Multi-Objective Robust Optimization of a Dual-Flux-Modulator Magnetic Geared Machine with Hybrid Uncertainties

Liu, X; Zhao, Y; Zhu, J; Chen, Z; Huang, S

Multi-Objective Robust Optimization of a Dual-Flux-Modulator Magnetic Geared Machine with Hybrid Uncertainties

Liu, X Zhao, Y Zhu, J

Chen, Z Huang, S

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Energy Conversion, 2020, 35, (4), pp. 2106-2115
Issue Date:: 2020-12-01

Closed Access

	Filename	Description	Size
	09120178.pdf	Published version	4.91 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, X
dc.contributor.author	Zhao, Y
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.contributor.author	Chen, Z
dc.contributor.author	Huang, S
dc.date.accessioned	2021-03-08T20:19:50Z
dc.date.available	2021-03-08T20:19:50Z
dc.date.issued	2020-12-01
dc.identifier.citation	IEEE Transactions on Energy Conversion, 2020, 35, (4), pp. 2106-2115
dc.identifier.issn	0885-8969
dc.identifier.issn	1558-0059
dc.identifier.uri	http://hdl.handle.net/10453/146936
dc.description.abstract	© 1986-2012 IEEE. In order to improve the robustness and torque performance of a dual-flux-modulator magnetic-geared machine (DFM-MGM) considering simultaneously both random and interval uncertainties of design parameters, a multi-objective robust optimization (MORO) method with multiple Monte Carlo simulations (MCSs) is proposed. In this method, the multiple MCSs are adopted to evaluate the effects of parametric hybrid uncertainties on the robustness of optimization results. To build a MORO model of the DFM-MGM, the three-dimensional finite element model is established firstly and then validated by the experiment. Through a parametric study, it is found that five dimensional parameters of the permanent magnets (PMs) and stator have more significant effects on the stall torque (ST) and ST per PM volume (STPPV). Finally, a multi-objective particle swarm optimization algorithm with surrogate models, sigma criteria design and multiple MCSs method is implemented to solve the MORO problem. Both the average standard deviations and standard deviation differences of the ST and STPPV are used to deal with hybrid uncertainties during MORO. The optimized DFM-MGM by MORO has a STPPV 6.3% higher than that of the initial design under the same ST constraint. Moreover, the average standard deviations and standard deviation differences obtained by MORO are much smaller than those achieved by the deterministic optimization, indicating that the robustness of optimal results can also be significantly improved by the MORO.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Energy Conversion
dc.relation.isbasedon	10.1109/TEC.2020.3003402
dc.rights	info:eu-repo/semantics/closedAccess
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.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Multi-Objective Robust Optimization of a Dual-Flux-Modulator Magnetic Geared Machine with Hybrid Uncertainties
dc.type	Journal Article
utslib.citation.volume	35
utslib.for	0906 Electrical and Electronic Engineering
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-08T20:19:46Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	4

Abstract:

© 1986-2012 IEEE. In order to improve the robustness and torque performance of a dual-flux-modulator magnetic-geared machine (DFM-MGM) considering simultaneously both random and interval uncertainties of design parameters, a multi-objective robust optimization (MORO) method with multiple Monte Carlo simulations (MCSs) is proposed. In this method, the multiple MCSs are adopted to evaluate the effects of parametric hybrid uncertainties on the robustness of optimization results. To build a MORO model of the DFM-MGM, the three-dimensional finite element model is established firstly and then validated by the experiment. Through a parametric study, it is found that five dimensional parameters of the permanent magnets (PMs) and stator have more significant effects on the stall torque (ST) and ST per PM volume (STPPV). Finally, a multi-objective particle swarm optimization algorithm with surrogate models, sigma criteria design and multiple MCSs method is implemented to solve the MORO problem. Both the average standard deviations and standard deviation differences of the ST and STPPV are used to deal with hybrid uncertainties during MORO. The optimized DFM-MGM by MORO has a STPPV 6.3% higher than that of the initial design under the same ST constraint. Moreover, the average standard deviations and standard deviation differences obtained by MORO are much smaller than those achieved by the deterministic optimization, indicating that the robustness of optimal results can also be significantly improved by the MORO.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/146936