Finite-set Model Predictive Current Control for Variable-flux Memory Machine Drives with a Three-stage Optimization Strategy
ID:62 View Protection:PRIVATE Updated Time:2023-06-12 11:25:52 Hits:1184 Oral Presentation

Start Time:2023-06-18 17:00(Asia/Shanghai)

Duration:20min

Session:S Oral Session » S6Oral Session 9 & Oral Session 12

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Abstract
This paper proposes a novel finite-set model predictive current control (FS-MPCC) method for variable flux memory machine (VFMM) drives with a three-stage optimization strategy. A geometric modulation method is proposed for the sake of extending the control set, by which, the number of options is extended from 7 to 193. In order to reduce the computation burden, a three-stage optimization strategy is proposed, which is composed of three parts, i.e. determining the sector, narrowing the targeted range and selecting the optimum vector. By the proposed strategy, only 9 vectors are associated with the current prediction and the cost function calculation. On this basis, a duty-cycle solving algorithm is utilized to further improve the steady-state performance of the investigated VFMM. Finally, simulation results are presented to verify the proposed method.
Keywords
control set;duty-cycle calculation;finite-set model predictive current control;optimization strategy;variable flux memory machine
Speaker
Xing Liu
Student Southeast University

Xing Liu was born in Huaian, China, in 1995. He received the B.Eng. and M.Sc. degrees in the School of Electrical Engineering, Nantong University, Nantong, China, in 2018 and 2022, respectively. He is currently working toward the Ph.D. degree in the School of Electrical Engineering, Southeast University, Nanjing, China.
His current research interests include permanent magnet synchronous machine drives and control strategies.

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Important Date
  • Conference Date

    Jun 16

    2023

    to

    Jun 19

    2023

  • Jun 15 2023

    Contribution Submission Deadline

  • Jul 02 2023

    Registration deadline

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