Robust Design Optimization of Electrical Machines Considering Hybrid Random and Interval Uncertainties

Ma, B; Zheng, J; Zhu, J; Wu, J; Lei, G; Guo, Y

Robust Design Optimization of Electrical Machines Considering Hybrid Random and Interval Uncertainties

Ma, B Zheng, J Zhu, J

Wu, J Lei, G

Guo, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Energy Conversion, 2020, 35, (4), pp. 1815-1824
Issue Date:: 2020-12-01

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Field	Value	Language
dc.contributor.author	Ma, B
dc.contributor.author	Zheng, J
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.contributor.author	Wu, J
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684
dc.date.accessioned	2021-03-18T01:11:33Z
dc.date.available	2021-03-18T01:11:33Z
dc.date.issued	2020-12-01
dc.identifier.citation	IEEE Transactions on Energy Conversion, 2020, 35, (4), pp. 1815-1824
dc.identifier.issn	0885-8969
dc.identifier.issn	1558-0059
dc.identifier.uri	http://hdl.handle.net/10453/147312
dc.description.abstract	© 1986-2012 IEEE. For robust design optimization (RDO) of electrical machines, cases with random uncertainty and interval uncertainty are generally investigated separately. Accordingly, the performance fluctuation analysis under uncertainty is based on random method and interval method respectively. However, the problem with hybrid uncertainties can also be met yet rarely researched. Under this circumstance, the uncertainty analysis methods for a single type of uncertainty may no longer be applicable, which challenges the robust optimization conduction. For effective RDO of electrical machines with hybrid uncertainties, this article presents a robust optimizer based on evolutionary algorithms and the polynomial chaos Chebyshev interval (PCCI) method. The PCCI method is utilized for effectively modeling the fluctuations caused by the hybrid uncertainty with a small number of samples. As additional enhancements, the filtering strategy for the algorithm with deterministic constraints is proposed to reduce the solutions that require robustness analysis in each iteration and accelerate the optimization further while not affecting the global convergence ability. A design example of a brushless DC motor considering hybrid uncertainties is analyzed and optimized. The results confirm the feasibility of the proposed method.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP0773858
dc.relation.ispartof	IEEE Transactions on Energy Conversion
dc.relation.hasversion	Accepted manuscript version	en
dc.relation.isbasedon	10.1109/TEC.2020.2996244
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
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 Considering Hybrid Random and Interval Uncertainties
dc.type	Journal Article
utslib.citation.volume	35
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	*
pubs.consider-herdc	false
utslib.copyright.embargo	2022-05-21T00:00:00+1000Z
dc.date.updated	2021-03-18T01:11:32Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	4

Abstract:

© 1986-2012 IEEE. For robust design optimization (RDO) of electrical machines, cases with random uncertainty and interval uncertainty are generally investigated separately. Accordingly, the performance fluctuation analysis under uncertainty is based on random method and interval method respectively. However, the problem with hybrid uncertainties can also be met yet rarely researched. Under this circumstance, the uncertainty analysis methods for a single type of uncertainty may no longer be applicable, which challenges the robust optimization conduction. For effective RDO of electrical machines with hybrid uncertainties, this article presents a robust optimizer based on evolutionary algorithms and the polynomial chaos Chebyshev interval (PCCI) method. The PCCI method is utilized for effectively modeling the fluctuations caused by the hybrid uncertainty with a small number of samples. As additional enhancements, the filtering strategy for the algorithm with deterministic constraints is proposed to reduce the solutions that require robustness analysis in each iteration and accelerate the optimization further while not affecting the global convergence ability. A design example of a brushless DC motor considering hybrid uncertainties is analyzed and optimized. The results confirm the feasibility of the proposed method.

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http://hdl.handle.net/10453/147312