Dynamic multilevel optimization of machine design and control parameters based on correlation analysis

Meng, X; Wang, S; Qiu, J; Zhu, JG; Wang, Y; Guo, Y; Liu, D; Xu, W

Dynamic multilevel optimization of machine design and control parameters based on correlation analysis

Meng, X Wang, S Qiu, J Zhu, JG

Wang, Y Guo, Y

Liu, D

Xu, W

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Magnetics, 2010, 46 (8), pp. 2779 - 2782
Issue Date:: 2010-08-01

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Field	Value	Language
dc.contributor.author	Meng, X	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Qiu, J	en_US
dc.contributor.author	Zhu, JG https://orcid.org/0000-0002-9763-4047	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684	en_US
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Xu, W	en_US
dc.date.issued	2010-08-01	en_US
dc.identifier.citation	IEEE Transactions on Magnetics, 2010, 46 (8), pp. 2779 - 2782	en_US
dc.identifier.issn	0018-9464	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13685
dc.description.abstract	In this paper, a multilevel optimization method is proposed for a motor drive system including a surface-mounted permanent magnet synchronous machine (SPMSM), the converter/inverter, and the control schemes. First, the multilevel optimization is described by using the problem matrix which may be used to allocate the design variables on different levels. The parameters in the problem matrix are deduced by using correlation analysis. Second, the architecture and implementation of multilevel genetic algorithm (MLGA) are carried out. As one of the advantages of MLGA, the dynamic adjustment strategy of GA operators is utilized to improve the optimal performance. The algorithm is then applied to a three-level optimization problem in which the optimization of SPMSM design and the control parameters of drive are considered in different levels. Finally, some results and discussions about the application of the proposed algorithm are presented. © 2006 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Magnetics	en_US
dc.relation.isbasedon	10.1109/TMAG.2010.2044773	en_US
dc.subject.classification	Applied Physics	en_US
dc.title	Dynamic multilevel optimization of machine design and control parameters based on correlation analysis	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	46	en_US
utslib.for	0999 Other Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	02 Physical Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
utslib.copyright.status	open_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	46	en_US

Abstract:

In this paper, a multilevel optimization method is proposed for a motor drive system including a surface-mounted permanent magnet synchronous machine (SPMSM), the converter/inverter, and the control schemes. First, the multilevel optimization is described by using the problem matrix which may be used to allocate the design variables on different levels. The parameters in the problem matrix are deduced by using correlation analysis. Second, the architecture and implementation of multilevel genetic algorithm (MLGA) are carried out. As one of the advantages of MLGA, the dynamic adjustment strategy of GA operators is utilized to improve the optimal performance. The algorithm is then applied to a three-level optimization problem in which the optimization of SPMSM design and the control parameters of drive are considered in different levels. Finally, some results and discussions about the application of the proposed algorithm are presented. © 2006 IEEE.

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http://hdl.handle.net/10453/13685