Induction motor parameters identification using genetic algorithms for varying flux levels

Konstantinos Kampisios, Pericle Zanchetta, Chris Gerada, Andrew Trentin, Omar Jasim

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

15 Citations (Scopus)

Abstract

This paper describes a novel approach for identifying induction motor electrical parameters in function of flux levels 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 and simulation model responses. An accurate and fast estimation of the electrical motor parameters is performed by running a number of optimizations using experimental tests taken under different operating conditions (flux level). 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 13th International Power Electronics and Motion Control Conference, EPE-PEMC 2008
Pages887-892
Number of pages6
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 13th International Power Electronics and Motion Control Conference, EPE-PEMC 2008 - Poznan, Poland
Duration: 1 Sep 20083 Sep 2008

Publication series

Name2008 13th International Power Electronics and Motion Control Conference, EPE-PEMC 2008

Conference

Conference2008 13th International Power Electronics and Motion Control Conference, EPE-PEMC 2008
Country/TerritoryPoland
CityPoznan
Period1/09/083/09/08

Keywords

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

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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