Neural MPPT Control of Wind Generators With Induction Machines Without Speed Sensors

This paper presents a maximum power point tracking (MPPT) technique for high-performance wind generators with induction machines based on the growing neural gas (GNG) network. In this paper, a GNG network has been trained offline to learn the turbine-characteristic surface torque versus wind and machine speeds and has been implemented online to obtain the wind tangential speed on the basis of the estimated torque and measured machine speed (surface function inversion). The machine reference speed is then computed on the basis of 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 the other on the grid side. The field-oriented control of the machine has been further integrated with an intelligent sensorless technique; in particular, the so-called total least squares (TLS) EXIN full-order observer has been adopted. Finally, a comparison with a classic perturb-and-observe MPPT has been made on a real wind-speed profile.