Calculation of Iron Loss in Permanent Magnet Synchronous Motors Based on PSO-RNN

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

Calculation of Iron Loss in Permanent Magnet Synchronous Motors Based on PSO-RNN

Xu, K Guo, Y

Lei, G

Liu, L Zhu, J

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023, 00, pp. 1-2
Issue Date:: 2023-01-01

Embargoed

	Filename	Description	Size
	2-s2.0-85172729386 AM.pdf	Accepted version	3.1 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Embargoed
Open Access

This item is currently unavailable due to the publisher's embargo.

The embargo period expires on 19 May 2025

Full metadata record

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	Liu, L
dc.contributor.author	Zhu, J
dc.date	2023-05-15
dc.date.accessioned	2024-04-19T09:40:29Z
dc.date.available	2024-04-19T09:40:29Z
dc.date.issued	2023-01-01
dc.identifier.citation	2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023, 00, pp. 1-2
dc.identifier.isbn	9798350338362
dc.identifier.uri	http://hdl.handle.net/10453/178101
dc.description.abstract	This paper proposes a Particle Swarm Optimization (PSO) Recursive Neural Network (RNN) method independent of the empirical iron loss formula to analyze the iron loss in a permanent magnet synchronous motor (PMSM). This model establishes an iron loss calculation model considering high-order harmonic, rotating magnetization, and temperature factors. Considering the influence of multi-factors, the model studies the law of loss change under different magnetic flux densities, frequencies, and temperature conditions. To avoid the deviation problem caused by traditional polynomial fitting, the multi-layer RNN and PSO are used to train and optimize the neural network. Using the machine learning method, the iron loss model versus input parameters is established. The iron loss in complex cases beyond the measurement range can be estimated accurately. The proposed method helps achieve a PMSM iron loss calculation model with broad applicability and high accuracy.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers)
dc.relation.ispartof	2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers)
dc.relation.isbasedon	10.1109/intermagshortpapers58606.2023.10228355
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Calculation of Iron Loss in Permanent Magnet Synchronous Motors Based on PSO-RNN
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	embargoed	*
utslib.copyright.embargo	2025-05-19T00:00:00+1000Z
dc.date.updated	2024-04-19T09:40:25Z
pubs.finish-date	2023-05-19
pubs.publication-status	Published
pubs.start-date	2023-05-15
pubs.volume	00

Abstract:

This paper proposes a Particle Swarm Optimization (PSO) Recursive Neural Network (RNN) method independent of the empirical iron loss formula to analyze the iron loss in a permanent magnet synchronous motor (PMSM). This model establishes an iron loss calculation model considering high-order harmonic, rotating magnetization, and temperature factors. Considering the influence of multi-factors, the model studies the law of loss change under different magnetic flux densities, frequencies, and temperature conditions. To avoid the deviation problem caused by traditional polynomial fitting, the multi-layer RNN and PSO are used to train and optimize the neural network. Using the machine learning method, the iron loss model versus input parameters is established. The iron loss in complex cases beyond the measurement range can be estimated accurately. The proposed method helps achieve a PMSM iron loss calculation model with broad applicability and high accuracy.

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

http://hdl.handle.net/10453/178101