Novel optimisation algorithm of electrical machines

Zheng Tan, N.J. Baker, Wenping Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a case study for multi-variable and multi-modal design optimisation of a doubly fed induction generator (DFIG) based on surrogate-model optimisation algorithm. The DFIG's winding of stator and rotor are optimised to obtain higher efficiency for rewinding purposes. First, a Latin hypercube design is selected as the design of experiments to obtain sampling points. Then, the surrogate model is constructed using Kriging Model (KRG) method based on the Latin hypercube design. Finally, the particle swarm optimisation algorithm is applied in conjunction with the finite element method to achieve the machine design optimisation.

Original languageEnglish
Title of host publication8th IET International Conference on Power Electronics, Machines and Drives (PEMD 2016), 2016
PublisherIET
ISBN (Print)978-1-78561-188-9
DOIs
Publication statusPublished - 10 Nov 2016
Event8th IET International Conference on Power Electronics, Machines and Drives - Glasgow, United Kingdom
Duration: 19 Apr 201621 Apr 2016

Conference

Conference8th IET International Conference on Power Electronics, Machines and Drives
Abbreviated titlePEMD 2016
CountryUnited Kingdom
CityGlasgow
Period19/04/1621/04/16

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Bibliographical note

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Keywords

  • DFIG
  • Kriging model
  • particle swarm optimisation

Cite this

Tan, Z., Baker, N. J., & Cao, W. (2016). Novel optimisation algorithm of electrical machines. In 8th IET International Conference on Power Electronics, Machines and Drives (PEMD 2016), 2016 IET. https://doi.org/10.1049/cp.2016.0323