A hybrid Jiles–Atherton and prisach model of dynamic magnetic hysteresis based on backpropagation neural networks

Li, Y; Zhu, J; Li, Y; Zhu, L

A hybrid Jiles–Atherton and prisach model of dynamic magnetic hysteresis based on backpropagation neural networks

Li, Y Zhu, J

Li, Y Zhu, L

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Journal of Magnetism and Magnetic Materials, 2022, 544
Issue Date:: 2022-02-15

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Field	Value	Language
dc.contributor.author	Li, Y
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.contributor.author	Li, Y
dc.contributor.author	Zhu, L
dc.date.accessioned	2023-03-17T01:42:53Z
dc.date.available	2023-03-17T01:42:53Z
dc.date.issued	2022-02-15
dc.identifier.citation	Journal of Magnetism and Magnetic Materials, 2022, 544
dc.identifier.issn	0304-8853
dc.identifier.issn	1873-4766
dc.identifier.uri	http://hdl.handle.net/10453/167408
dc.description.abstract	Ferromagnetic materials are widely used for magnetic cores in electromagnetic devices such as inductors, transformers, generators, and motors. In a magnetic core, as the magnetic field varies with time, core loss is generated due to magnetic hysteresis and eddy currents. For performance analysis and design optimization of electromagnetic devices, it is essential to model the dynamic magnetization processes and associated core losses accurately. This paper proposes a hybrid model of dynamic magnetic hysteresis, which incorporates the effects of both hysteresis and eddy currents, by combining the dynamic Jiles-Atherton and Preisach models based on backpropagation neural networks. This model can accurately reproduce the dynamic hysteresis loops and core losses under different excitations. The numerical simulations are verified by experimental measurements.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Journal of Magnetism and Magnetic Materials
dc.relation.isbasedon	10.1016/j.jmmm.2021.168655
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0204 Condensed Matter Physics, 0912 Materials Engineering, 0913 Mechanical Engineering
dc.subject.classification	Applied Physics
dc.title	A hybrid Jiles–Atherton and prisach model of dynamic magnetic hysteresis based on backpropagation neural networks
dc.type	Journal Article
utslib.citation.volume	544
utslib.for	0204 Condensed Matter Physics
utslib.for	0912 Materials Engineering
utslib.for	0913 Mechanical 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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-17T01:42:51Z
pubs.publication-status	Published
pubs.volume	544

Abstract:

Ferromagnetic materials are widely used for magnetic cores in electromagnetic devices such as inductors, transformers, generators, and motors. In a magnetic core, as the magnetic field varies with time, core loss is generated due to magnetic hysteresis and eddy currents. For performance analysis and design optimization of electromagnetic devices, it is essential to model the dynamic magnetization processes and associated core losses accurately. This paper proposes a hybrid model of dynamic magnetic hysteresis, which incorporates the effects of both hysteresis and eddy currents, by combining the dynamic Jiles-Atherton and Preisach models based on backpropagation neural networks. This model can accurately reproduce the dynamic hysteresis loops and core losses under different excitations. The numerical simulations are verified by experimental measurements.

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http://hdl.handle.net/10453/167408