Growing Neural Gas Based MPPT for Wind Generator Using DFIG

Abstract

This paper presents Growing Neural Gas (GNG) based a maximum power point tracking (MPPT) technique for a high performance wind generator using DFIG. It is used in variable speed wind energy conversion system. Here, two back to back converters is used and connected to the stator, correspondingly FOC and VOC is done on machine and supply side converter. Constant voltage over the grid is obtained through dc link voltage. For Variable speed wind energy conversion system the maximum power point tracking (MPPT) is a very important requirement in order to maximize the efficiency. Here Neural Network has been trained to learn the turbine characteristic i.e torque versus wind speed and machine speed. It has been implemented to obtain maximum power point tracking for varying wind speed. And finally comparison has been made with and without growing neural gas.