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Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSO-RBFNN)

High dynamic performance of induction motor drives is required for accurate system information. From the actual parameters, it is possible to design high performance induction motor drive controllers. In this paper, improving the induction motor performance using intelligent parameter identification...

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Main Authors: Rashag, H.F., Koh, S.P., Tiong, S.K., Chong, K.H., Abdalla, A.N.
Format: Article
Language:en_US
Published: 2017
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spelling my.uniten.dspace-58202018-01-03T08:14:54Z Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSO-RBFNN) Rashag, H.F. Koh, S.P. Tiong, S.K. Chong, K.H. Abdalla, A.N. High dynamic performance of induction motor drives is required for accurate system information. From the actual parameters, it is possible to design high performance induction motor drive controllers. In this paper, improving the induction motor performance using intelligent parameter identification was proposed. First, machine model parameters were presented by a set of time-varying differential equations. Second, estimation of each parameter was achieved by minimizing the experimental response based on matching of the stator current, voltage and rotor speed. Finally, simulation results demonstrate the effectiveness of the proposed method and great improvement of induction motor performance. © 2011 Academic Journals. 2017-12-08T07:26:25Z 2017-12-08T07:26:25Z 2011 Article https://www.scopus.com/record/display.uri?eid=2-s2.0-80053074635&origin=resultslist&sort=plf-f&src=s&sid=836ec3bb6bbc5fbd1734457435e74e80&sot en_US International Journal of Physical Sciences Volume 6, Issue 19, 16 September 2011, Pages 4564-4570
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language en_US
description High dynamic performance of induction motor drives is required for accurate system information. From the actual parameters, it is possible to design high performance induction motor drive controllers. In this paper, improving the induction motor performance using intelligent parameter identification was proposed. First, machine model parameters were presented by a set of time-varying differential equations. Second, estimation of each parameter was achieved by minimizing the experimental response based on matching of the stator current, voltage and rotor speed. Finally, simulation results demonstrate the effectiveness of the proposed method and great improvement of induction motor performance. © 2011 Academic Journals.
format Article
author Rashag, H.F.
Koh, S.P.
Tiong, S.K.
Chong, K.H.
Abdalla, A.N.
spellingShingle Rashag, H.F.
Koh, S.P.
Tiong, S.K.
Chong, K.H.
Abdalla, A.N.
Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSO-RBFNN)
author_facet Rashag, H.F.
Koh, S.P.
Tiong, S.K.
Chong, K.H.
Abdalla, A.N.
author_sort Rashag, H.F.
title Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSO-RBFNN)
title_short Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSO-RBFNN)
title_full Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSO-RBFNN)
title_fullStr Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSO-RBFNN)
title_full_unstemmed Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSO-RBFNN)
title_sort investigation of induction motor parameter identification using particle swarm optimization-based rbf neural network (pso-rbfnn)
publishDate 2017
_version_ 1644493784445091840
score 13.214268

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