A Two-Stage Topology Identification Strategy for Low-Voltage Distribution Grids Based on Contrastive Learning

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 430074, China
Fan Yang: Power Science Research Institute of State Grid Hubei Electric Power Co., Wuhan 430074, China
Yanjun Feng: Institute of Power Distribution, College of Electrical Engineering, Southeast University, Nanjing 210096, China
Wei Hu: Power Science Research Institute of State Grid Hubei Electric Power Co., Wuhan 430074, China
Yinzhang Cheng: Power Science Research Institute of State Grid Shanxi Electric Power Co., Taiyuan 030001, China

Energies, 2025, vol. 18, issue 22, 1-18

Abstract: An accurate topology of low-voltage distribution grids (LVDGs) serves as the foundation for advanced applications such as line loss analysis, fault location, and power supply planning. This paper proposes a two-stage topology identification strategy for LVDGs based on Contrastive Learning. Firstly, the Dynamic Time Warping (DTW) algorithm is utilized to align the time series of measurement data and evaluate their similarity, yielding the DTW similarity coefficient of the sequences. The Prim algorithm is then employed to construct the initial topology framework. Secondly, aiming at the topology information obtained from the initial identification, an Unsupervised Graph Attention Network (Unsup-GAT) model is proposed to aggregate node features, enabling the learning of complex correlation patterns in unsupervised scenarios. Subsequently, a loss function paradigm that incorporates both InfoNCE loss and power imbalance loss is constructed for updating network parameters, thereby realizing the identification and correction of local connection errors in the topology. Finally, case studies are conducted on 7 LVDGs of different node scales in a certain region of China to verify the effectiveness of the proposed two-stage topology identification strategy.

Keywords: low-voltage distribution grid; topology identification; unsupervised graph attention network; dynamic time warping; contrastive learning (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/1996-1073/18/22/5886/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/22/5886/ (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:22:p:5886-:d:1790401

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

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