基于时空关联矩阵的配电台区反窃电预警方法

唐冬来; 刘友波; 熊智临; 马铁丰; 苏童

doi:10.7500/AEPS20200316010

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基于时空关联矩阵的配电台区反窃电预警方法

研制与开发 | 更新时间：2025-06-16

- 基于时空关联矩阵的配电台区反窃电预警方法
- Early Warning Method of Electricity Anti-theft in Distribution Station Area Based on Spatiotemporal Correlation Matrix
- 电力系统自动化-方正平台 2020年44卷第19期页码：168-176
- 作者机构：
  
  1.四川中电启明星信息技术有限公司,四川省成都市 610041
  2.四川大学电气工程学院,四川省成都市610065
  3.西南财经大学统计学院,四川省成都市610074
- 作者简介：
  
  唐冬来(1980—)，男，高级工程师，主要研究方向：配电网量测、分析和信息化咨询等。E-mail：tangdonglai@sgitg.sgcc.com.cn
  刘友波(1983—)，男，通信作者，博士，副教授，硕士生导师，主要研究方向：电力系统人工智能技术、电力市场、主动配电网与储能利用、电网投资规划等。E-mail：liuyoubo@scu.edu.cn
  熊智临(1996—)，女，硕士，主要研究方向：智能电网大数据分析。E-mail: xiongzhil024@163.com
- 基金信息：
  
  四川省科技计划资助项目(2016GZ0143);国家电网公司科技项目(202058184A)
- DOI：10.7500/AEPS20200316010
  中图分类号：
- 收稿日期：2020-03-16，
  
  修回日期：2020-07-03，
  
  网络出版日期：2020-08-24，
  
  纸质出版日期：2020-10-10
- 稿件说明：
移动端阅览
唐冬来,刘友波,熊智临等.基于时空关联矩阵的配电台区反窃电预警方法[J].电力系统自动化,2020,44(19):168-176.

TANG Donglai,LIU Youbo,XIONG Zhilin,et al.Early Warning Method of Electricity Anti-theft in Distribution Station Area Based on Spatiotemporal Correlation Matrix[J].Automation of Electric Power Systems,2020,44(19):168-176.
唐冬来,刘友波,熊智临等.基于时空关联矩阵的配电台区反窃电预警方法[J].电力系统自动化,2020,44(19):168-176. DOI： 10.7500/AEPS20200316010.

TANG Donglai,LIU Youbo,XIONG Zhilin,et al.Early Warning Method of Electricity Anti-theft in Distribution Station Area Based on Spatiotemporal Correlation Matrix[J].Automation of Electric Power Systems,2020,44(19):168-176. DOI： 10.7500/AEPS20200316010.

摘要

针对配电台区窃电用户难发现、窃电量预估不准确的问题，提出了一种基于时空关联矩阵的配电台区反窃电预警方法。首先，构建配电台区数据清洗方法，采用线性插值算法对低压户表采集的缺失数据进行补正，以消除配电台区量测数据缺失对模型的影响。其次，构建配电台区窃电分析算法，通过台区线损波动率、线损与电流差异曲线的变点时间进行关联分析，从而判断台区是否存在窃电行为。再次，构建窃电用户的时空关联分析模型，通过变点、离群点和关联检测分析窃电嫌疑用户的时空分布特征，并计及用户窃电时间和用电容量等特性，提供预估窃电量。最后，通过实例验证了所提方法的有效性和实用性。

Abstract

Aiming at the problems that it is difficult to find electricity theft users and accurately estimate the quantity of electricity theft

an early warning method of electricity anti-theft in the distribution station area based on the spatiotemporal correlation matrix is proposed. Firstly

the data cleaning method in the distribution station area is constructed

and the linear interpolation algorithm is used to fill in the missing data of the low-voltage household meter

which eliminates the influence of missing measurement data in the distribution station area on the model. Secondly

the analysis algorithm of electricity theft in the distribution station area is built

which can diagnose whether there is electricity theft behavior in the station area through the correlative analysis of line loss volatility

line loss and the time of change-points on the current difference curve. Thirdly

the analysis model of spatiotemporal correlation is proposed. It analyzes the temporal and spatial distribution characteristics of users suspected of electricity theft through change-points

outlier points and correlation detection

and provides estimated quantity of electricity theft with characteristics of electricity theft time and power consumption capacity. Finally

the effectiveness and practicability of the proposed method are verified by an example.

关键词

Keywords

references

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