A Switched Capacitor Inverter Structure with Hybrid Modulation Method Lowering Switching Loss

Yanjun Zhao, Wentao Ge, Xiaobing Liang, Yue Yang, Jingxing Tang and Junfeng Liu ()
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Yanjun Zhao: Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510000, China
Wentao Ge: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Xiaobing Liang: Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510000, China
Yue Yang: Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510000, China
Jingxing Tang: Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510000, China
Junfeng Liu: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China

Energies, 2023, vol. 16, issue 14, 1-18

Abstract: The high-frequency modulation method (HFM) for a switched capacitor (SC) inverter often leads to high switching loss since it increases switching frequency. In order to reduce the switching frequency and switching loss in the HFM for SC inverter, this paper proposes a novel hybrid modulation strategy as well as a corresponding demo switched capacitor topology. The hybrid strategy limits the number of high-frequency switches, thus reducing the switching loss significantly. Meanwhile, the demo topology maintains the merits of current switched capacitor inverter such as low switch count, quadruple voltage-boosting ability and self-balancing capacity. The principle of the hybrid modulation method and the circuit configuration of the inverter are analyzed in detail. And comparisons are introduced to demonstrate the advantages of the proposed modulation method and topology. Finally, experimental results have proved the feasibility of the proposed inverter.

Keywords: switched capacitor inverter; hybrid modulation method; switching loss (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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