Improving the synchronous stability of grid-following converters using flux linkage feedback combined with the DFF neural network
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 gener...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1644865/full |
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| author | Yang Liu Haoheng Li Yiming Ma |
| author_facet | Yang Liu Haoheng Li Yiming Ma |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dc5e6c979bba44738ac2f51d7ed99fb6 |
| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-dc5e6c979bba44738ac2f51d7ed99fb62025-08-20T03:36:41ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-08-011310.3389/fenrg.2025.16448651644865Improving the synchronous stability of grid-following converters using flux linkage feedback combined with the DFF neural networkYang Liu0Haoheng Li1Yiming Ma2School of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaCSG Power Generation Co., Ltd., Guangzhou, ChinaA 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.https://www.frontiersin.org/articles/10.3389/fenrg.2025.1644865/fullgrid-following converterflux linkage feedbacksynchronous stabilitydeep learningneural network |
| spellingShingle | Yang Liu Haoheng Li Yiming Ma Improving the synchronous stability of grid-following converters using flux linkage feedback combined with the DFF neural network Frontiers in Energy Research grid-following converter flux linkage feedback synchronous stability deep learning neural network |
| title | Improving the synchronous stability of grid-following converters using flux linkage feedback combined with the DFF neural network |
| title_full | Improving the synchronous stability of grid-following converters using flux linkage feedback combined with the DFF neural network |
| title_fullStr | Improving the synchronous stability of grid-following converters using flux linkage feedback combined with the DFF neural network |
| title_full_unstemmed | Improving the synchronous stability of grid-following converters using flux linkage feedback combined with the DFF neural network |
| title_short | Improving the synchronous stability of grid-following converters using flux linkage feedback combined with the DFF neural network |
| title_sort | improving the synchronous stability of grid following converters using flux linkage feedback combined with the dff neural network |
| topic | grid-following converter flux linkage feedback synchronous stability deep learning neural network |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1644865/full |
| work_keys_str_mv | AT yangliu improvingthesynchronousstabilityofgridfollowingconvertersusingfluxlinkagefeedbackcombinedwiththedffneuralnetwork AT haohengli improvingthesynchronousstabilityofgridfollowingconvertersusingfluxlinkagefeedbackcombinedwiththedffneuralnetwork AT yimingma improvingthesynchronousstabilityofgridfollowingconvertersusingfluxlinkagefeedbackcombinedwiththedffneuralnetwork |