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

Hassan Farhan Rashag, Johnny Siaw Paw Koh, Sieh Kiong Tiong, Kok Hen Chong, Ahmed N. Abdalla

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)4564-4570
Number of pages7
JournalInternational Journal of Physical Sciences
Volume6
Issue number19
Publication statusPublished - 16 Sep 2011

Fingerprint

parameter identification
induction motors
Induction motors
Particle swarm optimization (PSO)
Identification (control systems)
Neural networks
optimization
rotor speed
information systems
stators
Stators
controllers
Information systems
Differential equations
differential equations
Rotors
Controllers
Electric potential
electric potential
simulation

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Physics and Astronomy(all)

Cite this

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AU - Chong, Kok Hen

AU - Abdalla, Ahmed N.

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AB - 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.

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