Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering

Qingzhong Ni and Hui Jiang ()
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Qingzhong Ni: College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Hui Jiang: College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China

Energies, 2023, vol. 16, issue 11, 1-12

Abstract: Accurate topology relationships of low-voltage distribution networks are important for distribution network management. However, the topological information in Geographic Information System (GIS) systems for low-voltage distribution networks is prone to errors such as omissions and false alarms, which can have a heavy impact on the effective management of the networks. In this study, a novel method for the identification of topology relationships, including the user-transformer relationship and the user-phase relationship, is proposed, which is based on Deep Convolutional Time-Series Clustering (DCTC) analysis. The proposed DCTC method fuses convolutional autoencoder and clustering layers to perform voltage feature representation and clustering in a low-dimensional feature space simultaneously. By jointly optimizing the clustering process via minimizing the sum of the reconstruction loss and clustering loss, the proposed method effectively identifies the network topology relationships. Analysis of examples shows that the proposed method is correct and effective.

Keywords: deep clustering; topology relationship; convolutional autoencoder (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: 2023
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