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This study develops a robust nonlinear control, using an integral sliding mode control (ISMC) associated to an artificial neural network (ANN) approach for a variable-speed wind turbine (VSWT). At below rated speed of wind, the control aims to extract the maximum energy from the wind by the WT as well decreasing mechanical loads. Therefore, in the proposed controller, a modified sliding mode control (SMC) law with action of integral is elaborated to reach smoothly the optimal turbine speed. Moreover, the main problem of the controller design is that of external disturbances and uncertainties in the model of the dynamic system. To bridge this gap, a new technique based on ANN is employed to identify the uncertain dynamics of the wind turbine model. Thus, the system control stability was demonstrated by means of function Lyapunov analysis. The developed controller (NN-ISMC) efficiency was compared with the conventional ISMC and SMC techniques, and the simulation analysis show better performances and robustness of the developed NN-ISMC method regarding transition of response and error of tracking level.

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Intelligent Control for Increasing Maximum Extracted Power of a Wind Generation System

  • 会议论文

  • 发表日期：

    2025年

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  • I. Elidrissi,
  • F.-E. Lamzouri,
  • A. Mouradi,
  • E.-M. Boufounas

摘要

This study develops a robust nonlinear control, using an integral sliding mode control (ISMC) associated to an artificial neural network (ANN) approach for a variable-speed wind turbine (VSWT). At below rated speed of wind, the control aims to extract the maximum energy from the wind by the WT as well decreasing mechanical loads. Therefore, in the proposed controller, a modified sliding mode control (SMC) law with action of integral is elaborated to reach smoothly the optimal turbine speed. Moreover, the main problem of the controller design is that of external disturbances and uncertainties in the model of the dynamic system. To bridge this gap, a new technique based on ANN is employed to identify the uncertain dynamics of the wind turbine model. Thus, the system control stability was demonstrated by means of function Lyapunov analysis. The developed controller (NN-ISMC) efficiency was compared with the conventional ISMC and SMC techniques, and the simulation analysis show better performances and robustness of the developed NN-ISMC method regarding transition of response and error of tracking level.