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

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. •An artificial neural network based adaptive control approach for a DFIG based wind energy conversion system is presented.•The artificial neural networks are used to online estimate the system uncertainties.•A Lyapunov approach is used to prove the asymptotic stability of the closed-loop system.•A comparative study with field oriented vector control and sliding mode control is conducted.