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|Title:||Investigation of induction motor parameter identification using particle swarm optimization-based RBF neural network (PSO-RBFNN)||Authors:||Rashag, H.F.
|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|
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