Non-probabilistic Reliability-based Robust Design Optimization of Electrical Machines Considering Interval Uncertainties

Zheng, J; Sun, K; Ma, B; Zhu, J; Lei, G

Non-probabilistic Reliability-based Robust Design Optimization of Electrical Machines Considering Interval Uncertainties

Zheng, J Sun, K Ma, B Zhu, J

Lei, G

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: CEFC 2022 - 20th Biennial IEEE Conference on Electromagnetic Field Computation, Proceedings, 2022, 00, pp. 1-2
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Zheng, J
dc.contributor.author	Sun, K
dc.contributor.author	Ma, B
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.date	2022-10-24
dc.date.accessioned	2023-04-13T03:50:15Z
dc.date.available	2023-04-13T03:50:15Z
dc.date.issued	2022-01-01
dc.identifier.citation	CEFC 2022 - 20th Biennial IEEE Conference on Electromagnetic Field Computation, Proceedings, 2022, 00, pp. 1-2
dc.identifier.isbn	9781665468336
dc.identifier.uri	http://hdl.handle.net/10453/169751
dc.description.abstract	An efficient non-probabilistic reliability-based robust optimization method (NRBRDO) is proposed by incorporating the decoupling strategy and interval analysis approach for the electromagnetic machines with interval uncertainties. The double-loop NRBRDO problem is decoupled into a series of sequential loops, including the robust optimization based on the Chebyshev polynomial expansion and the reliability analysis based on the performance measure approach. The result of a benchmark design optimization problem of blushless direct current wheel motor is presented. Furthermore, the optimization results are compared with the deterministic optimization and the robust optimization to verify the developed method.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	CEFC 2022 - 20th Biennial IEEE Conference on Electromagnetic Field Computation, Proceedings
dc.relation.ispartof	2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC)
dc.relation.isbasedon	10.1109/CEFC55061.2022.9940770
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Non-probabilistic Reliability-based Robust Design Optimization of Electrical Machines Considering Interval Uncertainties
dc.type	Conference Proceeding
utslib.citation.volume	00
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-13T03:50:14Z
pubs.finish-date	2022-10-26
pubs.publication-status	Published
pubs.start-date	2022-10-24
pubs.volume	00

Abstract:

An efficient non-probabilistic reliability-based robust optimization method (NRBRDO) is proposed by incorporating the decoupling strategy and interval analysis approach for the electromagnetic machines with interval uncertainties. The double-loop NRBRDO problem is decoupled into a series of sequential loops, including the robust optimization based on the Chebyshev polynomial expansion and the reliability analysis based on the performance measure approach. The result of a benchmark design optimization problem of blushless direct current wheel motor is presented. Furthermore, the optimization results are compared with the deterministic optimization and the robust optimization to verify the developed method.

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http://hdl.handle.net/10453/169751