Publication detail

Preference based and ideal multi-objective optimization applied on a high-torque ferrite assisted synchronous reluctance machine

KNEBL, L.

Original Title

Preference based and ideal multi-objective optimization applied on a high-torque ferrite assisted synchronous reluctance machine

Type

conference paper

Language

English

Original Abstract

This paper introduces comparison of optimization algorithms applied on high-torque ferrite-assisted synchronous reluctance machine. The comparison is focused not solely on two algorithms within the same multi-objective optimization approach - preference based or ideal, but also on comparison of these two approaches. The genetic algorithm and self-organizing migrating algorithm in both approaches are used to find optimal solution. The optimiation goal is an optimal parameter combination to achieve the highest torque and power factor, while developing the lowest torque ripple. The optimized design will be evaluateed by the 2D finite element analysis in steady-state analysis.

Keywords

steady-state, synchronous reluctance motor, finite element analysis, optimization

Authors

KNEBL, L.

Released

27. 4. 2021

Publisher

Brno University of Technology, Faulty of Electrical Engineering and Communication

Location

Brno

ISBN

978-80-214-5943-4

Book

Proceeding II of the 27th Converence Student EEICT 2021 Selected papers

Edition

1

Pages from

235

Pages to

239

Pages count

5

URL

https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_2.pdf

BibTex

@inproceedings{BUT176403,
  author="Ladislav {Knebl}",
  title="Preference based and ideal multi-objective optimization applied on a high-torque ferrite assisted synchronous reluctance machine",
  booktitle="Proceeding II of the 27th Converence Student EEICT 2021 Selected papers",
  year="2021",
  series="1",
  pages="235--239",
  publisher="Brno University of Technology, Faulty of Electrical Engineering and Communication",
  address="Brno",
  doi="10.13164/eeict.2021.235",
  isbn="978-80-214-5943-4",
  url="https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_2.pdf"
}