IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i5p993-d1342378.html
   My bibliography  Save this article

Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining

Author

Listed:
  • Jianqiao Sun

    (State Grid Zhejiang Marketing Service Center, Hangzhou 310007, China)

  • Wei Zhang

    (State Grid Zhejiang Marketing Service Center, Hangzhou 310007, China)

  • Peng Guo

    (State Grid Zhejiang Marketing Service Center, Hangzhou 310007, China)

  • Xunan Ding

    (State Grid Zhejiang Marketing Service Center, Hangzhou 310007, China)

  • Chaohui Wang

    (School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Fei Wang

    (School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Metering anomalies not only mean huge economic losses but also indicate the faults of equipment and power lines, especially within the substation. As a result, metering anomaly diagnosis is becoming one of the most important missions in smart grids. However, due to the insufficient and imbalanced anomaly cases, identifying the anomalies in smart meter data accurately and efficiently remains challenging. Existing methods usually employ few-shot learning models in computer vision directly, which requires the rich experience of human experts and sufficient abnormal cases for training. It blocks model generalizing to various application scenarios. To address these shortcomings, we propose a novel framework for metering anomaly diagnosis based on few-shot learning, named FSMAD. Firstly, we design a fault data injection model to emulate anomalies, so that no abnormal samples are required in the training phase. Secondly, we provide a learnable variable transformation to reveal inherent relationships among various smart meter data and help FSMAD extract more efficient features. Finally, the deeper metric network is equipped to support FSMAD in obtaining powerful comparison capability. Extensive experiments on a real-world dataset demonstrate the advantages of our FSMAD over state-of-the-art methods.

Suggested Citation

  • Jianqiao Sun & Wei Zhang & Peng Guo & Xunan Ding & Chaohui Wang & Fei Wang, 2024. "Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining," Energies, MDPI, vol. 17(5), pages 1-19, February.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:993-:d:1342378
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/5/993/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/5/993/
    Download Restriction: no
    ---><---

    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:17:y:2024:i:5:p:993-:d:1342378. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.