Identification of induction machine electrical parameters using genetic algorithms optimization

Konstantinos Kampisios, Pericle Zanchetta, Chris Gerada, Andrew Trentin

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

25 Citations (Scopus)

Abstract

This paper introduces a new heuristic approach for identifying induction motor equivalent circuit parameters based on experimental transient measurements from a vector controlled Induction Motor (I.M.) drive and using an off line Genetic Algorithm (GA) routine with a linear machine model. The evaluation of the electrical motor parameters is achieved by minimizing the error between experimental responses (speed or current) measured on a motor drive and the respective ones obtained by a simulation model based on the same control structure as the experimental rig, but with varying electrical parameters. An accurate and fast estimation of the electrical motor parameters is so achieved. Results are verified through a comparison of speed, torque and line current responses between the experimental IM drive and a Matlab-Simulink model.

Original languageEnglish
Title of host publication2008 IEEE Industry Applications Society Annual Meeting, IAS'08
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 IEEE Industry Applications Society Annual Meeting, IAS'08 - Edmonton, AB, Canada
Duration: 5 Oct 20089 Oct 2008

Publication series

NameConference Record - IAS Annual Meeting (IEEE Industry Applications Society)
ISSN (Print)0197-2618

Conference

Conference2008 IEEE Industry Applications Society Annual Meeting, IAS'08
Country/TerritoryCanada
CityEdmonton, AB
Period5/10/089/10/08

Keywords

  • Genetic algorithms
  • Induction motor drives
  • System identification
  • Vector control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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