International Journal of
Physical Sciences

  • Abbreviation: Int. J. Phys. Sci.
  • Language: English
  • ISSN: 1992-1950
  • DOI: 10.5897/IJPS
  • Start Year: 2006
  • Published Articles: 2572

Full Length Research Paper

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

Hassan Farhan Rashag1, S. P. Koh1, S. K. Tiong1, K. H. Chong1 and Ahmed N. Abdalla2*        
1Department of Electronics and Communication Engineering, University Tenaga Nasional, Selangor 43009, Malaysia. 2Faculty of Electrical and Electronic Engineering, University of Technology, Pekan 26600, Malaysia.
Email: [email protected]

  •  Accepted: 05 August 2011
  •  Published: 16 September 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.

 

Key words: Induction motor, particle swarm optimization (PSO), parameter identification, least square algorithm.

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