Robust power control of DFIG using artificial neural networks for a wind energy conversion system based energy storage unit

Abstract

This paper presents the control of the dual fed induction generator (DFIG) using the Artificial Neural Networks (ANN), used in a variable speed wind energy conversion system. After having presented the simplified model of the DFIG, we approached its indirect vector control by stator field orientation. We focused on PI controllers for the control of active and reactive stator powers and the impact of its replacement by other neural controllers; which have a high robustness against parameters variations of DFIG. Simulation results on a 1.5 MW DFIG system are provided to demonstrate the robustness of the ANN controllers and the large interest of energy storage unit in such wind energy conversion systems.