Title :
Sensors-less neural MPPT control of wind generators with induction machines
Author :
Cirrincione, Maurizio ; Pucci, Marcello
Author_Institution :
Univ. Technol. de Belfort Montbeliard (UTBM), Belfort, France
Abstract :
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.
Keywords :
asynchronous machines; electric generators; neurocontrollers; sensorless machine control; wind power plants; wind turbines; GNG network; TLS EXIN full order; field oriented control; growing neural gas network; induction machines; intelligent sensorless technique; machine reference speed; maximum power point tracking technique; sensorless neural MPPT control; surface torque; turbines; voltage source converters; wind generators; wind speed; Induction generators; Induction machines; Intelligent sensors; Machine intelligence; Sensorless control; Torque measurement; Turbines; Velocity measurement; Voltage; Wind speed;
Conference_Titel :
Industrial Electronics, 2009. IECON '09. 35th Annual Conference of IEEE
Conference_Location :
Porto
Print_ISBN :
978-1-4244-4648-3
Electronic_ISBN :
1553-572X
DOI :
10.1109/IECON.2009.5415025