Zhu, Z-Q. orcid.org/0000-0001-7175-3307, Xue, S. orcid.org/0000-0003-4534-3878, Chu, W. et al. (4 more authors) (2019) Evaluation of Iron Loss Models in Electrical Machines. IEEE Transactions on Industry Applications, 55 (2). pp. 1461-1472. ISSN 0093-9994
Abstract
In this paper, more than 10 different iron loss models are experimentally evaluated, which cover alternating and rotating fields, the influence of temperature, dc bias flux density and distorted flux density due to pulsewidth modulation inverter. Iron loss models considering alternating fields are evaluated by the measured results of a lamination ring specimen. The iron loss model considering the rotating field and the nonsinusoidal field are evaluated by the measured results of an electrical machine under different conditions. The iron loss models considering temperature influence are also evaluated by thermal analyses and experimental tests. Based on these comprehensive investigations, the iron loss models having the best prediction accuracy for each case are identified.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Electrical machine; iron loss; thermal analysis |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 May 2019 09:43 |
Last Modified: | 12 Nov 2019 01:39 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/TIA.2018.2880674 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146185 |