Hybrid Fennec Fox–Sand Cat optimized cascaded ANFIS MPPT for enhanced control of DFIG-based WECS with grid support

The Doubly Fed Induction Generator (DFIG)-based Wind Energy Conversion System (WECS) has gained significant attention due to its capability to operate efficiently over a wide range of wind speeds and in various modes. To enhance the performance and reliability of such systems, advanced control strategies are essential. This study introduces a Hybrid Fennec Fox-Sand Cat Optimization Algorithm (HFFSCOA) integrated with a Cascaded Adaptive Neuro-Fuzzy Inference System (ANFIS) for Maximum Power Point Tracking (MPPT) control of DFIG-based WECS. The proposed hybrid algorithm adaptively tunes ANFIS membership functions and parameters, improving its learning capability and ensuring accurate maximum power tracking with minimal oscillations. The coordinated control of the Rotor Side Converter (RSC) and Grid Side Converter (GSC) maintains a stable DC-link voltage and facilitates smooth power delivery to the grid. Moreover, d-q transformation is employed for harmonic suppression, enhancing overall power quality and ensuring compliance with grid standards. Simulation results in MATLAB/Simulink demonstrate the proposed controller's effectiveness in minimizing active (Ps) and reactive (Qs) power fluctuations while achieving a remarkably low total harmonic distortion (THD) of 0.09%. Consequently, the proposed HFFSCOA-Cascaded ANFIS MPPT offers an intelligent, efficient, and scalable solution for sustainable wind energy systems seamlessly integrated into modern smart grids.