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With the continuous improvement of the penetration rate of distributed photovoltaic in the distribution area, the safe and stable operation of the distribution area is facing great challenges. Therefore, this paper studied a fault warning method for distribution stations based on an improved LSTM-FCM algorithm. Firstly, the fault characteristic information of the distribution station area with high penetration rate distributed photovoltaics was extracted, which can provide a data foundation for early warning; Secondly, the improved FCM method was used to determine the fault boundary conditions, the improved LSTM method was used to predict the feature information, and the two were combined for comprehensive judgment to obtain the warning results. Then, the comparative analysis of examples shows that the improved LSTM and FCM clustering algorithms have more advantages. Finally, the simulation analysis of a low-voltage distribution station area in Zaozhuang verifies that the method in this paper can accurately warn the fault of the station area.

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Research on Fault Early Warning Method of Distributed Photovoltaic Access Distribution Station Area Based on Improved LSTM-FCM Method

  • 会议论文

  • 发表日期：

    2026年

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  • Simeng Zhu,
  • Ke Li,
  • Maoshu Chen,
  • Guoliang Li,
  • Yifan Wang,
  • Wenhua Xia

摘要

With the continuous improvement of the penetration rate of distributed photovoltaic in the distribution area, the safe and stable operation of the distribution area is facing great challenges. Therefore, this paper studied a fault warning method for distribution stations based on an improved LSTM-FCM algorithm. Firstly, the fault characteristic information of the distribution station area with high penetration rate distributed photovoltaics was extracted, which can provide a data foundation for early warning; Secondly, the improved FCM method was used to determine the fault boundary conditions, the improved LSTM method was used to predict the feature information, and the two were combined for comprehensive judgment to obtain the warning results. Then, the comparative analysis of examples shows that the improved LSTM and FCM clustering algorithms have more advantages. Finally, the simulation analysis of a low-voltage distribution station area in Zaozhuang verifies that the method in this paper can accurately warn the fault of the station area.