Global and local meta-models for the robust design of electrical machines

Issue title: Selected Papers from ISEF 2015

Guest editors: Paolo Di Barba and Sƚawomir Wiak

Article type: Research Article

Authors: Li, Min^* | Silva, Rodrigo | Lowther, David

Affiliations: Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada

Correspondence: [*] Corresponding author: Min Li, Room 630, McConnell Engineering Building, 3840 University Street, H3A 2A7, Montreal, QC, Canada. Tel.: +1 514 398 1533; Fax: +1 154 398 4470; E-mail:min.li@mail.mcgill.ca

Abstract: This paper presents an optimization framework for the robust design of electrical machines. During the run of a genetic algorithm, all evaluated solutions are kept in an archive, and are used to build different meta-models for the assessment of robustness. The proposed robust design algorithms are verified using analytical test functions and are shown to be efficient and accurate. They have also been applied to the robust design of a surface mount permanent magnet machine considering manufacturing imprecision and different optimal designs (robust and non-robust) are obtained.

Keywords: Evolutionary algorithm, meta-modeling, robust design

DOI: 10.3233/JAE-2013

Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 51, no. s1, pp. S89-S95, 2016