AUTHOR=Liu Yang , Li Haoheng , Ma Yiming 

TITLE=Improving the synchronous stability of grid-following converters using flux linkage feedback combined with the DFF neural network

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2025.1644865

DOI=10.3389/fenrg.2025.1644865

ISSN=2296-598X

ABSTRACT=A hybrid scheme of flux linkage feedback (FLF) combined with the deep feed-forward (DFF)-genetic algorithm (GA) method to improve the synchronous stability of grid-following (GFL) converters is proposed in this paper. By subtracting three-phase flux linkages from three-phase voltage references generated by the GFL controller, the FLF and the parameter optimization based on DFF-GA are expected to improve the equivalent virtual damping in the dynamics of the angular speed of the phase-locked loop (PLL). The implementation of the proposed method will reduce the risk of synchronous instability of the converter with respect to the stability region. The virtual flux linkages can be calculated by the integration of the instantaneous voltage; thus, no additional measurement is required. The effectiveness of FLF has been verified by region of attraction, simulation, and experimental studies, which show that the system with the FLF scheme presents longer critical clearing time (CCT) than the traditional method and the PLL damping enhancement strategy. Moreover, the proposed scheme does not change the internal structure of the existing controller and is easy to implement on various GFL converters.