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Title: Parameter estimation for VSI-fed PMSM based on a dynamic PSO with learning strategies
Authors: Liu, Zhao-Hua
Wei, Hua-Liang
Zhong, Qing-Chang
Liu, Kan
Xiao, Xiao-Shi
Wu, Liang-Hong
Keywords: Parameter estimation
Mathematical model
Voltage measurement
Heuristic algorithms
Issue Date: 2016
Publisher: © IEEE
Citation: LIU, Z.-H. ... et al, 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.
Abstract: 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 (OBL) 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.
Description: © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
Sponsor: This work was supported in part by the National Natural Science Foundation of China under Grant (51374107,61503134,51577057, 61573299, 61403134), the China Postdoctoral Science Foundation funded project under Grant (2013M540628, 2014T70767), and the Hunan Provincial Education Department outstanding youth project under Grant (15B087).
Version: Accepted for publication
DOI: 10.1109/TPEL.2016.2572186
URI: https://dspace.lboro.ac.uk/2134/22597
Publisher Link: http://dx.doi.org/10.1109/TPEL.2016.2572186
ISSN: 0885-8993
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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