Enhancing vector control performance in DFIG-based wind turbine through ANFIS controller using real variable wind speed profile

Abstract

This article focuses on enhancing vector control for a Doubly Fed Induction Generator (DFIG) through the integration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method and comparing it with the classical Proportional-Integral (PI) controller. In this study, the wind turbine operates on a DFIG connected to the AC power grid. The proposed vector-ANFIS controller is implemented on the rotor-side converter to regulate the current and achieve the desired torque for Maximum Power Point Tracking (MPPT). The system was modeled and simulated using MATLAB Simulink. The simulation employed a real variable wind speed profile to reflect a real-world scenario. our contribution was also to compress several hours of collected data into a matter of seconds, enabling us to simulate a multi-hour period and observe the results in MATLAB. The results demonstrate the superior performance of ANFIS compared to PI, indicating its potential utility in enhancing the power quality and stability of wind power systems.