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Predictive direct torque control of permanent magnet synchronous motors using deadbeat torque and flux control

  • Ao Wang (School of Electrical and Electronic Engineering, Shandong University of Technology) ;
  • Housheng Zhang (School of Electrical and Electronic Engineering, Shandong University of Technology) ;
  • Junjie Jiang (School of Electrical and Electronic Engineering, Shandong University of Technology) ;
  • Duo Jin (School of Electrical and Electronic Engineering, Shandong University of Technology) ;
  • Shengjie Zhu (State Grid Shandong Electric Power Company, Dong'e County Power Supply Company)
  • Received : 2022.06.12
  • Accepted : 2022.10.06
  • Published : 2023.02.20

Abstract

In consideration of the shortcomings of the large torque and flux ripples in direct torque control (DTC), and the insufficient utilization of the duty cycle module in traditional DTC based on the duty cycle modulation of PMSMs, a DTC method based on deadbeat control of the torque and flux is proposed in this paper. In the synchronous rotation, coordinate system positioned with the stator flux vector, the reference voltage vector is calculated in accordance to the deadbeat control method. Six virtual voltages are constructed in such a manner that two adjacent basic voltage vectors are evenly divided in one control cycle. The effective voltage vectors are expanded to 12. The effective voltage vector closest to the phase angle of the reference voltage vector is determined to be the optimal voltage vector. Then, the duty cycle is introduced to further reduce the error between the final output voltage vector and the voltage vector required by the system. Simulation comparisons are carried out for DTC, traditional DTC based on duty cycle modulation, and the proposed DTC. The obtained simulation results demonstrate that the proposed DTC can effectively suppress both torque ripple and fux ripple, improve the utilization rate of the duty cycle module, and reduce the stator current distortion rate.

Keywords

Acknowledgement

This study was funded by the National Natural Science Foundation of China (Grant No. 62076152) and the Shandong Provincial Postgraduate Education Quality Improvement Program of China (Grant No. SDYKC21127).

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