Artificial neural network-based adaptive control for a DFIG-based WECS.

Journal: ISA transactions
Published Date: Dec 10, 2021

Abstract

This paper presents an artificial neural network-based adaptive control approach for a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS). The control objectives are: (1) extraction of maximum available power from the wind; (2) stator reactive power regulation according to the grid requirements. Artificial neural networks are used to estimate some nonlinear functions which represent the system uncertainties. The Lyapunov method is employed to prove the asymptotic stability of the closed-loop system. Numerical simulation results illustrate the effectiveness of the proposed control scheme in comparison with both vector control and sliding mode control techniques.

Authors

  • S Labdai
    National Polytechnic School of Algiers, LCP laboratory, 10 Av. Hassen Badi, BP 182, Algiers, Algeria.
  • N Bounar
    University of Jijel, LAJ, BP 98, Ouled-Aissa, 18000 Jijel, Algeria.
  • A Boulkroune
    University of Jijel, LAJ, BP 98, Ouled-Aissa, 18000 Jijel, Algeria. Electronic address: boulkroune2002@yahoo.fr.
  • B Hemici
    National Polytechnic School of Algiers, LCP laboratory, 10 Av. Hassen Badi, BP 182, Algiers, Algeria.
  • L Nezli
    National Polytechnic School of Algiers, LCP laboratory, 10 Av. Hassen Badi, BP 182, Algiers, Algeria.

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