IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i11p4274-d1153778.html
   My bibliography  Save this article

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

Author

Listed:
  • Qingzhong Ni

    (College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Hui Jiang

    (College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

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.

Suggested Citation

  • Qingzhong Ni & Hui Jiang, 2023. "Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering," Energies, MDPI, vol. 16(11), pages 1-12, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4274-:d:1153778
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/11/4274/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/11/4274/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pavel Ilyushin & Vladislav Volnyi & Konstantin Suslov & Sergey Filippov, 2023. "State-of-the-Art Literature Review of Power Flow Control Methods for Low-Voltage AC and AC-DC Microgrids," Energies, MDPI, vol. 16(7), pages 1-35, March.
    2. Guodong Liu & Maximiliano F. Ferrari & Thomas B. Ollis & Aditya Sundararajan & Mohammed Olama & Yang Chen, 2023. "Distributed Energy Management for Networked Microgrids with Hardware-in-the-Loop Validation," Energies, MDPI, vol. 16(7), pages 1-27, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rodriguez, Mauricio & Arcos-Aviles, Diego & Guinjoan, Francesc, 2024. "Simple fuzzy logic-based energy management for power exchange in isolated multi-microgrid systems: A case study in a remote community in the Amazon region of Ecuador," Applied Energy, Elsevier, vol. 357(C).
    2. Mohammad Javad Bordbari & Fuzhan Nasiri, 2024. "Networked Microgrids: A Review on Configuration, Operation, and Control Strategies," Energies, MDPI, vol. 17(3), pages 1-28, February.
    3. Rafael A. Núñez-Rodríguez & Clodomiro Unsihuay-Vila & Johnny Posada & Omar Pinzón-Ardila & Alexandre Rasi Aoki & Rodrigo Bueno-Otto, 2024. "Real-Time Testing Optimal Power Flow in Smart-Transformer-Based Meshed Hybrid Microgrids: Design and Validation," Energies, MDPI, vol. 17(8), pages 1-21, April.
    4. Rani, Preeti & Parkash, Ved & Sharma, Naveen Kumar, 2024. "Technological aspects, utilization and impact on power system for distributed generation: A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    5. Aleksey Suvorov & Alisher Askarov & Nikolay Ruban & Vladimir Rudnik & Pavel Radko & Andrey Achitaev & Konstantin Suslov, 2023. "An Adaptive Inertia and Damping Control Strategy Based on Enhanced Virtual Synchronous Generator Model," Mathematics, MDPI, vol. 11(18), pages 1-29, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4274-:d:1153778. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.