Improved Control Approaches for Dynamic Voltage Restoration in Doubly Fed Induction Generator-Based Wind Turbines

This research presents improved control techniques for dynamic voltage recovery in dual-feed induction generator (DFIG) wind turbine systems. The paper aims to enhance power quality and voltage stability for sensitive loads prone to grid outages and wind-induced voltage fluctuations. To minimize voltage drop and ensure continuous load voltage support, the DFIG system incorporates a dynamic voltage restorer (DVR). The effectiveness of several control strategies was developed and compared, including adaptive neuro-fuzzy inference system (ANFIS), proportional-integral-derivative (PID), tilt-integral derivative (TID), proportional integral (PI), and fractional-order proportional integral derivative (FOPID). MATLAB/Simulink was used to simulate the proposed system under various fault and voltage unbalance scenarios for a 9 MW grid-connected dual-feed induction generator-based wind farm. Simulation results show that advanced controllers, particularly ANFIS and FOPID, significantly improve the performance of DVRs by stabilizing load voltage patterns and efficiently adjusting voltage drop. Compared to all DVR controllers, the proposed modified ANFIS control system resolves the signal overshooting and ripples issues associated with traditional control methods, such as the classic PI control system. In DVRs, ripples and excess voltage increase with PI (16%), PID (32%), TID (10%), and FOPID (10%), while the ANFIS control system exhibits no ripple or excess voltage injections at the same time (0.07s-0.09s). These findings demonstrate how intelligent, microcontroller-level control strategies can improve dynamic voltage recovery and power quality in modern power systems.