Principal Component Optimization with Mesh Adaptive Direct Search for Optimal Design of IPMSM

Myung Ki Seo; Tae Yong Lee; Jong Wook Kim; Yong Jae Kim; Sang Yong Jung

doi:10.1109/TMAG.2017.2665651

Principal Component Optimization with Mesh Adaptive Direct Search for Optimal Design of IPMSM

Myung Ki Seo, Tae Yong Lee, Jong Wook Kim, Yong Jae Kim, Sang Yong Jung

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

In this paper, a novel global search algorithm, implementing principal component optimization (PCO) in combination with mesh adaptive direct search (MADS) is proposed. The PCO is based on principal component analysis, a widely used multivariate technique in statistics, while MADS is a highly effective local search algorithm, implemented to find local optima. Their integration reduces computation time and improves convergence reliability to a global optimum. The proposed algorithm is verified via application of a test function and comparison to conventional hybrid algorithms such as particle swarm optimization with MADS. Finally, the algorithm is applied to the optimal design of an interior permanent magnet synchronous machine for minimization of total harmonic distortion and torque ripple via finite element analysis.

Original language	English
Article number	7847343
Journal	IEEE Transactions on Magnetics
Volume	53
Issue number	6
DOIs	https://doi.org/10.1109/TMAG.2017.2665651
State	Published - Jun 2017
Externally published	Yes

Keywords

AC machines
finite element analysis (FEA)
optimization methods
permanent magnet machine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/TMAG.2017.2665651

Cite this

@article{4f4bb528d2d44f23875fd2cb340ed3e1,

title = "Principal Component Optimization with Mesh Adaptive Direct Search for Optimal Design of IPMSM",

abstract = "In this paper, a novel global search algorithm, implementing principal component optimization (PCO) in combination with mesh adaptive direct search (MADS) is proposed. The PCO is based on principal component analysis, a widely used multivariate technique in statistics, while MADS is a highly effective local search algorithm, implemented to find local optima. Their integration reduces computation time and improves convergence reliability to a global optimum. The proposed algorithm is verified via application of a test function and comparison to conventional hybrid algorithms such as particle swarm optimization with MADS. Finally, the algorithm is applied to the optimal design of an interior permanent magnet synchronous machine for minimization of total harmonic distortion and torque ripple via finite element analysis.",

keywords = "AC machines, finite element analysis (FEA), optimization methods, permanent magnet machine",

author = "Seo, \{Myung Ki\} and Lee, \{Tae Yong\} and Kim, \{Jong Wook\} and Kim, \{Yong Jae\} and Jung, \{Sang Yong\}",

note = "Publisher Copyright: {\textcopyright} 1965-2012 IEEE.",

year = "2017",

month = jun,

doi = "10.1109/TMAG.2017.2665651",

language = "English",

volume = "53",

journal = "IEEE Transactions on Magnetics",

issn = "0018-9464",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "6",

}

TY - JOUR

T1 - Principal Component Optimization with Mesh Adaptive Direct Search for Optimal Design of IPMSM

AU - Seo, Myung Ki

AU - Lee, Tae Yong

AU - Kim, Jong Wook

AU - Kim, Yong Jae

AU - Jung, Sang Yong

PY - 2017/6

Y1 - 2017/6

N2 - In this paper, a novel global search algorithm, implementing principal component optimization (PCO) in combination with mesh adaptive direct search (MADS) is proposed. The PCO is based on principal component analysis, a widely used multivariate technique in statistics, while MADS is a highly effective local search algorithm, implemented to find local optima. Their integration reduces computation time and improves convergence reliability to a global optimum. The proposed algorithm is verified via application of a test function and comparison to conventional hybrid algorithms such as particle swarm optimization with MADS. Finally, the algorithm is applied to the optimal design of an interior permanent magnet synchronous machine for minimization of total harmonic distortion and torque ripple via finite element analysis.

AB - In this paper, a novel global search algorithm, implementing principal component optimization (PCO) in combination with mesh adaptive direct search (MADS) is proposed. The PCO is based on principal component analysis, a widely used multivariate technique in statistics, while MADS is a highly effective local search algorithm, implemented to find local optima. Their integration reduces computation time and improves convergence reliability to a global optimum. The proposed algorithm is verified via application of a test function and comparison to conventional hybrid algorithms such as particle swarm optimization with MADS. Finally, the algorithm is applied to the optimal design of an interior permanent magnet synchronous machine for minimization of total harmonic distortion and torque ripple via finite element analysis.

KW - AC machines

KW - finite element analysis (FEA)

KW - optimization methods

KW - permanent magnet machine

UR - https://www.scopus.com/pages/publications/85028774521

U2 - 10.1109/TMAG.2017.2665651

DO - 10.1109/TMAG.2017.2665651

M3 - Article

AN - SCOPUS:85028774521

SN - 0018-9464

VL - 53

JO - IEEE Transactions on Magnetics

JF - IEEE Transactions on Magnetics

IS - 6

M1 - 7847343

ER -

Principal Component Optimization with Mesh Adaptive Direct Search for Optimal Design of IPMSM

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this