A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network
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 p...
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MDPI AG
2025-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/11/2821 |
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| author | Yang Lei Fan Yang Yanjun Feng Wei Hu Yinzhang Cheng |
| author_facet | Yang Lei Fan Yang Yanjun Feng Wei Hu Yinzhang Cheng |
| author_sort | Yang Lei |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e70d07f845f946aa989d101fb85fec0b |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e70d07f845f946aa989d101fb85fec0b2025-08-20T02:32:37ZengMDPI AGEnergies1996-10732025-05-011811282110.3390/en18112821A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention NetworkYang Lei0Fan Yang1Yanjun Feng2Wei Hu3Yinzhang Cheng4Power Science Research Institute of State Grid Hubei Electric Power Co., Wuhan 430048, ChinaPower Science Research Institute of State Grid Hubei Electric Power Co., Wuhan 430048, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 211102, ChinaPower Science Research Institute of State Grid Hubei Electric Power Co., Wuhan 430048, ChinaPower Science Research Institute of State Grid Shanxi Electric Power Co., Taiyuan 030021, ChinaAccurate 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.https://www.mdpi.com/1996-1073/18/11/2821low-voltage distribution gridtopology identificationfeature-enhanced graph attention networkmeta-path formheterogeneous skip-gram |
| spellingShingle | Yang Lei Fan Yang Yanjun Feng Wei Hu Yinzhang Cheng A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network Energies low-voltage distribution grid topology identification feature-enhanced graph attention network meta-path form heterogeneous skip-gram |
| title | A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network |
| title_full | A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network |
| title_fullStr | A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network |
| title_full_unstemmed | A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network |
| title_short | A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network |
| title_sort | topology identification strategy of low voltage distribution grids based on feature enhanced graph attention network |
| topic | low-voltage distribution grid topology identification feature-enhanced graph attention network meta-path form heterogeneous skip-gram |
| url | https://www.mdpi.com/1996-1073/18/11/2821 |
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