Growing Neural Gas-Based MPPT of Variable Pitch Wind Generators With Induction Machines

Abstract

This paper proposes a maximum power point tracking (MPPT) technique for variable pitch wind generators with induction machines, which can suitably be adopted in both the maximum power range and the constant power range of the wind speed. For this purpose, an MPPT technique based on the growing neural gas (GNG) wind turbine surface identification and the corresponding function inversion has been adopted to cover also the situation of constant rated power region. This has been obtained by including the blade pitch angle in the space of the data learnt by the GNG and feeding back the estimated wind speed to compute the correct value of the pitch angle, which permits the machine to work at rated power and torque. A further enhancement of the pitch angle selection by a simple perturb & observe method has been added to cope with slight wind estimation errors occurring at machine rated speed. The proposed methodology has been verified both in numerical simulation and experimentally on a properly devised test setup. The correct behavior of the system has been proved also on a real wind speed profile on a daily scale.