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<p>When a fault occurs in the distribution network, traditional reclosing methods may not be sufficient to effectively distinguish the fault. Unrestricted reclosing may reintroduce system interference, potentially exacerbating the fault situation. To overcome these limitations, this paper developed a three-phase adaptive reclosing fault identification method that utilizes repetitive prediction of beat frequency current. This method extracts characteristic currents through full cycle current integration and analyzes voltage characteristic changes under different fault scenarios. This method adaptively adjusts the reclosing delay based on protection actions and system voltage conditions, while utilizing beat frequency characteristics to distinguish between transient and permanent faults. Compared with existing methods, the method proposed in this paper achieves a recognition accuracy of 97%, which is a significant improvement over traditional techniques that typically range from 85% to 92%. The predicted current waveform is accurately aligned with the actual measured value, with a maximum amplitude error of 0.001 kA, and the recognition time gradually shortens from 0.71&#xa0;s to 0.18&#xa0;s. Moreover, this method maintains excellent performance under high noise conditions, confirming its enhanced robustness. These advances have collectively established a new fault management paradigm for the distribution network, coordinating high-precision identification with real-time operational requirements.</p>

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Three-Phase Adaptive Reclosing Fault Identification Method of Distribution Network Based on Repeated Prediction of Beat Frequency Current

  • 期刊论文

  • 发表日期：

    2026年3月26日

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  • Kai Zhang,
  • Guanliang Li,
  • Shixuan Lv,
  • Dongdong Yang,
  • Wei Wang,
  • Lu Bai

摘要

When a fault occurs in the distribution network, traditional reclosing methods may not be sufficient to effectively distinguish the fault. Unrestricted reclosing may reintroduce system interference, potentially exacerbating the fault situation. To overcome these limitations, this paper developed a three-phase adaptive reclosing fault identification method that utilizes repetitive prediction of beat frequency current. This method extracts characteristic currents through full cycle current integration and analyzes voltage characteristic changes under different fault scenarios. This method adaptively adjusts the reclosing delay based on protection actions and system voltage conditions, while utilizing beat frequency characteristics to distinguish between transient and permanent faults. Compared with existing methods, the method proposed in this paper achieves a recognition accuracy of 97%, which is a significant improvement over traditional techniques that typically range from 85% to 92%. The predicted current waveform is accurately aligned with the actual measured value, with a maximum amplitude error of 0.001 kA, and the recognition time gradually shortens from 0.71 s to 0.18 s. Moreover, this method maintains excellent performance under high noise conditions, confirming its enhanced robustness. These advances have collectively established a new fault management paradigm for the distribution network, coordinating high-precision identification with real-time operational requirements.