Neural network controller for a permanent magnet generator applied in a wind energy conversion system

Abstract

In this paper a neural network controller for achieving maximum power tracking as well as output voltage regulation, for a wind energy conversion system (WECS) employing a permanent magnet synchronous generator, is proposed. The permanent magnet generator (PMG) supplies a DC load via a bridge rectifier and two buck–boost converters. Adjusting the switching frequency of the first buck–boost converter achieves maximum power tracking. Adjusting the switching frequency of the second buck–boost converter allows output voltage regulation. The on-times of the switching devices of the two converters are supplied by the developed neural network (NN). The effect of sudden changes in wind speed, and/or in reference voltage on the performance of the NN controller are explored. Simulation results showed the possibility of achieving maximum power tracking and output voltage regulation simultaneously with the developed NN controller. The results proved also the fast response and robustness of the proposed control system.