Sun, T., Wang, J. orcid.org/0000-0003-4870-3744 and Koc, M. (2017) Self-learning Direct Flux Vector Control of Interior Permanent Magnet Machine Drives. IEEE Transactions on Power Electronics, 32 (6). pp. 4652-4662. ISSN 0885-8993
Abstract
This paper proposes a novel self-learning control scheme for interior permanent magnet synchronous machine (IPMSM) drives to achieve maximum torque per ampere (MTPA) operation in constant torque region and voltage constraint maximum torque per ampere (VCMTPA) operation in field weakening region. The proposed self-learning control scheme (SLC) is based on the newly reported virtual signal injection aided direct flux vector control. However, other searching based optimal control schemes in the flux-torque (f-t) reference frame are also possible. Initially the reference flux amplitudes for MTPA operations are tracked by virtual signal injection and the data are used by the proposed self-learning control scheme to train the reference flux map online. After training, the proposed control scheme generates the optimal reference flux amplitude with fast dynamic response. The proposed control scheme can achieve MTPA or VCMTPA control fast and accurately without accurate prior knowledge of machine parameters and can adapt to machine parameter changes during operation. The proposed control scheme is verified by experiments under various operation conditions on a prototype 10 kW IPMSM drive.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 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: | Signal injection; Maximum torque per Ampere (MTPA) operation; Permanent magnet synchronous machine (IPMSM); Self-learning control |
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) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/K034987/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Dec 2016 14:42 |
Last Modified: | 21 Mar 2018 12:38 |
Published Version: | https://doi.org/10.1109/TPEL.2016.2602243 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TPEL.2016.2602243 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109770 |