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Title: GPU implementation of DPSO-RE algorithm for parameters identification of surface PMSM considering VSI nonlinearity
Authors: Liu, Zhao-Hua
Wei, Hua-Liang
Zhong, Qing-Chang
Liu, Kan
Li, Xiao-Hua
Keywords: Particle swarm optimization (PSO)
Artificial immune system (AIS)
Graphics Processing Unit (GPU)
Parallel computing
Parameter estimation
Permanent magnet synchronous machines (PMSMs)
Voltage-source-inverter (VSI)
Nonlinearity
Issue Date: 2017
Publisher: © IEEE
Citation: LIU, Z-H. ...et al., 2017. GPU implementation of DPSO-RE algorithm for parameters identification of surface PMSM considering VSI nonlinearity. IEEE Journal of Emerging and Selected Topics in Power Electronics, In Press.
Abstract: In this study, an accurate parameter estimation model of surface permanent magnet synchronous machines (SPMSM) is established by taking into account voltage-source-inverter (VSI) nonlinearity. A fast dynamic particle swarm optimization (DPSO) algorithm combined with a receptor editing (RE) strategy is proposed to explore the optimal values of parameter estimations. This combination provides an accelerated implementation on graphics processing unit (GPU), and the proposed method is therefore referred to as G-DPSO-RE. In G-DPSO-RE, a dynamic labor division strategy is incorporated into the swarms according to the designed evolutionary factor during the evolution process. Two novel modifications of the movement equation are designed to update the velocity of particles. Moreover, a chaotic-logistic based immune receptor editing operator is developed to facilitate the global best individual (gBest particle) to explore a potentially better region. Furthermore, a GPU parallel acceleration technique is utilized to speed up parameter estimation procedure. It has been demonstrated that the proposed method is effective for simultaneous estimation of the PMSM parameters and the disturbance voltage (Vdead) due to VSI nonlinearity from experimental data for currents and rotor speed measured with inexpensive equipment. The influence of the VSI nonlinearity on the accuracy of parameter estimation is analyzed.
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 (61503134, 61573299, 51374107, 51577057), the Hunan Provincial Education Department outstanding youth project under Grant (15B087),and the China Postdoctoral Science Foundation funded project under Grant (2013M540628, 2014T70767).
Version: Accepted for publication
DOI: 10.1109/JESTPE.2017.2690688
URI: https://dspace.lboro.ac.uk/2134/25085
Publisher Link: http://dx.doi.org/10.1109/JESTPE.2017.2690688
ISSN: 2168-6777
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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