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

This paper presents a MPPT technique for high performance wind generator with induction machine based on the Growing Neural Gas (GNG) network. Here a GNG network has been trained off-line to learn the turbine characteristic surface torque versus wind speed and machine speed, and implemented on-line 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 (FOC) of the machine has been further integrated with an intelligent sensorless technique; in particular the so called TLS EXIN full order observer has been adopted. Finally, a comparison with a classic Perturb & Observe (P&O) MPPT has been made on a real wind speed profile.