Please use this identifier to cite or link to this item: http://dspace.uniten.edu.my/jspui/handle/123456789/5820
Title: Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSO-RBFNN)
Authors: Rashag, H.F. 
Koh, S.P. 
Tiong, S.K. 
Chong, K.H. 
Abdalla, A.N. 
Issue Date: 2011
Abstract: 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.
URI: http://dspace.uniten.edu.my/jspui/handle/123456789/5820
Appears in Collections:COE Scholarly Publication

Show full item record

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.