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

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Abstract

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.

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Authors and Affiliations

  1. G.L.A. University, Mathura, Uttar Pradesh, India

    Sachin Goyal & Vinay Kumar Deolia

  2. Swami Vivekanand Technical University, Bhilai, Chattisgarh, India

    Sanjay Agrawal

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  1. Sachin Goyal
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  2. Vinay Kumar Deolia
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Goyal, S., Deolia, V.K. & Agrawal, S. Performance Evaluation of GA-Optimized TSFL Pitch Controller for 2 Mass Drive Train HAWTs. J. Electr. Eng. Technol. (2024). https://doi.org/10.1007/s42835-024-01786-y

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