Design optimization and comparative analysis of dual-stator flux modulation machines

Qingsong Wang, Shuangxia Niu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

This paper proposes three novel dual-stator flux-modulated permanent magnet (DSFMPM) machine concepts, which are particularly suitable for direct-drive applications with the virtue of their high torque density and low operation speed. The dual-stator configuration can help improve the use of inner cavity space, and achieve higher torque density comparing with the single-stator counterparts. Moreover, flux modulation is artfully employed to produce the gear effect, which can further benefit for the torque improvement. According to the PM location, the proposed DSFMPM machines are referred as (i) Stator-PM machine, (ii) Stator-rotor-PM machine, and (iii) Rotor-PM machine. Finite element method coupled with genetic algorithm, namely FEM-GA coupled method, is used to optimal design the proposed DSFMPM machines. Their electromagnetic performances are investigated in detail and quantitatively compared. The results show that the dual-stator topology can well improve the torque capability. Among all the proposed DSFMPM machines, the stator-PM one owns the lowest torque density because it has more short-circuit leakage flux.
Original languageEnglish
Title of host publicationProceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
Pages3719-3724
Number of pages6
Volume2017-January
ISBN (Electronic)9781538611272
DOIs
Publication statusPublished - 15 Dec 2017
Event43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017 - China National Convention Center, Beijing, China
Duration: 29 Oct 20171 Nov 2017

Conference

Conference43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017
Country/TerritoryChina
CityBeijing
Period29/10/171/11/17

Keywords

  • Dual-stator
  • flux modulation
  • genetic algorithm
  • optimal design

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Optimization
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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