Optimal switching sequence model predictive control for three‐level NPC grid‐connected inverters

IET Power Electronics

Qiuwei Wu¹  Xun Lyu²  Jianguo Lyu²  Jinyong Ding²  Zhuang Sun²  Han Yan³ 

[1] Department of Electrical Engineering Technical University of Denmark Lyngby Denmark;School of Automation Nanjing University of Science and Technology Xiaolingwei Nanjing 200 China;School of Electrical Engineering Southeast University Nanjing China;

关键词: Optimal control;    Voltage control;    Control of electric power systems;    DC‐AC power convertors (invertors);   

DOI  :  10.1049/pel2.12050

来源: DOAJ

【 摘 要 】

Abstract In order to concentrate the frequency spectrum of the output voltage and improve the quality of grid currents for the three‐level neutral point clamped inverter with the model predictive control, this paper proposes an optimal switching sequence model predictive control algorithm. Based on the increments of grid currents and the neutral‐point voltage, the predictive model of the inverter is established in αβ frame. Moreover, including grid currents and the neutral point voltage tracking, the cost function is designed and simplified by arranging the voltage vector sequence in a sampling cycle appropriately. Meanwhile, the calculation of the optimal dwell time for each voltage vector sequence is derived by Lagrange multiplier method, and its solving process is simplified to reduce online computations. Furthermore, according to different voltage vector sectors, a voltage vector sequence preselection principle is introduced in this paper, besides, considering redundant small vectors in a pair have opposite affections on the neutral point voltage, the amount of voltage vector sequence that needs to be verified is further cut down, for obtaining the optimal voltage vector sequence. Finally, the experimental results show that the proposed method can concentrate the output frequency spectrums, which maintains a good quality of grid currents with a small neutral‐point voltage fluctuation.

【 授权许可】

Unknown