daoud et al. (Sat,) studied this question.