Tag: data-driven

Abstract: This paper presents a cross-layer strategy for detecting a variety of potential cyber-attacks on the Smart Grid. While most literature focus on False Data Injection attacks on the measurements taken throughout the Smart Grid, there are many ways in which an attacker can affect power system real-time operation. Namely, an attacker can focus on […]

Congratulations to our labmates and collaborators, Keerthiraj Nagaraj, Sheng Zou, Cody Ruben, Surya Dhulipala, Allen Starke, Arturo Bretas, Alina Zare , and Janise McNair!  Their paper, “Ensemble CorrDet with Adaptive Statistics for Bad Data Detection,” was accepted to IET Smart Grid.   The online publication can be found here. Check it out!

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 […]

Abstract: This paper presents a hybrid data-driven physics model-based framework for real time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it’s real time monitoring becomes more vulnerable to cyber attacks like false data injections (FDI). Although smart grids cyber-physical security has an extensive scope, this paper […]