Research on Power Consumption Anomaly Detection Based on Fuzzy Clustering and Trend Judgment

被引：0

作者：

Xiong W. ^{[1
,2
]}

Li X. ^{[1
,2
]}

Zou Y. ^{[3
]}

Su S. ^{[1
,2
]}

Zhi L. ^{[1
,2
]}

机构：

[1] College of Electricity and New Energy, Three Gorges University, Yichang

[2] Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, Three Gorges University, Yichang

[3] Qinzhou Power Supply Bureau of Guangxi Power Grid, Qinzhou

来源：

Energy Engineering: Journal of the Association of Energy Engineering | 2022年 / 119卷 / 02期

关键词：

Degree of membership; Fuzzy clustering; Trend judgment; User power consumption;

D O I：

10.32604/ee.2022.018009

中图分类号：

学科分类号：

摘要：

Among the end-users of the power grid, especially in the rural power grid, there are a large number of users and the situation is complex. In this complex situation, there are more leakage caused by insulation damage and a small number of users stealing electricity. Maintenance staff will take a long time to determine the location of the abnormal user meter box. In view of this situation, the method of subjective fuzzy clustering and quartile difference is adopted to determine the partition threshold. The power consumption data of end-users are divided into three regions: high, normal and low, which can be used to screen users in the area of abnormal power consumption. Then the trend judgment method is used to further accurately screen to improve the accuracy and reduce the number of users in the abnormal range. Finally according to abnormal power consumption auxiliary locate abnormal electricity users list box. Then the simulation environment is set to verify the application of membership fuzzy clustering and trend judgment in power consumption data partition. © 2022, Tech Science Press. All rights reserved.

引用

页码：755 / 765

页数：10

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