Finite-set Model Predictive Current Control for Variable-flux Memory Machine Drives with a Three-stage Optimization Strategy
编号:62 访问权限:私有 更新:2023-06-12 11:25:52 浏览:390次 口头报告

报告开始:2023年06月18日 17:00(Asia/Shanghai)

报告时间:20min

所在会场:[S] Oral Session [S6] Oral Session 9 & Oral Session 12

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摘要
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.
关键词
control set;duty-cycle calculation;finite-set model predictive current control;optimization strategy;variable flux memory machine
报告人
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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重要日期
  • 会议日期

    06月16日

    2023

    06月19日

    2023

  • 06月15日 2023

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  • 07月02日 2023

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主办单位
Huazhong University of Science and Technology, China
(IEEE PELS)
IEEE
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