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

Yang Lei, Fan Yang, Yanjun Feng (), Wei Hu and Yinzhang Cheng
Additional contact information
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

Energies, 2025, vol. 18, issue 11, 1-17

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.

Keywords: low-voltage distribution grid; topology identification; feature-enhanced graph attention network; meta-path form; heterogeneous skip-gram (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/11/2821/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/11/2821/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:11:p:2821-:d:1667020

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().