conventional maximum power point tracking (MPPT) control strategies fail to effectively account for the dynamic influence of the pitch angle
leading to reduced wind energy utilization efficiency. Under medium and high wind speed conditions
traditional PI-based pitch angle controllers struggle to maintain stable power output. To address these challenges
this paper proposes a collaborative optimization strategy that integrates Physics-Informed Neural Networks (PINNs) with Model Predictive Control (MPC). The PINN model accurately captures the dynamic characteristics of wind turbines
while an online training mechanism based on a stochastic weight adjustment algorithm enhances adaptability. A unified MPC strategy is developed
considering both torque and pitch angle as control variables. By dynamically adjusting the cost function
the proposed approach achieves maximum power tracking under low wind speed conditions and ensures stable power output under medium and high wind speed conditions. Experimental results validate that the proposed method significantly improves the dynamic response of the wind turbines
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