基于信息物理双侧数据的配电网CPS窃电检测方法

doi:10.3969/j.issn.2097-0706.2024.05.003

综合智慧能源 ›› 2024, Vol. 46 ›› Issue (5): 20-29.doi: 10.3969/j.issn.2097-0706.2024.05.003

• 5G通信环境及数据检测 • 上一篇下一篇

基于信息物理双侧数据的配电网CPS窃电检测方法

杜龙¹(), 沙建秀¹(), 樊贝¹(), 胡静威²^,^*(), 刘增稷²()

1.国网江苏省电力有限公司宿迁供电分公司，江苏宿迁 223800
2.南京邮电大学自动化学院人工智能学院，南京 210023

收稿日期:2024-04-01 修回日期:2024-05-15 出版日期:2024-05-25
通讯作者: *胡静威（2000），女，硕士生，从事电力系统网络安全方面的研究，1628552013@qq.com。
*胡静威（2000），女，硕士生，从事电力系统网络安全方面的研究，1628552013@qq.com。
作者简介:杜龙（1995），男，工程师，硕士，从事电网调度运行工作，1260041171@qq.com；
沙建秀（1988），女，工程师，从事电网调度运行工作，shajianxiu@163.com；
樊贝（1985），男，高级工程师，硕士，从事电网调度运行工作，fanbei0703@163.com；
刘增稷（1993），男，博士生，从事电力系统网络安全方面的研究，liuzengji_njupt@163.com
基金资助:
国家自然科学基金项目(62302234)

Electricity theft detection method for distribution network CPS based on cyber and physical data

DU Long¹(), SHA Jianxiu¹(), FAN Bei¹(), HU Jingwei²^,^*(), LIU Zengji²()

1. Suqian Power Supply Branch of State Grid Jiangsu Electric Power Company Limited， Suqian 223800， China
2. College of Automation & College of Artificial Intelligence， Nanjing University of Posts and Telecommunications， Nanjing 210023， China

Received:2024-04-01 Revised:2024-05-15 Published:2024-05-25
Supported by:
National Natural Science Foundation of China(62302234)

摘要/Abstract

摘要：

高比例量测设备的广泛应用为配电网信息物理系统（CPS）带来了大量信息侧网络攻击风险，其中以获取经济利益为目的的窃电攻击严重危害了电力公司的应得利益。现有窃电检测方法中，数据驱动方法与模型驱动方法单独应用时都存在一定的局限性，无法有效降低输出结果的误报率。提出了一种基于信息物理双侧数据的配电网CPS窃电检测方法。首先对配电台区物理侧量测的线损序列进行监测，确定异常用电行为的时段；其次将配电台区信息侧用户传输的多参数用电数据以周为单位送入卷积神经网络进行疑似窃电用户排查；最后利用时间距离加权皮尔逊相关性算法，根据窃电时段内的台区线损序列和窃电用户电力消耗数据序列之间存在的负相关性，对窃电检测模型的输出结果进行窃电嫌疑二次筛查。基于IEEE 33节点系统对配电网运行过程进行仿真，输出用于分析和构建检测方法的正异常用户电力消耗多参数数据。对比试验结果表明，与其他方法相比，该检测方法具有可靠性更高、可解释性更强的窃电判据，能够在提高准确率的同时进一步降低数据驱动模型检测结果的误报率。

关键词: 配电网信息物理系统, 窃电检测, 网络安全, 卷积神经网络, 二次筛查, 误报率

Abstract:

Widespread applications of massive measurement equipment have raised frequent cyber attacks to the information side of a distribution network cyber and physical system（CPS）. The attacks aiming at encroaching on power companies' economic benefits through electricity thefts seriously endanger their interests. However， existing methods for detecting electricity theft including both data-driven algorithms and model-driven algorithms show limitations in their standalone applications， incapable of reducing false alarm rates of output results effectively. Therefore， an electricity theft detection method based on bilateral data of a distribution network CPS is proposed. Firstly， the method monitors the abnormal fluctuations of the line loss sequence measured on the physical side of the distribution station area， determining the time periods with abnormal electricity consumptions. Secondly， various electricity consumption data from users on the information side of the distribution station area are sent to the convolutional neural network on a weekly basis to investigate suspected electricity thieves. Finally， a second screening on electricity thefts is carried out on the outputs of the electricity stealing detection model based on the negative correlation between the line loss sequence in the station area and the electricity consumption data sequence of the electricity thieves obtained by the time distance weighted Pearson correlation algorithm. The operation process of this method is performed on an IEEE 33 bus system， obtaining normal and abnormal electricity consumption data for following analyses and detection model construction. The results of the comparative experiments verify that， compared to other methods， the proposed electricity theft detection method has a higher reliability and more solid criterion， which can further reduce the false alarm rate of data-driven model detection results while improving accuracy.

Key words: CPS for distribution network, electricity theft detection, cyber security, convolutional neural network, second screening, false alarm rate

中图分类号:

TK012：TM727

杜龙, 沙建秀, 樊贝, 胡静威, 刘增稷. 基于信息物理双侧数据的配电网CPS窃电检测方法[J]. 综合智慧能源, 2024, 46(5): 20-29.

DU Long, SHA Jianxiu, FAN Bei, HU Jingwei, LIU Zengji. Electricity theft detection method for distribution network CPS based on cyber and physical data[J]. Integrated Intelligent Energy, 2024, 46(5): 20-29.

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计量设备	采集变量
配电台区智能终端	A/B/C相电压幅值 A/B/C相电流幅值 A/B/C相及总有功、无功功率 A/B/C相及总功率因数正向、反向有功、无功总电量示值
用户智能电表	A/B/C相电压幅值 A/B/C相电流幅值 A/B/C相及总有功、无功功率 A/B/C相及总功率因数正向有功总电量示值

用户类别	判断为正常的用户	判断为窃电的用户
标签为正常的用户	TN	FP
标签为窃电的用户	FN	TP

窃电检测模型	R_A	R_recall	A_UC	R_FA
LR	0.712	0.758	0.689	0.319
RF	0.768	0.784	0.728	0.302
SVM	0.735	0.761	0.716	0.311
本文模型	0.834	0.863	0.793	0.240

窃电检测模型	R_A	R_recall	A_UC	R_FA
LR	0.748	0.769	0.705	0.251
RF	0.774	0.786	0.742	0.217
SVM	0.785	0.794	0.751	0.210
本文模型	0.896	0.917	0.874	0.132

台区	总用户	正常用户	窃电用户
1	128	128	0
2	128	116	12
3	128	116	12
4	128	128	0

基于信息物理双侧数据的配电网CPS窃电检测方法

Electricity theft detection method for distribution network CPS based on cyber and physical data

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参考文献 27

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台区	总用户数量	实际正/异常数量	窃电检测模型下正/异常数量
1	128	128/0	128/0
2	128	116/12	115/13
3	128	116/12	114/14
4	128	128/0	126/2

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台区	线损/kW	最高线损率/%	最高线损波动率/%	异常数据点
1	378	4	2
2	1 209	13	9	863—1 151
3	1 396	15	11	1 179—1 467
4	425	5	3