Title :
Induction motor dynamic neural stator flux estimation using active and reactive power for direct torque control
Author :
Ghouili, J. ; Chériti, A.
Author_Institution :
Ecole d´´Ingenierie, Quebec Univ., Trois-Rivieres, Que., Canada
Abstract :
In this paper, a new dynamic neural network stator flux and electromagnetic torque estimation method of the squirrel cage motor direct torque control is developed. The proposed method is based on an instantaneous active and reactive power and delayed samples of stator currents and voltages of the machine. This approach requires neither resistances nor inductances. The technique is robust and independent of variation in the electromagnetic parameters. Simulation results using Simulink are presented to confirm the theoretical analysis
Keywords :
electric machine analysis computing; machine control; neural nets; parameter estimation; reactive power; squirrel cage motors; stators; torque control; Simulink; active power; direct torque control; dynamic neural stator flux estimation; electromagnetic torque estimation method; induction motor; reactive power; squirrel cage motor; stator currents; stator voltages; Electric variables control; Electrical resistance measurement; Electromagnetic measurements; Induction motors; Neural networks; Reactive power; Stators; Torque control; Torque measurement; Voltage;
Conference_Titel :
Power Electronics Specialists Conference, 1999. PESC 99. 30th Annual IEEE
Conference_Location :
Charleston, SC
Print_ISBN :
0-7803-5421-4
DOI :
10.1109/PESC.1999.789053