Robust Design Optimization of Electrical Machines: Multi-Objective Approach

Lei, G; Bramerdorfer, G; Ma, B; Guo, Y; Zhu, J

Robust Design Optimization of Electrical Machines: Multi-Objective Approach

Lei, G

Bramerdorfer, G Ma, B Guo, Y

Zhu, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Energy Conversion, 2021, 36, (1), pp. 390-401
Issue Date:: 2021-03-01

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Field	Value	Language
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Bramerdorfer, G
dc.contributor.author	Ma, B
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.date.accessioned	2022-01-30T09:48:35Z
dc.date.available	2022-01-30T09:48:35Z
dc.date.issued	2021-03-01
dc.identifier.citation	IEEE Transactions on Energy Conversion, 2021, 36, (1), pp. 390-401
dc.identifier.issn	0885-8969
dc.identifier.issn	1558-0059
dc.identifier.uri	http://hdl.handle.net/10453/153874
dc.description.abstract	This article presents a new method for multi-objective robust design optimization of electrical machines and provides a detailed comparison with so far introduced techniques. First, two robust design approaches, worst-case design and design for six-sigma, are compared with the conventional deterministic approach for multi-objective optimization. Through a case study on a permanent magnet motor, it is found that the reliabilities of motors produced based on robust designs are 100% under the investigated constraints, while the reliabilities of deterministic designs can be lower than 30%. A major disadvantage of robust optimization is the huge computation cost, especially for high-dimensional problems. To attempt this problem, a new multi-objective sequential optimization method (MSOM) with an orthogonal design technique and hypervolume indicator (as a measure of convergence) is proposed for both deterministic and robust design optimization of electrical machines. Through another case study, it is found that the new MSOM can improve motor performance and greatly reduce the computational cost. For the robust optimization, the number of required finite element simulations can be reduced by more than 40%, compared with that required by the conventional approach. The proposed method can be applied to many-objective (robust) design optimization of electrical machines.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP0773858
dc.relation	http://purl.org/au-research/grants/arc/LP0454306
dc.relation.ispartof	IEEE Transactions on Energy Conversion
dc.relation.isbasedon	10.1109/TEC.2020.3003050
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Robust Design Optimization of Electrical Machines: Multi-Objective Approach
dc.type	Journal Article
utslib.citation.volume	36
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-03-31T00:00:00+1000Z
dc.date.updated	2022-01-30T09:48:18Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	36
utslib.citation.issue	1

Abstract:

This article presents a new method for multi-objective robust design optimization of electrical machines and provides a detailed comparison with so far introduced techniques. First, two robust design approaches, worst-case design and design for six-sigma, are compared with the conventional deterministic approach for multi-objective optimization. Through a case study on a permanent magnet motor, it is found that the reliabilities of motors produced based on robust designs are 100% under the investigated constraints, while the reliabilities of deterministic designs can be lower than 30%. A major disadvantage of robust optimization is the huge computation cost, especially for high-dimensional problems. To attempt this problem, a new multi-objective sequential optimization method (MSOM) with an orthogonal design technique and hypervolume indicator (as a measure of convergence) is proposed for both deterministic and robust design optimization of electrical machines. Through another case study, it is found that the new MSOM can improve motor performance and greatly reduce the computational cost. For the robust optimization, the number of required finite element simulations can be reduced by more than 40%, compared with that required by the conventional approach. The proposed method can be applied to many-objective (robust) design optimization of electrical machines.

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http://hdl.handle.net/10453/153874