Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks
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 linka...
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2009-01-01
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Series: | Advances in Fuzzy Systems |
Online Access: | http://dx.doi.org/10.1155/2009/241809 |
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doaj-e02999fcd499451e9073ce7e52abe8f72020-11-24T23:57:18ZengHindawi LimitedAdvances in Fuzzy Systems1687-71011687-711X2009-01-01200910.1155/2009/241809241809Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural NetworksBaburaj Karanayil0Muhammed Fazlur Rahman1Colin Grantham2School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney NSW 2052, AustraliaSchool of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney NSW 2052, AustraliaSchool of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney NSW 2052, AustraliaThis 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.http://dx.doi.org/10.1155/2009/241809 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Baburaj Karanayil Muhammed Fazlur Rahman Colin Grantham |
spellingShingle |
Baburaj Karanayil Muhammed Fazlur Rahman Colin Grantham Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks Advances in Fuzzy Systems |
author_facet |
Baburaj Karanayil Muhammed Fazlur Rahman Colin Grantham |
author_sort |
Baburaj Karanayil |
title |
Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks |
title_short |
Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks |
title_full |
Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks |
title_fullStr |
Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks |
title_full_unstemmed |
Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks |
title_sort |
identification of induction motor parameters in industrial drives with artificial neural networks |
publisher |
Hindawi Limited |
series |
Advances in Fuzzy Systems |
issn |
1687-7101 1687-711X |
publishDate |
2009-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2009/241809 |
work_keys_str_mv |
AT baburajkaranayil identificationofinductionmotorparametersinindustrialdriveswithartificialneuralnetworks AT muhammedfazlurrahman identificationofinductionmotorparametersinindustrialdriveswithartificialneuralnetworks AT colingrantham identificationofinductionmotorparametersinindustrialdriveswithartificialneuralnetworks |
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