2025年4月5日 周六
首页 > 过刊浏览>2023年第38卷第4期 >1-14. DOI:10.19781/j.issn.1673-9140.2023.04.001
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窃电行为检测方法研究综述
CSTR:
[cstr]
作者:
作者单位:

(1.国网湖南省电力有限公司，湖南 长沙 410004; 2.湖南大学电气与信息工程学院，湖南 长沙 410082)

通讯作者:

高云鹏（1978—），男，教授，博士生导师，主要从事电能计量、智能信息处理方面的研究；E?mail: gfront@126.com

中图分类号:

TM715

基金项目:

国网湖南省电力有限公司科技项目（5216AG21001T）；国家自然科学基金（51777061）


Summary of research on electricity theft behavior detection methods
Author:
Affiliation:

(1.State Grid Hunan Electric Power Co.,Ltd.,Changsha 410004, China; 2. College of Electrical and Information Engineering,Hunan University, Changsha 410082, China.)

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    摘要:

    电力系统中因窃电行为对电网公司造成的非技术损失一直是电网公司迫切解决的难题。伴随电网大量部署智能电表，利用电力计量自动化系统采集的用户侧数据开展窃电行为准确检测受到研究者和电网公司的普遍关注。首先，介绍用户窃电行为基本分类情况、评价指标与现有窃电检测数据集；然后，从基于电网状态分析、机器学习、博弈论以及硬件4个方面对现有窃电行为检测方法进行全方面整理、剖析与对比，总结出各方法基本思路和优缺点；最后，对当前窃电行为检测领域研究中存在的挑战深入分析，并对未来研究工作重点进行展望。

    Abstract:

    The non?technical losses caused by electricity theft in the power system have always been a pressing issue for power grid companies to urgently address. With the deployment of a large number of smart meters in the power grid, the use of user?side data collected by the power metering automation system to accurately detect electricity theft has attracted widespread attention from researchers and power grid companies. Firstly, the basic classification of users' electricity stealing behavior, evaluation indicators and existing electricity theft detection data sets are introduced. Then, from the four aspects of grid state analysis, machine learning, game theory and hardware, the existing detection methods of electricity theft behavior are comprehensively sorted, analyzed and compared, and the basic ideas, advantages and disadvantages of each method are summarized. Finally, the current challenges in the field of electricity theft behavior detection are deeply analyzed, and a prospective outlook on the focus of future research work is provided.

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肖 宇,叶 志,黄 瑞,等.窃电行为检测方法研究综述[J].电力科学与技术学报,2023,38(4):1-14.
XIAO Yu, YE Zhi, HUANG Rui, et al. Summary of research on electricity theft behavior detection methods[J]. Journal of Electric Power Science and Technology,2023,38(4):1-14.

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