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Author:

Liu, Shen (Liu, Shen.) | Gao, Lin (Gao, Lin.)

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Abstract:

In view of the shortcomings of torque ripple and flux ripple in direct torque control (DTC) of permanent magnet synchronous motor,an improved model predictive DTC was proposed. In the two-phase stationary coordinate system,the expected voltage vector which made the electromagnetic torque and stator flux linkage which reach their given value in the next cycle was calculated based on deadbeat control. By introducing virtual voltage vectors,voltage vector space was divided into 13 sections,and the corresponding actual voltage vector was selected according to the position of the sector where the desired voltage vector was located. The selectable voltage vectors were expanded from the conventional 8 voltage vectors to 14 voltage vectors. The proposed method possesses the fixed duty ratio of virtual voltage vector,simple modulation and less operation. Simulation comparisons were carried out for original DTC,traditional model predictive DTC and improved model predictive DTC. The simulation results show that the improved model predictive DTC can effectively suppress torque ripple and flux ripple,significantly reduce stator current distortion rate and improve system robustness. © 2020, Harbin University of Science and Technology Publication. All right reserved.

Keyword:

Electric machine control Electric machine theory Permanent magnets Stators Synchronous motors Torque Torque control Vectors Vector spaces

Author Community:

  • [ 1 ] [Liu, Shen]School of Electrical Engineering, Xi'an Jiaotong University, Xi'an; 710049, China
  • [ 2 ] [Gao, Lin]School of Electrical Engineering, Xi'an Jiaotong University, Xi'an; 710049, China

Reprint Author's Address:

  • [Liu, Shen]School of Electrical Engineering, Xi'an Jiaotong University, Xi'an; 710049, China;;

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Source :

Dianji yu Kongzhi Xuebao/Electric Machines and Control

ISSN: 1007-449X

Year: 2020

Issue: 1

Volume: 24

Page: 10-17

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 32

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 17

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