Reinforcement Learning-Based Adaptive Optimal Fuzzy MPPT Control for Variable Speed Wind Turbine

نویسندگان

چکیده

A reinforcement learning-based adaptive optimal fuzzy controller is proposed for maximum power point tracking (MPPT) control of a variable-speed permanent magnet synchronous generator-based wind energy generation system. The algorithm consists critic, an controller, and estimator. critic built based on neuro-fuzzy inference system (ANFIS) network instead the neural as normal to reduce computation. error between output estimator used input critic. In addition, calculate update law parameters minimizing function. Moreover, scheme feedback state feedback, which does not require model well parameters, so robust uncertainties external disturbances. Besides, stabilization proof accomplished by using Lyapunov stability theorem closed-loop convergence law. Finally, effectiveness verified through simulation with various scenarios such step speed, random parameter variations. Also, comparisons other schemes in state-of-art (neural learning PI controller) are executed demonstrate advantages scheme.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2022

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2022.3205124