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A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network

Author

Listed:
  • Yang Lei

    (Power Science Research Institute of State Grid Hubei Electric Power Co., Wuhan 430048, China)

  • Fan Yang

    (Power Science Research Institute of State Grid Hubei Electric Power Co., Wuhan 430048, China)

  • Yanjun Feng

    (School of Electrical Engineering, Southeast University, Nanjing 211102, China)

  • Wei Hu

    (Power Science Research Institute of State Grid Hubei Electric Power Co., Wuhan 430048, China)

  • Yinzhang Cheng

    (Power Science Research Institute of State Grid Shanxi Electric Power Co., Taiyuan 030021, China)

Abstract

Accurate topological connectivity is critical for the safe operation and management of low-voltage distribution grids (LVDGs). However, due to the complexity of the structure and the lack of measurement equipment, obtaining and maintaining these topological connections has become a challenge. This paper proposes a topology identification strategy for LVDGs based on a feature-enhanced graph attention network (F-GAT). First, the topology of the LVDG is represented as a graph structure using measurement data collected from intelligent terminals, with a feature matrix encoding the basic information of each entity. Secondly, the meta-path form of the heterogeneous graph is designed according to the connection characteristics of the LVDG, and the walking sequence is enhanced using a heterogeneous skip-gram model to obtain an embedded representation of the structural characteristics of each node. Then, the F-GAT model is used to learn potential association patterns and structural information in the graph topology, achieving a joint low-dimensional representation of electrical attributes and graph semantics. Finally, case studies on five urban LVDGs in the Wuhan region are conducted to validate the effectiveness and practicality of the proposed F-GAT model.

Suggested Citation

  • Yang Lei & Fan Yang & Yanjun Feng & Wei Hu & Yinzhang Cheng, 2025. "A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network," Energies, MDPI, vol. 18(11), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2821-:d:1667020
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    References listed on IDEAS

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    1. Qingzhong Ni & Hui Jiang, 2023. "Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering," Energies, MDPI, vol. 16(11), pages 1-12, May.
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