Ensemble CorrDet with adaptive statistics for bad data detection
Ensemble CorrDet with adaptive statistics for bad data detection
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DOI：
10.1049/iet-stg.2020.0029
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发表时间：
2020-06
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影响因子：
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通讯作者：
Keerthiraj Nagaraj;Sheng Zou;Cody Ruben;S. Dhulipala;Allen Starke;A. Bretas;A. Zare;J. Mcnair
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文献类型：
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作者：
Keerthiraj Nagaraj;Sheng Zou;Cody Ruben;S. Dhulipala;Allen Starke;A. Bretas;A. Zare;J. Mcnair
智能电网(SG)系统旨在利用数字自动化技术进行监测、控制和分析。随着SG技术在越来越多的电力系统中的应用，SG数据越来越容易受到网络攻击。传统的基于分析物理模型的不良数据检测方法可能无法检测到这些攻击。最近，物理模型和数据驱动的方法被提出利用数据的时间方面来学习电网的多元统计量，如电压、潮流的均值和协方差矩阵等，然后基于这些统计量的固定值做出决策。但是，随着系统中的负载和发电量发生变化，这些统计数据可能会迅速更改。提出了一种自适应数据驱动的异常检测框架--集成自适应统计模型(ECD-AS)，用于检测不断变化的系统状态下的虚假数据注入网络攻击。ECD-AS在IEEE 118总线系统上针对15组不同的训练和测试数据集进行了测试，以用于各种当前最先进的不良数据检测策略。实验结果表明，所提出的ECD-AS解决方案的性能优于相关策略，因为它具有捕获和解释SG中快速变化的统计数据的独特能力。
Smart grid (SG) systems are designed to leverage digital automation technologies for monitoring, control and analysis. As SG technology is implemented in increasing number of power systems, SG data becomes increasingly vulnerable to cyber-attacks. Classic analytic physics-model based bad data detection methods may not detect these attacks. Recently, physics-model and data-driven methods have been proposed to use the temporal aspect of the data to learn multivariate statistics of the SG such as mean and covariance matrices of voltages, power flows etc., and then make decisions based on fixed values of these statistics. However, as loads and generation change within a system, these statistics may change rapidly. In this study, an adaptive data-driven anomaly detection framework, Ensemble CorrDet with Adaptive Statistics (ECD-AS), is proposed to detect false data injection cyber-attacks under a constantly changing system state. ECD-AS is tested on the IEEE 118-bus system for 15 different sets of training and test datasets for a variety of current state-of-the-art bad data detection strategies. Experimental results show that the proposed ECD-AS solution outperforms the related strategies due to its unique ability to capture and account for rapidly changing statistics in SG.