National Polytechnic School of Algiers, LCP laboratory, 10 Av. Hassen Badi, BP 182, Algiers, Algeria.
University of Jijel, LAJ, BP 98, Ouled-Aissa, 18000 Jijel, Algeria.
ISA Trans. 2022 Sep;128(Pt B):171-180. doi: 10.1016/j.isatra.2021.11.045. Epub 2021 Dec 10.
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.
本文提出了一种基于人工神经网络的双馈感应发电机（DFIG）风力发电系统（WECS）自适应控制方法。控制目标为：（1）从风中提取最大可用功率；（2）根据电网要求调节定子无功功率。人工神经网络用于估计表示系统不确定性的一些非线性函数。采用李雅普诺夫方法证明了闭环系统的渐近稳定性。数值仿真结果表明，与矢量控制和滑模控制技术相比，所提出的控制方案具有有效性。
