NDLI: Design optimization of permanent magnet motors using response surface methodology and genetic algorithms

Content Provider	IEEE Xplore Digital Library
Author	Jolly, L. Jabbar, M.A. Liu, Q.
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore (Jolly, L.; Jabbar, M.A.)
Abstract	Finite element method (FEM) reveals to be one of the most valuable tools in electric machines design as it accurately takes into account non-linear materials and complex geometry. However, this is by nature an analysis tool, which makes the design procedure very iterative, hence time consuming. Furthermore, the final result is quite uncertain. This main drawback can be eliminated by combining FEM with response surface method (RSM). RSM is a statistical tool to derive experimentally an analytical relationship between a response and some input variables. It is particularly suitable when the underlying phenomenon linking them is not well known or too complex to be modeled mathematically. The relationship is derived by regression from a small number of observations of the response that can be provided by FEM. Genetic algorithm (GA) can then worked directly on this model to optimize the device with a decrease in computational cost. This paper presents the combination of FEM, RSM and GA to optimize the rotor geometry of an interior permanent magnet synchronous motor for fast and accurate design procedure.
Sponsorship	Magnetics Soc. of Japan Magnetics Soc. of the IEEE
Starting Page	91
Ending Page	92
File Size	185417
Page Count	2
File Format	PDF
ISBN	0780390091
DOI	10.1109/INTMAG.2005.1463474
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-04-04
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Design optimization Permanent magnet motors Response surface methodology Genetic algorithms Geometry Finite element methods Electric machines Magnetic materials Magnetic analysis Input variables
Content Type	Text
Resource Type	Article

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