Performance Evaluation of GA-Optimized TSFL Pitch Controller for 2 Mass Drive Train HAWTs

In the over-nominal wind speed region, the power output of a wind turbine is controlled by adjusting the blade pitch angle. A wind turbine exhibits nonlinear relations with varying wind speeds; therefore, designing a suitable pitch angle controller for the wind turbines is a significant engineering challenge. The current article primarily focuses on developing a Takagi–Sugeno fuzzy logic (TSFL) tuned PID pitch controller for a wind turbine connected to an electric generator through a 2-mass drive train. Further, the second stage presents a comparative analysis between optimized and Unoptimised power outputs from the permanent magnet synchronous generator. The Genetic Algorithm (GA) modifies the mutation rate and crossover point number. MATLAB/Simulink software validated the GA approach and produced superior results. Thus, the proposed GA-optimized controller better adjusts the wind turbine’s blade pitch angle at higher wind speeds than the unoptimized pitch controller.