Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm

Abstract

In this paper, a neural inverse model controller to achieve maximum power tracking for wind energy conversion systems (WECS's) employing a double- fed induction generator (DFIG) is proposed. Changes on the firing angle of the inverter can control the operation point of the generator. This purpose complies with a neural network (NN) controller. Its feasibility and effectiveness are demonstrated by simulation results of a typical turbine/generator pair