Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive
Baburaj Karanayil
Muhammed Fazlur Rahman
Colin Grantham
Power Electron. & Drives Group, New South Wales Univ., Sydney, NSW;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb. 2007
Volume: 54,
Issue: 1
On page(s): 167-176
ISSN: 0278-0046
INSPEC Accession Number: 9299040
Digital Object Identifier: 10.1109/TIE.2006.888778
Current Version Published: 2007-02-05
Abstract
This paper presents a new method of online estimation for the stator and rotor resistances of the induction motor for speed sensorless indirect vector controlled drives, using artificial 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 rotor speed is synthesized from the induction motor state equations. 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. With this speed sensorless approach, the rotor resistance estimation was made insensitive to the stator resistance variations both in simulation and experiment. The accuracy of the estimated speed achieved experimentally, without the speed sensor clearly demonstrates the reliable and high-performance operation of the drive
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