Growing Neural Gas based MPPT of variable pitch wind generators with induction machines

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. To this aim, an MPPT technique based on the Growing Neural Gas (GNG) wind turbine surface identification and corresponding function inversion has been adopted here 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 permitting the machine to work at rated power and torque. A further enhancement of the pitch angle selection by a simple Perturb & Observe (P&O) method has been integrated to cope with the wind estimation errors at machine rated speed. The proposed methodology has been verified both in numerical simulation and experimentally on a properly devised test set-up.