Liu, Z.H., Wei, H. orcid.org/0000-0002-4704-7346, Zhong, Q.C. et al. (2 more authors) (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, 5 (3). pp. 1334-1345. ISSN 2168-6777
Abstract
In this paper, an accurate parameter estimation model of surface permanent magnet synchronous machines (SPMSMs) 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-DPSORE. 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 RE 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Institute of Electrical and Electronics Engineers. This is an author produced version of a paper subsequently published in IEEE Journal of Emerging and Selected Topics in Power Electronics. Uploaded in accordance with the publisher's self-archiving policy. |
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 |
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: | 31 Mar 2017 13:53 |
Last Modified: | 04 Jul 2018 14:42 |
Published Version: | https://doi.org/10.1109/JESTPE.2017.2690688 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/JESTPE.2017.2690688 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:114197 |