Injection based sensorless performance optimization of surface mounted permanent magnet motor using Particle Swarm

M. Caner, C. Gerada

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

Abstract

This paper shows the results of an intention to design of surface mounted permanent magnet (SMPM) machines with self-sensing capabilities determining design parameters by Particle Swarm Optimization (PSO). A methodology will be presented which will look at the use of PSO to close up the torque to the highest values as possible and to maximize the self-sensing properties of such machines. A PSO environment has been combined with a finite element analysis (FEA) environment to enable the designer to account for both geometrical and saturation saliencies for an effective determination of the machine's self-sensing characteristics. The results obtained are satisfactory in terms of torque maximization and self-sensing capability. The sensitivity of the major geometrical parameters of the machine investigated, as well.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages107-112
Number of pages6
ISBN (Electronic)9781509058532
DOIs
Publication statusPublished - 13 Jun 2017
Event2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2017 - Nottingham, United Kingdom
Duration: 20 Apr 201721 Apr 2017

Publication series

NameProceedings - 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2017

Conference

Conference2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2017
Country/TerritoryUnited Kingdom
CityNottingham
Period20/04/1721/04/17

Keywords

  • Design optimization
  • Particle swarm method
  • Sensorless control performance
  • Surface mounted permanent magnet machine

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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