Assessment of Overloading Correlations Among Transmission Lines Under Load Redistribution Attacks

被引:13
作者
Gao, Shibin [1 ]
Lei, Jieyu [1 ]
Shi, Jian [2 ]
Wei, Xiaoguang [1 ]
Dong, Ming [3 ]
Han, Zhu [4 ,5 ]
机构
[1] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 611756, Peoples R China
[2] Univ Houston, Dept Engn Technol, Houston, TX 77004 USA
[3] Alberta Elect Syst Operator, Calgary, AB T2P 0L4, Canada
[4] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA
[5] Kyung Hee Univ, Dept Comp Sci & Engn, Seoul 446701, South Korea
基金
中国国家自然科学基金;
关键词
Transmission line measurements; Security; Power transmission lines; Power measurement; State estimation; Pollution measurement; Correlation; Line overloading; power system operation; cyber-physical power systems; load redistribution attacks; DATA INJECTION ATTACKS; POWER-SYSTEMS; PROTECTION;
D O I
10.1109/TSG.2021.3134306
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a systematic approach to explicitly quantify and evaluate the overloading associations among lines under load redistribution (LR) attacks. We define overloading associations as a measure of the statistical correlation between two sets of lines in terms of their susceptibility to simultaneous overloading. We then show how overloading associations can be obtained to capture the patterns of simultaneous line overloading potentially induced by malicious data manipulation and assess the network's security risk in the face of the LR attack. Furthermore, we develop a novel priority line selection approach to identify key network components that are crucial for the system-level propagation of line overloading, based on which effective defensive insights can be obtained to protect the system from the severe damaging effects of LR attacks. The effectiveness of the proposed approach is validated on the IEEE 118-bus system. Simulation results show that the proposed approach is capable of revealing the properties of simultaneous line overloading in the network and supporting decision-making from both the attacker's and the defender's perspectives.
引用
收藏
页码:1570 / 1581
页数:12
相关论文
共 31 条
[1]   Minimax-Regret Robust Defensive Strategy Against False Data Injection Attacks [J].
Abusorrah, Abdullah ;
Alabdulwahab, Ahmed ;
Li, Zhiyi ;
Shahidehpour, Mohammad .
IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (02) :2068-2079
[2]   Cyber-Physical Attack-Resilient Wide-Area Monitoring, Protection, and Control for the Power Grid [J].
Ashok, Aditya ;
Govindarasu, Manimaran ;
Wang, Jianhui .
PROCEEDINGS OF THE IEEE, 2017, 105 (07) :1389-1407
[3]   Fast Screening of High-Risk Lines Under False Data Injection Attacks [J].
Che, Liang ;
Liu, Xuan ;
Li, Zuyi .
IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (04) :4003-4014
[4]   False Data Injection Attacks Induced Sequential Outages in Power Systems [J].
Che, Liang ;
Liu, Xuan ;
Li, Zuyi ;
Wen, Yunfeng .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2019, 34 (02) :1513-1523
[5]   Cyber Cascades Screening Considering the Impacts of False Data Injection Attacks [J].
Che, Liang ;
Liu, Xuan ;
Shuai, Zhikang ;
Li, Zuyi ;
Wen, Yunfeng .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (06) :6545-6556
[6]   Mitigating False Data Attacks Induced Overloads Using a Corrective Dispatch Scheme [J].
Che, Liang ;
Liu, Xuan ;
Li, Zuyi .
IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (03) :3081-3091
[7]   A modified super-efficiency measure based on simultaneous input-output projection in data envelopment analysis [J].
Chen, Jin-Xiao ;
Deng, Mingrong ;
Gingras, Sylvain .
COMPUTERS & OPERATIONS RESEARCH, 2011, 38 (02) :496-504
[8]   Defending Against False Data Injection Attacks on Power System State Estimation [J].
Deng, Ruilong ;
Xiao, Gaoxi ;
Lu, Rongxing .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017, 13 (01) :198-207
[9]   Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid [J].
Esmalifalak, Mohammad ;
Liu, Lanchao ;
Nguyen, Nam ;
Zheng, Rong ;
Han, Zhu .
IEEE SYSTEMS JOURNAL, 2017, 11 (03) :1644-1652
[10]   A Stealthy Attack Against Electricity Market Using Independent Component Analysis [J].
Esmalifalak, Mohammad ;
Huy Nguyen ;
Zheng, Rong ;
Xie, Le ;
Song, Lingyang ;
Han, Zhu .
IEEE SYSTEMS JOURNAL, 2018, 12 (01) :297-307