Journal of Power Electronics

  • 제20권2호
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  • Pages.553-565
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  • 2020
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  • 1598-2092(pISSN)
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  • 2093-4718(eISSN)
전력전자학회 (The Korean Institute of Power Electronics)
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Predictive control with optimal vector sequence for permanent magnet synchronous motors

  • Li, Chen (School of Electrical and Information Engineering, Tianjin University) ;
  • Shi, Ting-Na (College of Electrical Engineering, Zhejiang University) ;
  • Yan, Yan (College of Electrical Engineering, Zhejiang University) ;
  • Zhou, Zhan-Qing (School of Electrical Engineering and Automation, Tiangong University) ;
  • Xia, Chang-Liang (School of Electrical Engineering and Automation, Tiangong University)
  • 투고 : 2019.07.23
  • 심사 : 2019.11.07
  • 발행 : 2020.03.20
⟨ 이전 논문 다음 논문 ⟩

초록

Conventional model predictive control aims to minimize ripples of the torque, stator flux and current at the end instant of each control period. However, the trajectories of the torque and flux during the entire control period are changed when multiple voltage vectors are applied in different sequences in each period. This indicates that the ripples of the torque and stator flux are influenced by different vector sequences (i.e., the sequence multiple vectors applied in). For that issue, this paper analyzes the RMS values of the stator flux vector ripple when three basic vectors are applied in different five-segment sequences, and proposes a predictive control with optimal vector sequence method. Based on this analysis, the available vector sequences that reduce the stator flux vector ripple are screened out. Moreover, a cost function is constructed to select the optimal vector sequence by evaluating the stator flux ripple during the entire control period and the tracking error of the stator flux vector at the end instant of each control period. Finally, simulation and experimental results verify the effectiveness of the proposed method.

키워드

과제정보

The authors are grateful to the Key Project in the Major Program of National Natural Science Foundation of China (51690183), and the National Natural Science Foundation of China (51907142) for supporting this work.

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