基于边缘计算的电表计量系统数据协同检测方案

doi:10.11930/j.issn.1004-9649.201904049

中国电力 ›› 2019, Vol. 52 ›› Issue (11): 145-152.DOI: 10.11930/j.issn.1004-9649.201904049

基于边缘计算的电表计量系统数据协同检测方案

钱斌¹, 蔡梓文¹, 肖勇¹, 杨劲锋², 董歆滢³, 谷科³

1. 南方电网科学研究院, 广东广州 530100;
2. 中国南方电网有限公司, 广东广州 530100;
3. 长沙理工大学计算机与通信工程学院, 湖南长沙 410114

收稿日期:2019-04-11 修回日期:2019-07-27 出版日期:2019-11-05 发布日期:2019-11-05
通讯作者: 董歆滢(1995-),女,通信作者,硕士研究生,从事网络安全研究,E-mail:296014320@qq.com
作者简介:钱斌(1989-),男,高级工程师,从事智能用电与计量技术研究,E-mail:qianbin@csg.cn
基金资助:
南方电网科技项目（ZNKJXM20170085）；国家自然科学基金资助项目（51777015）

Data Collaborative Detection Scheme of Electric Metering System Based on Edge Computing

QIAN Bin¹, CAI Ziwen¹, XIAO Yong¹, YANG Jinfeng², DONG Xingyin³, GU Ke³

1. Electric Power Research Institute of China Southern Power Grid, Guangzhou 510080, China;
2. China Southern Power Grid Co., Ltd., Guangzhou 510080, China;
3. College of Computer and Communication, Changsha University of Science and Technology, Changsha 410114, China

Received:2019-04-11 Revised:2019-07-27 Online:2019-11-05 Published:2019-11-05
Supported by:
This work is supported by Science & Technology Project of CSG (No.ZNKJXM20170085) and National Natural Science Foundation (No.51777015)

摘要/Abstract

摘要： 虚假数据注入攻击以破坏电力数据采集与监控系统的数据完整性和可用性为目标，其检测方案对智能电网的安全与稳定运行具有重要意义。针对智能电网中庞大的数据带来的许多安全问题和漏洞，提出了基于边缘计算的一种虚假数据注入攻击协同检测模型，该模型基于边缘计算的框架，改进传统计量系统的网络结构，利用边缘服务器来提供全局的监控能力和虚假数据检测功能。该方案针对计量系统中的虚假数据注入问题不仅提出了检测规则，还对采集器进行了信誉评分。该方案在测试系统上进行了仿真实验，验证了该模型的有效性。

关键词: 智能电网, 边缘计算, 虚假数据, 注入攻击, 信誉评分, 电表计量系统

Abstract: False data injection attacks are aimed at destroying the integrity and availability of power data in electric metering system. The false data detection scheme is of great significance to the secure and stable operation of smart grid. In order to address many security problems caused by false data in smart grid, this paper proposes a collaborative detection model of false data injection attacks based on edge computing, which improves the network structures of traditional metering system and provides the capability of global monitoring and detection function for false data by edge servers in smart grid. The scheme not only proposes some rules of detection for false data injection in electric metering system, but also constructs the reputation mechanism for data collectors. The simulation experiments have proved the validity of our proposed scheme.

Key words: smart grid, edge computing, false data, injection attacks, reputation mechanism, meter metering system

中图分类号:

TM76

钱斌, 蔡梓文, 肖勇, 杨劲锋, 董歆滢, 谷科. 基于边缘计算的电表计量系统数据协同检测方案[J]. 中国电力, 2019, 52(11): 145-152.

QIAN Bin, CAI Ziwen, XIAO Yong, YANG Jinfeng, DONG Xingyin, GU Ke. Data Collaborative Detection Scheme of Electric Metering System Based on Edge Computing[J]. Electric Power, 2019, 52(11): 145-152.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201904049

https://www.electricpower.com.cn/CN/Y2019/V52/I11/145

参考文献

[1] FERHANGI H. The path of the smart grid[J]. IEEE Power and Energy Magazine, 2010, 8(1):18-28.
[2] FANG X, MISRA S, XUE G, et al. Smart grid-the new and improved power grid:a survey[J]. IEEE Communications Surveys & Tutorials, 2012, 14(4):944-980.
[3] YIGIT M, GUNGOR V. Cloud computing for smart grid applications[J]. Computer Networks, 2014, 70(9):312-329.
[4] 田秀霞, 陈希, 田福粮. 基于区块链的社区分布式电能安全交易平台方案[J]. 信息网络安全, 2019(1):51-58 TIAN Xiuxia, CHEN Xi, TIAN Fuliang. Community distributed power security trading platform scheme based on block chain[J]. Information Network Security, 2019(1):51-58
[5] 李梁, 张应辉, 邓恺鑫, 等. 5G智能电网中具有隐私保护的电力注入系统[J]. 信息网络安全, 2018(12):87-92 LI Liang, ZHANG Yinghui, DENG Kaixin, et al. Power injection system with privacy protection in 5G smart grid[J]. Information Network Security, 2018(12):87-92
[6] 阮振, 吕林, 刘友波, 等. 考虑负荷数据虚假注入的电力信息物理系统协同攻击模型[J]. 电力自动化设备, 2019, 39(2):181-187 RUAN Zhen, LU Lin, LIU Youbo, et al. Coordinated attack model of cyber-physical power system considering false load data injection[J]. Electric Power Automation Equipment, 2019, 39(2):181-187
[7] 李永攀, 门锟, 吴俊阳. 基于双低秩约束的智能电网虚假数据检测[J]. 计算机应用, 2018, 38(增刊2):141−144. LI Yongpan, MEN Kun, WU Junyang. Dual low-rank constraint for false data detection in smart grid[J]. Journal of Computer Applications, 2018, 38(S2):141−144.
[8] 李青芯, 孙宏斌, 盛同天, 等. 变电站状态估计中互感器虚假数据注入攻击分析[J]. 电力系统自动化, 2016, 40(17):79-86 LI Qingxin, SUN Hongbin, SHENG Tongtian, et al. Injection attack analysis of transformer false data in substation state estimation[J]. Automation of Electric Power Systems, 2016, 40(17):79-86
[9] CHEN J, ABUR A P. Placement of PMUs to enable bad data detection in state estimation[J]. IEEE Transactions on Power Systems, 2006, 21(4):1608-1615.
[10] SCHWEPPE J W, ROM D B. Power system static-state estimation, parts I, II, and III[J]. IEEE Transactions on Power Apparatus and Systems, 1970, 89(1):120-135.
[11] MERRILL H M, SCHWEPPE F C. Bad data suppression in power system static state estimation[J]. IEEE Transactions on Power Apparatus and Systems, 1971, 90(6):2718-2725.
[12] HANDSCHIN E, SCHWEPPE F, KOHLAS J, et al. Bad data analysis for power system state estimation[J]. IEEE Transactions on Power Apparatus and Systems, 1975, 94(2):329-337.
[13] LUAN T H, GAO L, LI Z, et al. Fog computing:Focusing on mobile users at the edge[R]. Networking and Internet Architecture (submitted), 2015.
[14] TRUONG N, LEE G M, GHAMRI-DOUDANE Y. Software defined networking-based vehicular adhoc network with fog computing[C]//2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2015:1202−1207.
[15] ZHU J, CHAN D, PRABHU M, et al. Improving web sites performance using edge servers in fog computing architecture[C]//2013 IEEE 7th International Symposium on Service Oriented System Engineering (SOSE), 2013.
[16] HONG K, LILLETHUN D, RAMACHANDRAN U, et al. Mobile fog:a programming model for large scale applications on the internet of things[C]//Proceedings of the second ACM SIGCOMM workshop on Mobile cloud computing, ACM MCC'13, 2013.
[17] BONOMI F, MILITO R, ZHU J, et al. Fog computing and its role in the internet of things[C]//Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, ACM, 2012.
[18] KUMAR N, ZEADALLY S, RODRIGUES J P C. Vehicular delay-tolerant networks for smart grid data management using mobile edge computing[J]. IEEE Communications Magazine, 2016, 54(10):60-66.
[19] LIU J, WAN J, ZENG B, et al. A scalable and quick-response software defined vehicular network assisted by mobile edge computing[J]. IEEE Communications Magazine, 2017, 55(7):94-100.
[20] 李彬, 贾滨诚, 曹望璋, 等. 边缘计算在电力需求响应业务中的应用展望[J]. 电网技术, 2018, 42(1):79-87 LI Bin, JIA Bincheng, CAO Wangzhang, et al. Application prospect of edge computing in power demand response business[J]. Power System Technology, 2018, 42(1):79-87
[21] 祁兵, 夏琰, 李彬, 等. 基于边缘计算的家庭能源管理系统:架构、关键技术及实现方式[J]. 电力建设, 2018, 39(3):33-41 QI Bin, XIA Yan, LI Bin, et al. Family energy management system based on edge computing:architecture, key technology and implementation[J]. Electric Power Construction, 2018, 39(3):33-41
[22] 龚钢军, 罗安琴, 陈志敏, 等. 基于边缘计算的主动配电网信息物理系统[J]. 电网技术, 2018, 42(10):3128-3135 GONG Gangjun, LUO Anqin, CHEN Zhimin, et al. Cyber physical system of active distribution network based on edge computing[J]. Power System Technology, 2018, 42(10):3128-3135

基于边缘计算的电表计量系统数据协同检测方案

Data Collaborative Detection Scheme of Electric Metering System Based on Edge Computing

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	陶磊, 罗萍萍, 林济铿. 基于深度学习的直流微电网虚假数据注入攻击二阶段检测方法[J]. 中国电力, 2024, 57(9): 11-19.
[2]	席磊, 王艺晓, 何苗, 程琛, 田习龙. 基于反向鲸鱼-多隐层极限学习机的电网FDIA检测[J]. 中国电力, 2024, 57(9): 20-31.
[3]	孔志恒, 谭冲, 唐培耀, 胡成博, 郑敏. 基于Light-Resnet卷积神经网络的电力设备监测数值识别算法[J]. 中国电力, 2024, 57(8): 206-213.
[4]	魏明江, 李鹏, 于浩, 冀浩然, 宋关羽, 习伟. 数字配电网边缘计算模拟实验平台[J]. 中国电力, 2024, 57(3): 12-19.
[5]	夏向阳, 谭欣欣, 单周平, 李辉, 徐志强, 吴晋波, 岳家辉, 陈贵全. 储能电站锂离子电池本体安全关键技术及新技术应用情况[J]. 中国电力, 2024, 57(11): 1-17.
[6]	于宗超, 文明, 李湘华, 谢欣涛, 杨洪明. 融合群体智慧的分布式智能电网高效发展管理策略[J]. 中国电力, 2024, 57(10): 57-68.
[7]	吴钢, 周金辉, 李慧. 面向边缘增强分布式电力无线传感网的资源分配[J]. 中国电力, 2023, 56(8): 77-85,98.
[8]	李天楚, 容斌, 伍智鹏, 黄珏, 黄开来, 杨刚, 易杨. 基于边缘计算的风电群非故意发射超高次谐波抑制策略[J]. 中国电力, 2023, 56(8): 200-206,215.
[9]	杨晋祥, 彭勇刚, 蔡田田, 习伟, 邓清唐. 面向边缘计算的电力终端轻量级认证协议[J]. 中国电力, 2023, 56(4): 88-94.
[10]	吴岩, 王光政. 基于CiteSpace的配电网韧性评估与提升研究综述与展望[J]. 中国电力, 2023, 56(12): 100-112, 137.
[11]	徐杰, 汪石农. 面向智能电网电压崩溃路径的优化粒子群算法[J]. 中国电力, 2023, 56(1): 142-149.
[12]	孙毅, 常少南, 陈恺, 崔强, 沈维捷. 面向电工智慧物联的服务缓存和计算卸载策略[J]. 中国电力, 2022, 55(4): 23-32,43.
[13]	陆旭, 陈影, 许中平, 张海全, 慕春芳, 陈恺, 孙毅. 面向5G边缘计算网络的联合需求响应与任务卸载策略[J]. 中国电力, 2022, 55(10): 209-218.
[14]	黄冬梅, 王玥琦, 胡安铎, 孙锦中, 时帅, 孙园, 房岭锋. 融合多维度特征的绝缘子状态边缘识别方法[J]. 中国电力, 2022, 55(1): 133-141.
[15]	李峰, 彭慧敏, 袁虎玲, 万芳茹, 黄天罡, 李威. UPFC在线运行优化辅助决策应用功能设计[J]. 中国电力, 2021, 54(7): 76-82.

模态框（Modal）标题

基于边缘计算的电表计量系统数据协同检测方案

Data Collaborative Detection Scheme of Electric Metering System Based on Edge Computing

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics