Detail publikace

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

KNEBL, L.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

KNEBL, L.

Vydáno

27. 4. 2021

Nakladatel

Brno University of Technology, Faulty of Electrical Engineering and Communication

Místo

Brno

ISBN

978-80-214-5943-4

Kniha

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

Edice

1

Strany od

235

Strany do

239

Strany počet

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"
}