Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering
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, whi...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/11/4274 |
| _version_ | 1797597664605372416 |
|---|---|
| author | Qingzhong Ni Hui Jiang |
| author_facet | Qingzhong Ni Hui Jiang |
| author_sort | Qingzhong Ni |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:08:46Z |
| format | Article |
| id | doaj.art-962b5b011b534165b12eaa47b6a94d70 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T03:08:46Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-962b5b011b534165b12eaa47b6a94d702023-11-18T07:46:39ZengMDPI AGEnergies1996-10732023-05-011611427410.3390/en16114274Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series ClusteringQingzhong Ni0Hui Jiang1College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, ChinaAccurate 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.https://www.mdpi.com/1996-1073/16/11/4274deep clusteringtopology relationshipconvolutional autoencoder |
| spellingShingle | Qingzhong Ni Hui Jiang Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering Energies deep clustering topology relationship convolutional autoencoder |
| title | Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering |
| title_full | Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering |
| title_fullStr | Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering |
| title_full_unstemmed | Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering |
| title_short | Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering |
| title_sort | topology identification of low voltage distribution network based on deep convolutional time series clustering |
| topic | deep clustering topology relationship convolutional autoencoder |
| url | https://www.mdpi.com/1996-1073/16/11/4274 |
| work_keys_str_mv | AT qingzhongni topologyidentificationoflowvoltagedistributionnetworkbasedondeepconvolutionaltimeseriesclustering AT huijiang topologyidentificationoflowvoltagedistributionnetworkbasedondeepconvolutionaltimeseriesclustering |