Liu, Z.H., Wei, H.L. orcid.org/0000-0002-4704-7346, Zhong, Q.C. et al. (3 more authors) (2016) Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies. IEEE Transactions on Power Electronics, 32 (4). pp. 3154-3165. ISSN 0885-8993
Abstract
© 1986-2012 IEEE.A dynamic particle swarm optimization with learning strategy (DPSO-LS) is proposed for key parameter estimation for permanent magnet synchronous machines (PMSMs), where the voltage-source inverter (VSI) nonlinearities are taken into account in the parameter estimation model and can be estimated simultaneously with other machine parameters. In the DPSO-LS algorithm, a novel movement modification equation with variable exploration vector is designed to effectively update particles, enabling swarms to cover large areas of search space with large probability and thus the global search ability is enhanced. Moreover, a Gaussian-distribution-based dynamic opposition-based learning strategy is developed to help the pBest jump out local optima. The proposed DPSO-LS can significantly enhance the estimator model accuracy and dynamic performance. Finally, the proposed algorithm is applied to multiple parameter estimation including the VSI nonlinearities of a PMSM. The performance of DPSO-LS is compared with several existing PSO algorithms, and the comparison results show that the proposed parameters estimation method has better performance in tracking the variation of machine parameters effectively and estimating the VSI nonlinearities under different operation conditions.
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 |
Keywords: | particle swarm optimization (PSO); dynamic; opposition-based learning (OBL); parameter identification; voltage source inverter (VSI) nonlinearity; permanent magnet synchronous machines (PMSMs). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Mar 2017 11:04 |
Last Modified: | 22 Mar 2018 06:39 |
Published Version: | https://doi.org/10.1109/TPEL.2016.2572186 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TPEL.2016.2572186 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:113033 |