Wu, L.J., Li, Z., Wang, D. et al. (3 more authors) (2019) On-load field prediction of surface-mounted PM machines considering nonlinearity based on hybrid field model. IEEE Transactions on Magnetics, 55 (3). 8100911. ISSN 0018-9464
Abstract
Analytical models show weakness in dealing with saturation in surface-mounted permanent-magnet machines. A hybrid field model (HFM) integrating complex permeance method (CPM) and lumped parameter magnetic circuit model (LPMCM) is proposed in this paper for predicting the on-load magnetic field considering nonlinearity effect of stator lamination. In the proposed model, the CPM calculates the field in the air gap and magnet regions, while LPMCM calculates the magnetic potential distribution inside the iron reflecting nonlinearity effect. The equivalent current sheet is obtained to replace such distribution on the stator bore. Moreover, local magnetic saturation of tooth tip is also transformed into equivalent current on the tooth surface. A solving procedure is proposed to calculate the equivalent current and guarantee the convergence. Compared with CPM, the proposed model considering the saturation effect significantly improves the prediction accuracy of the on-load performance. The HFM predictions are compared with finite element and experimental results. The excellent agreement validates its effectiveness.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 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: | Analytical model; complex permeance; magnetic equivalent circuit; saturation effect; surface-mounted permanentmagnet (SPM) machines |
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: | 31 May 2019 09:43 |
Last Modified: | 31 Jan 2020 01:39 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/TMAG.2018.2890244 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146797 |