Detection Methods in Smart Meters for Electricity Thefts: A Survey | |
Xia XF(夏小芳)3,4; Xiao Y(肖杨)4; Liang W(梁炜)1,2,5; Cui JT(崔江涛)3 | |
刊名 | PROCEEDINGS OF THE IEEE |
2022 | |
页码 | 1-47 |
关键词 | Power systems Companies Meters Smart grids Meter reading Electric variables measurement Distribution networks Binary trees cyber-physical systems detection methods electricity theft Internet of Things (IoT) machine learning measurement mismatch security smart grid smart meters |
ISSN号 | 0018-9219 |
产权排序 | 3 |
英文摘要 | For accommodating rapidly increasing power demands, power systems are transitioning from analog systems to systems with increasing digital control and communications. Although this modernization brings many far-reaching benefits, the hardware and software newly incorporated into the power systems also incur many vulnerabilities. By taking advantage of these vulnerabilities, adversaries can launch various cyber/physical attacks to tamper with electricity meter readings, i.e., to steal electricity. It is reported that total worldwide annual economic losses caused by electricity theft reached up to almost one hundred billion dollars in recent years. With methods to tamper with meter readings becoming more versatile, secret, and flexible, electricity theft tends to get even more serious in modernized power systems. For preventing adversaries from stealing electricity, researchers have done a lot of works. Although some related surveys on these works exist, they are not updated or just discuss electricity theft in a specific region. This survey aims to gain a comprehensive and in-depth understanding of the electricity theft issue. After investigating how adversaries tamper with meter readings, we systematically survey all existing detection methods up to date, which is classified into machine learning- and measurement mismatch-based methods. Adverse effects and political and socioeconomic factors of electricity theft are also provided. This survey can help relevant researchers to shape future research directions, especially in the area of developing new effective electricity theft detection methods. |
资助项目 | Special Fund for Strategic Pilot Technology of Chinese Academy of Sciences[XDC02020600] ; National Key Research and Development Program[2019YFB1707401] ; National Key Research and Development Program[2021YFB3301001] ; National Natural Science Foundation of China (NSFC)[61902299] ; National Natural Science Foundation of China (NSFC)[61976168] ; National Natural Science Foundation of China (NSFC)[62022088] ; Liaoning Provincial Natural Science Foundation of China[2020JH2/10500002] ; Liaoning Provincial Natural Science Foundation of China[2019-YQ-09] ; Liaoning Revitalization Talents Program[XLYC1902110] ; Natural Science Basic Research Program of Shaanxi Province[2019CGXNG-023] ; S&T Program of Hebei[20310102D] ; International Partnership Program of the Chinese Academy of Sciences[173321KYSB20200002] ; China Postdoctoral Science Foundation[2019TQ0239] ; China Postdoctoral Science Foundation[2019M663636] |
WOS关键词 | NONTECHNICAL LOSS DETECTION ; HOME AREA NETWORK ; ENERGY THEFT ; LEARNING TECHNIQUES ; MALICIOUS METERS ; STATE ESTIMATION ; LOSS FRAUD ; LOSSES ; INSPECTION ; ALGORITHM |
WOS研究方向 | Engineering |
语种 | 英语 |
WOS记录号 | WOS:000745508300001 |
资助机构 | Special Fund for Strategic Pilot Technology of Chinese Academy of Sciences [XDC02020600] ; National Key Research and Development Program [2019YFB1707401, 2021YFB3301001] ; National Natural Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [61902299, 61976168, 62022088] ; Liaoning Provincial Natural Science Foundation of China [2020JH2/10500002, 2019-YQ-09] ; Liaoning Revitalization Talents Program [XLYC1902110] ; Natural Science Basic Research Program of Shaanxi Province [2019CGXNG-023] ; S&T Program of Hebei [20310102D] ; International Partnership Program of the Chinese Academy of Sciences [173321KYSB20200002] ; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2019TQ0239, 2019M663636] |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/30309] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Xiao Y(肖杨); Cui JT(崔江涛) |
作者单位 | 1.Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 3.School of Computer Science and Technology, Xidian University, Xi’an 710071, China 4.Department of Computer Science, The University of Alabama, Tuscaloosa, AL 35487-0290 USA 5.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
推荐引用方式 GB/T 7714 | Xia XF,Xiao Y,Liang W,et al. Detection Methods in Smart Meters for Electricity Thefts: A Survey[J]. PROCEEDINGS OF THE IEEE,2022:1-47. |
APA | Xia XF,Xiao Y,Liang W,&Cui JT.(2022).Detection Methods in Smart Meters for Electricity Thefts: A Survey.PROCEEDINGS OF THE IEEE,1-47. |
MLA | Xia XF,et al."Detection Methods in Smart Meters for Electricity Thefts: A Survey".PROCEEDINGS OF THE IEEE (2022):1-47. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论