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Signal Injection-Based Topology Identification for Low-Voltage Distribution Networks Considering Missing Data

Author

Listed:
  • Yilong Duan

    (College of Electrical Engineering, Hunan Industry Polytechnic, Changsha 410208, China)

  • Zheng Liu

    (College of Electrical Engineering, Hunan Industry Polytechnic, Changsha 410208, China)

  • Yuanyuan Liu

    (College of Electrical Engineering, Hunan Industry Polytechnic, Changsha 410208, China)

  • Yong Li

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

Abstract

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal injection-based topology identification algorithm is favored for its speed and efficiency. This research introduces an innovative topology identification algorithm based on signal injection, specifically designed to address the challenges of incomplete and inaccurate identifications caused by the missing data in feature signal records (FSRs). Based on the correlations among FSRs at various devices, the algorithm introduces a dual-axis completion strategy—both vertical and horizontal—to effectively address missing data. Subsequently, an inclusion detection process is devised to process the completed FSRs, culminating in an accurate topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the proposed algorithm markedly enhances the completeness and accuracy of topology identification. This advancement offers a robust solution tailored to accommodate the dynamic and swiftly changing topological configurations of LVDNs.

Suggested Citation

  • Yilong Duan & Zheng Liu & Yuanyuan Liu & Yong Li, 2024. "Signal Injection-Based Topology Identification for Low-Voltage Distribution Networks Considering Missing Data," Energies, MDPI, vol. 17(9), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2060-:d:1383499
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    References listed on IDEAS

    as
    1. Lingyan Sun & Yu Chen & Qinjun Du & Rui Ding & Zhidong Liu & Qian Cheng, 2023. "Topology Identification of Low-Voltage Power Lines Based on IEC 61850 and the Clustering Method," Energies, MDPI, vol. 16(3), pages 1-20, January.
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