Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Weighting factor design based on SVR-MOPSO for finite set MPC operated power electronic converters

  • Liu, Yonglu (School of Automation, Central South University) ;
  • Yang, Zhengmao (School of Automation, Central South University) ;
  • Liu, Xubin (School of Automation, Central South University) ;
  • Dan, Hanbing (School of Automation, Central South University) ;
  • Xiong, Wenjing (School of Automation, Central South University) ;
  • Ling, Tao (School of Automation, Central South University) ;
  • Su, Mei (School of Automation, Central South University)
  • Received : 2021.10.21
  • Accepted : 2022.03.06
  • Published : 2022.07.20
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Abstract

Selecting weighting factors is a challenge for the finite set model predictive control (FS-MPC). Based on the support vector regression (SVR) algorithm and the multi-objective particle swarm optimization (MOPSO) algorithm, this paper proposes a new weighting factor design principle. SVR is used to establish the functional relationship between the input weighting factors and the output performance indexes (such as the average switching frequency (fsw) and the total harmonic distortion of the output voltage). Even in the case of small samples, this can provide accurate performance index estimates for any combination of weighting factors. The established SVR function is taken as the fitness function. Then, MOPSO is used to search for Pareto optimal weighting factor combinations. The proposed method can converge in a few steps and does not require tedious calculations. Moreover, it is applicable to optimization problem with two or more weighting factors for arbitrary topology models. It also provides a range of optimal weighting factor solution sets. Finally, the proposed methodology is verified on a practical weighting factor design problem in a FS-MPC regulated voltage source inverter. Experimental results confirm the correctness of the theoretical analysis.

Keywords

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 61903381, in part by Changsha City Science and Technology Plan Project under Grant kq2009007, and in part by the Hunan Provincial Key Laboratory of Power Electronics Equipment and Grid under Grant 2018TP1001.

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