ENSEMBLE CORRDET WITH ADAPTIVE STATISTICS FOR BAD DATA DETECTION

Abstract:

Smart grid (SG) systems are designed to leverage digital automation technologies for monitoring, control and analysis. As SG technology is implemented in increasing numbers 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 datadriven 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 paper, 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

Links:

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Citation:

K. Nagaraj, S. Zou, C. Ruben, S. C. Dhulipala, A. Starke, A. Bretas, A. Zare and J. McNair, "Ensemble CorrDet with Adaptive Statistics for Bad Data Detection," in IET Smart Grid. In Press.
@Article{Nagaraj2020CorrDet,
Title = {Ensemble CorrDet with Adaptive Statistics for Bad Data Detection }, 
Author = {Nagaraj, Keerthiraj and Zou, Sheng and Ruben, Cody and Dhulipala, Surya and Starke, Allen and Bretas, Arturo and Zare, Alina and McNair, Janise},  
Journal = {IET Smart Grid}, 
Volume = {},  
Year = {In Press},  
Publisher ={Institution of Engineering and Technology},  
Copyright = {This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)},  
url = {https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2020.0029},  
}