Adaptive AI-Based Intelligent Sliding Mode Control of DFIG for Efficient and Robust Wind Energy Conversion

Abstract

The growing integration of wind energy into modern power systems requires advanced control strategies to ensure high efficiency, robustness, and power quality under variable and uncertain operating conditions. Doubly Fed Induction Generators (DFIGs) are widely employed in variable-speed wind energy conversion systems due to their flexible control capabilities and reduced converter ratings. Conventional Sliding Mode Control (SMC) offers strong robustness against parameter variations and external disturbances; however, its performance is highly dependent on controller parameter tuning and may suffer from chattering effects under fluctuating wind speeds. This paper proposes an adaptive AI-based Sliding Mode Control (AI-SMC) strategy for efficient and robust control of a DFIG-based wind energy conversion system. An artificial intelligence module is integrated into the control loop to dynamically adjust the SMC parameters in real time, enhancing system adaptability, reducing chattering, and improving active and reactive power regulation under varying wind conditions. The complete DFIG system, including both rotor-side and grid-side converters, is mathematically modeled and implemented in MATLAB/Simulink. Extensive simulations are carried out under different wind speed profiles to evaluate the effectiveness of the proposed approach. The obtained results demonstrate that the AI-based SMC significantly outperforms conventional SMC in terms of power tracking accuracy, system stability, robustness, and energy quality.