Growing Neural Gas (GNG)-Based Maximum Power Point Tracking for High-Performance Wind Generator With an Induction Machine

Abstract

This paper presents a maximum power point tracking (MPPT) technique for a high-performance wind generator with induction machine based on the growing neural gas (GNG) network. Here, a GNG network has been trained offline to learn the turbine characteristic surface torque versus wind speed and machine speed. It has been implemented online to perform the inversion of this function, obtaining the wind free speed based on the estimated torque and measured machine speed. The machine reference speed is then computed by the optimal tip speed ratio. For the experimental application, a back-to-back configuration with two voltage source converters has been considered: one on the machine side and another on the grid side. Finally, two comparisons have been made. The first approach maintains the same generator structure and compares the GNG MMPT with the classic perturb-and-observe MPPT, whereas the second approach compares the squirrel-cage induction generator with a doubly fed induction generator, both integrated with the GNG MPPT. Both comparisons have been made on real wind speed profiles.