Identification of induction motor parameters in industrial drives with artificial neural networks

  • Authors:

  • Baburaj Karanayil;Muhammed Fazlur Rahman;Colin Grantham

  • Affiliations:

  • School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, Australia;School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, Australia;School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, Australia

  • Venue:
  • Advances in Fuzzy Systems
  • Year:
  • 2009

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Abstract

This paper presents a new method of online estimation of the stator and rotor resistance of the induction motor in the indirect vector-controlled drive, with artificial neural networks. The back propagation algorithm is used for training of the neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, is investigated with the help of simulations for variations in the stator and rotor resistance from their nominal values. Both types of resistance are estimated experimentally, using the proposed neural network in a vector-controlled induction motor drive. Data on tracking performances of these estimators are presented.With this approach, the rotor resistance estimation was found to be insensitive to the stator resistance variations both in simulation and experiment.