A Highly Discriminative Detector Against False Data Injection Attacks in AC State Estimation
A Highly Discriminative Detector Against False Data Injection Attacks in AC State Estimation
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DOI：
10.1109/tsg.2022.3141803
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发表时间：
2022-05-01
影响因子：
9.6
通讯作者：
Yan, Jun
中科院分区：
文献类型：
--
作者：
Cheng, Gang;Lin, Yuzhang;Yan, Jun
虚假数据注入攻击（FDIAs）可以绕过传统的坏数据检测方法。最近发展的基于测量值的统计一致性的FDIA检测方法可能在错误数据不明显偏离历史趋势时无法有效工作。他们还可能错误地将实际的电网事件视为外国直接投资。本文提出了一种高度判别的FDIA检测器，即k-最小残差相似度（kSRS）检验。该方法基于在交流状态估计中很难获得完美的FDIAs，而现实世界中的不完美FDIAs总是导致测量残差的概率分布发生微妙变化的基本原理。因此，可以仔细描绘测量残差的统计一致性，以检测交流状态估计中的实际fdia。本文采用Jensen-Shannon距离（JSD）来精确量化测量残差分布的相似性。在IEEE 30总线系统上的仿真结果表明，在现有方法无法达到理想检测效果的各种情况下，该方法均能实现较高的检测率和较低的虚报率。
False data injection attacks (FDIAs) can bypass conventional bad data detection methods. Recently developed FDIA detection methods based on statistical consistency of measurement values may not work effectively when false data do not significantly deviate from historical trends. They may also mistakenly treat actual power grid events as FDIAs. In this paper, a highly discriminative FDIA detector named the k-smallest residual similarity (kSRS) test is proposed. The method is based on the rationale that perfect FDIAs can hardly be achieved in AC state estimation, and real-world imperfect FDIAs always lead to subtle changes in the probability distributions of measurement residuals. Therefore, the statistical consistency of measurement residuals can be carefully portrayed to detect practical FDIAs in AC state estimation. Herein, the Jensen-Shannon distance (JSD) is used to precisely quantify the similarity of measurement residual distributions. Simulations on the IEEE 30-bus system demonstrate that the proposed method can achieve high detection rates and low false alarm rates under a variety of conditions where existing methods do not yield satisfactory results.