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Modulated-Virtual-Vector-Based Predictive Current Control for Dual Three-Phase PMSM With Enhanced Steady-State Performance
College of Electronic and Information Engineering, Hebei University, Baoding, China.
College of Electronic and Information Engineering, Hebei University, Baoding, China.
School of Automation, Southeast University, Nanjing, China; Southeast University, Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education, Nanjing, China.
College of Electronic and Information Engineering, Hebei University, Baoding, China.
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2023 (English)In: IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has attracted great attention due to its high reliability and high-power output capacities. However, the conventional single-voltage-vector-based predictive current control (SV-PCC) for DTP-PMSM presents high torque ripple and current harmonics, and high computational burden. To solve those issues, a modulated-virtual-vector-based PCC (MVV-PCC) for DTP-PMSM is proposed in this paper. Wherein, twenty-four VVs are synthesized by the inherent voltage vectors, and two VVs and one zero voltage vector with optimal duty cycles are determined and applied in each sampling period to improve the steady-state performance. The selection of optimal VVs and the calculation of the optimal duty cycles are simplified by integrating the deadbeat control and modulation scheme. Various comparisons are carried out to validate the effectiveness and superiority of the proposed MVV-PCC strategy.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023.
Keywords [en]
deadbeat control, dual three-phase PMSM, predictive current control, virtual vector
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-341618DOI: 10.1109/IECON51785.2023.10312694Scopus ID: 2-s2.0-85179502338OAI: oai:DiVA.org:kth-341618DiVA, id: diva2:1822834
Conference
49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023, Singapore, Singapore, Oct 16 2023 - Oct 19 2023
Note

Part of ISBN 9798350331820

QC 20231228

Available from: 2023-12-28 Created: 2023-12-28 Last updated: 2024-02-29Bibliographically approved

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Gao, Xiaonan

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