Genetic Algorithm-Optimized Fuzzy Control for Doubly-Fed Asynchronous Generator in Variable-Speed Wind Turbines

Optimization of Doubly Fed Induction Generator (DFIG) control is crucial for applications such as renewable energy systems, industrial automation, and electric vehicles. However, the dynamic and nonlinear nature of DFIGs often challenges conventional control methods, leading to suboptimal performance. To address these limitations, this paper proposes an optimized fuzzy speed control strategy for a DFIG-based wind generator using a genetic algorithm (GA), offering superior performance compared to traditional PI controllers. The study begins with the modeling of the generator in Park’s reference frame and its indirect vector control applied to stator flux orientation. To ensure real-time tracking of the optimal operating point and maximize power extraction under varying wind speeds, a fuzzy PI speed controller is implemented. Further, the genetic algorithm—combined with a local search method—is employed to optimize the controller’s parameters, significantly reducing the tuning effort compared to trial-and-error approaches. This optimization enhances the wind system’s ability to track the maximum power point (MPP) with high efficiency. MATLAB/Simulink simulations demonstrate the effectiveness and adaptability of the proposed control scheme under diverse operating conditions. The results exhibit excellent speed regulation, reduced voltage and current ripples, and robust performance, highlighting the potential of this approach for practical implementation in variable-speed wind turbine systems.