Estimation of Iron Loss in Permanent Magnet Synchronous Motors Based on Particle Swarm Optimization and a Recurrent Neural Network

Xu, K; Guo, Y; Lei, G; Zhu, J

Estimation of Iron Loss in Permanent Magnet Synchronous Motors Based on Particle Swarm Optimization and a Recurrent Neural Network

Xu, K Guo, Y

Lei, G

Zhu, J

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Magnetism, 3, (4), pp. 327-342

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Field	Value	Language
dc.contributor.author	Xu, K
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Zhu, J
dc.date.accessioned	2024-05-03T03:32:41Z
dc.date.available	2024-05-03T03:32:41Z
dc.identifier.citation	Magnetism, 3, (4), pp. 327-342
dc.identifier.issn	2673-8724
dc.identifier.issn	2673-8724
dc.identifier.uri	http://hdl.handle.net/10453/178594
dc.description.abstract	<jats:p>The popularity of permanent magnet synchronous motors (PMSMs) has increased in recent years due to their high efficiency, compact size, and low maintenance needs. Calculating iron loss in PMSMs is crucial for designing and optimizing PMSMs to achieve high efficiency and a long lifespan, as this can significantly affect motor performance. However, multiple factors influence the accuracy of iron loss calculations in PMSMs, including the intricate magnetic behavior of the motor under different operating conditions, as well as the influence of the motor’s dynamic behavior during the operation process. This paper proposes a method based on particle swarm optimization (PSO) and a recurrent neural network (RNN) to estimate the iron loss in PMSMs, independent of the empirical iron loss formula. This method establishes an iron loss calculation model considering high-order harmonics, rotating magnetization, and temperature factors. Accounting for the multifactor influence, the model studies the law of loss change under different magnetic flux densities, frequencies, and temperature conditions. To avoid the deviation problem caused by conventional polynomial fitting, a multilayer RNN and PSO are used to train and optimize the neural network. Iron loss in complex cases beyond the measurement range can be accurately estimated. The proposed method helps achieve a PMSM iron loss calculation model with broad applicability and high accuracy.</jats:p>
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Magnetism
dc.relation.isbasedon	10.3390/magnetism3040025
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Estimation of Iron Loss in Permanent Magnet Synchronous Motors Based on Particle Swarm Optimization and a Recurrent Neural Network
dc.type	Journal Article
utslib.citation.volume	3
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	open_access	*
dc.date.updated	2024-05-03T03:32:37Z
pubs.issue	4
pubs.publication-status	Published online
pubs.volume	3
utslib.citation.issue	4

Abstract:

The popularity of permanent magnet synchronous motors (PMSMs) has increased in recent years due to their high efficiency, compact size, and low maintenance needs. Calculating iron loss in PMSMs is crucial for designing and optimizing PMSMs to achieve high efficiency and a long lifespan, as this can significantly affect motor performance. However, multiple factors influence the accuracy of iron loss calculations in PMSMs, including the intricate magnetic behavior of the motor under different operating conditions, as well as the influence of the motor’s dynamic behavior during the operation process. This paper proposes a method based on particle swarm optimization (PSO) and a recurrent neural network (RNN) to estimate the iron loss in PMSMs, independent of the empirical iron loss formula. This method establishes an iron loss calculation model considering high-order harmonics, rotating magnetization, and temperature factors. Accounting for the multifactor influence, the model studies the law of loss change under different magnetic flux densities, frequencies, and temperature conditions. To avoid the deviation problem caused by conventional polynomial fitting, a multilayer RNN and PSO are used to train and optimize the neural network. Iron loss in complex cases beyond the measurement range can be accurately estimated. The proposed method helps achieve a PMSM iron loss calculation model with broad applicability and high accuracy.

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http://hdl.handle.net/10453/178594