Abstract

The doubly fed induction generator (DFIG) has become one of the most widely adopted technologies in modern wind energy conversion systems due to its high efficiency, flexible operation, and capability to operate under variable wind-speed conditions. Nevertheless, maintaining high power quality, reducing harmonic distortion, and ensuring reliable operation under parameter uncertainties and grid disturbances remain major challenges. Conventional proportional–integral (PI)-based control approaches often suffer from limited robustness and sensitivity to system nonlinearities, which can adversely affect the dynamic performance and operational stability of wind energy systems. To address these limitations, this paper proposes an advanced hybrid control strategy based on the integration of fuzzy logic (FL) and a third-order sliding mode controller (TOSMC) for the control of DFIG-based wind turbines. The proposed FL–TOSMC combines the robustness and fast convergence properties of sliding mode control with the adaptive capability of fuzzy logic, thereby reducing dependence on accurate mathematical models and enhancing tolerance to parameter variations and external disturbances. The developed controller is applied to maximum power point tracking (MPPT) and direct field-oriented control of the DFIG to maximize energy extraction and ensure stable power regulation. The simulation results obtained in the MATLAB/Simulink (2021) environment demonstrate that the proposed strategy improves the dynamic response of the system by reducing overshoot, accelerating error convergence, minimizing steady-state oscillations, and decreasing total harmonic distortion compared with the conventional TOSMC approach. Furthermore, the proposed controller provides enhanced tracking performance, improved robustness against parameter uncertainties, and better power-quality characteristics under various operating conditions. These results indicate that the FL–TOSMC approach is an effective control solution for improving the operational performance of DFIG-based wind energy conversion systems.

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