On-line stator and rotor resistance estimation scheme for vector-controlled induction motor drive using artificial neural networks

This paper presents a new method of on-line estimation for the stator and rotor resistances of the induction motor in the indirect vector controlled drive, using artificial neural networks. The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and the actual state variable of a neural network model is back propagated to adjust the weights. of the neural network model, so that the actual state variable tracks the desired value. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive; together with these estimators, are investigated with the help of simulations for variations in the stator and rotor resistances from their nominal values. Both resistances are estimated experimentally, using the proposed neural network in a vector controlled induction motor drive. Data on tracking performances of these estimators are presented. The rotor resistance estimation was found to be insensitive to the stator resistance variations both in simulation and experiment.