Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining

Jianqiao Sun, Wei Zhang, Peng Guo, Xunan Ding, Chaohui Wang and Fei Wang ()
Additional contact information
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

Energies, 2024, vol. 17, issue 5, 1-19

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.

Keywords: fault diagnosis; metering anomaly diagnosis; smart grid; few-shot learning; deep 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: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/5/993/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/5/993/ (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:17:y:2024:i:5:p:993-:d:1342378

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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