Globally Stealthy Attacks Against Distributed State Estimation in Smart Grid

被引:1
|
作者
Xu, Shaojie [1 ]
Ye, Dan [1 ,2 ]
Li, Guangdi [1 ]
Yang, Dongsheng [1 ]
机构
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
[2] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China
基金
中国国家自然科学基金;
关键词
State estimation; Power system stability; Smart grids; Power systems; Load modeling; Distributed databases; Detectors; Smart grid; distributed state estimation; stealthy false data injection attacks;
D O I
10.1109/TASE.2024.3364231
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the continuous expansion of grid nodes, traditional centralized methods exhibit certain limitations in the amount of communication data and computational cost for state estimation. In the past few decades, distributed state estimation has been fully developed in multi-area smart grid (SG). According to different areas or electrical equipment, the SG is divided into several nodes, and each node can estimate the entire state of the SG through local sensor measurement information and neighbor state estimation information. Although malicious attackers can use the different information between local sensor data and neighbor state estimation communication data to attack grid nodes, it will inevitably cause the attack detectors of other nodes to trigger alarms due to the consistency of grid state estimation. In this paper, we propose a globally stealthy attack strategy that biases the state estimation of the attacked node without triggering alarms on all nodes' detectors. We provide sufficient and necessary conditions to implement a globally stealthy attack on grid nodes and extend it to multi-node attacks in order to reduce the number of communication links and increase the degree of damage. We also give the corresponding attack algorithm to realize these attacks. Finally, our proposed globally stealthy attack methods are simulated and verified in IEEE 39-bus power system. .
引用
收藏
页码:1 / 11
页数:11
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