Detection of Electricity Theft by Low Voltage Users with Zero Power Consumption Based on Water-Electricity Correlation Information

Abstract

Figures/Tables 15

References 26

doi:10.11930/j.issn.1004-9649.202211098

Abstract:

It is hard to obtain the effective information on electricity consumption behaviors of zero-power consumption users among low-voltage residential electricity theft users who are easily confused with vacant house owners, which is a special problem for electricity theft detection. Based on the strong correlation between water and electricity data of residential users, a zero-power user electricity theft detection method is proposed based on water-electricity correlation information. Firstly, the relationship between electricity and water consumption data of residential users is analyzed. Then, a maximal information model of users' daily electricity consumption and daily water consumption is constructed, and the maximum information coefficient (MIC) at different time scales is calculated to measure the information correlation. Thirdly, the user's maximal information coefficient is clustered, and the samples that deviate significantly from the cluster are identified as the suspected electricity theft users with weak water-electricity correlation. When the electricity consumption of the suspected electricity theft users is zero, they are identified as the electricity theft users with zero power consumption. The test examples of two distribution areas show that the proposed method can effectively detect the electricity theft users with zero power consumption and guide the on-site electricity theft detection work.

Key words: multi-meter unification, zero power consumption users, electricity theft, water and electricity consumption behavior, maximal information coefficient, wavelet clustering

Nian ZOU, Meifang WEI, Sheng SU, Yingjun ZHENG, Wenqing ZHOU. Detection of Electricity Theft by Low Voltage Users with Zero Power Consumption Based on Water-Electricity Correlation Information[J]. Electric Power, 2023, 56(12): 206-216.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202211098

https://www.electricpower.com.cn/EN/Y2023/V56/I12/206

Fig.1 Scatter diagram of water and electricity consumption of typical residential users at different time scales

Fig.2 Diagram of daily water and electricity consumption of typical residential users

Fig.3 Box diagram of Pearson value of monthly water and electricity consumption of typical residential users

Fig.4 MIC generated by two-dimensional space

Fig.5 Flowchart of wavelet clustering

Fig.6 Flowchart of electricity theft detection based on users' daily water and electricity consumption data

Fig.7 MIC curve of daily water and electricity consumption when Q=20% and T=25 days

Fig.8 Visualization result of wavelet clustering when Q=20% and T=25 days

Table 1 Wavelet clustering results under various sliding window intervals when Q=20%

Table 2 Wavelet clustering results with only electricity consumption data

Q/%	检测结果（异常用户1~5的个数）	误检个数	Q/%	检测结果（异常用户1~5的个数）	误检个数
20	2	2	60	5	0
30	4	5	80	5	0
40	5	0	100	5	0

Table 3 Test results under various zero-power duration ratios

检测时长T/天	检测结果（异常用户1~5的个数）
检测时长T/天	Q=20%	Q=40%	Q=60%	Q=80%	Q=100%
15	3	5	5	5	5
20	5	5	5	5	5
25	5	5	5	5	5
30	5	5	5	5	5
35	5	5	5	5	5
40	4	5	5	5	5

Fig.9 Maximum information coefficient curve of daily water and electricity consumption in 2# high power-loss distribution area

Fig.10 Visualization result of wavelet clustering in 2# high power-loss distribution area

Table 4 Granger test results of line loss and electricity consumption in 2# high power-loss distribution area

假设		显著性水平
线损电量不是用户7的格兰杰原因		0.2414
用户7不是线损电量的格兰杰原因		0.3008
线损电量不是用户29的格兰杰原因		0.3559
用户29不是线损电量的格兰杰原因		0.0385

Fig.11 MIC value of line loss and electricity consumption in 2# high power-loss distribution area

检测结果（异常
用户1~5的个数）

检测结果（异常
用户1~5的个数）

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