AI-Driven Model Predictive Control for Optimized DFIG Performance in Dynamic Wind Conditions

AI-driven control, Model Predictive Control, Doubly Fed Induction Generator, Wind energy optimization, Machine Learning, Deep Reinforcement Learning, Adaptive Control The inherent variability of wind conditions presents significant challenges to the efficiency and stability of wind energy conversion systems. Doubly Fed Induction Generators (DFIGs) are widely deployed due to their operational flexibility and superior power extraction capabilities. However, conventional control methodologies, including proportional-integral-derivative (PID) controllers and classical Model Predictive Control (MPC), often fail to adapt effectively to fluctuating wind conditions. This paper presents an advanced AI-driven MPC framework, incorporating machine learning (ML) for wind profile prediction and deep reinforcement learning (DRL) for dynamic optimization of control strategies. The proposed methodology enhances real-time decision-making, mitigating power fluctuations and improving the overall efficiency of DFIG-based wind energy systems.