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Three-level boost inverter with capacitor voltage self-balancing and high conversion efficiency for low DC voltage systems

  • Bihua Hu (School of Automation and Electronic Information, Xiangtan University) ;
  • Zhaohong Tang (School of Automation and Electronic Information, Xiangtan University) ;
  • Zhi Zhang (College of Electronic Engineering, Dongguan University of Technology) ;
  • Jinqing Linghu (School of Mechanical and Electrical Engineering, Guizhou Normal University) ;
  • Jihong Qian (Weisheng Energy Industry Technology Co., Ltd.) ;
  • Xiangyun Qin (School of Automation and Electronic Information, Xiangtan University) ;
  • Bumin Meng (School of Automation and Electronic Information, Xiangtan University) ;
  • Rong Han (TBEA Hengyang Transformer Co., Ltd.)
  • Received : 2023.02.10
  • Accepted : 2023.06.26
  • Published : 2023.12.20

Abstract

Currently, Z-source networks are widely employed to extend the output-voltage range of inverters operating at a low voltage DC source. However, these inverters are troubled by low power-conversion efficiency and an obvious current distortion due to the copper losses and core losses of the inductors. In addition, they have limited voltage levels. In this paper, a novel boost network composed of two power switches, two capacitors, and two diodes is proposed to overcome these shortcomings. Meanwhile, a corresponding modulation strategy is also set forth to achieve capacitor voltage self-balancing and to regulate the output AC voltage. Moreover, by adding more switched-capacitor cells, the range of the output voltage can be further improved, and the max DC/AC-voltage conversion ratio of the inverter with n cells is √3(n+1)/3. At last, an inverter prototype with a 1 kW power rating is built, and the obtained results demonstrate that this inverter possesses the following superiorities: a wider range of output voltage, automatic balancing of the capacitor voltage, less current distortion, and high-efficiency power conversion.

Keywords

Acknowledgement

This work was supported in part by the Innovation Platform and Talent Program of Hunan Province under Grant 2021RC2095, in part by Natural Science Foundation of China under Grant 62003288, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515140009, in part by Science and Technology Research Project of Guizhou Province under Grant Qiankehe Basic-ZK[2022]General 17, and in part by the Science and Technology Innovation Program of Hunan Province under Grant 2022GK2050.

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