AUTHOR=Deng Wenyang , Zhang Yongjun , Tang Yuan , Li Qinhao , Yi Yingqi 

TITLE=A neural network-based adaptive power-sharing strategy for hybrid frame inverters in a microgrid

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.1082948

DOI=10.3389/fenrg.2022.1082948

ISSN=2296-598X

ABSTRACT=Flexible reactive power condition is vital for a microgrid (MG) system. The power conditioning character of the capacitive-coupling inverter (CCI) based DG unit distinguishes it from the inductive-coupling inverter (ICI) based DG unit. Previous studies indicate that CCI is more cost-effective in reactive power conditioning and enhanced reactive power regulation ability. Thus, to improve the flexibility of reactive power condition for the MG system, a brand-new MG frame with hybrid parallel-connected ICIs and CCIs are proposed.  Besides, considering the CCI can work under a lower DC-link voltage, to reduce the total power capacity of the proposed system to decrease the loss, in this paper, an unequal power-sharing method is investigated, to match different inverters. A case study with proposed unequal power-sharing is presented; a BP neural network-based power-sharing control layer that guarantees the rapid and accurate sharing ratio computation is investigated as well. Simulation and experiment results verified the effectiveness of the proposed method.