Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security. Issue 4 (15th April 2020)

• Record Type: Journal Article
• Authors: Ruben, Cody; Dhulipala, Surya; Nagaraj, Keerthiraj; Zou, Sheng; Starke, Allen; Bretas, Arturo; Zare, Alina; McNair, Janise
• Is Part Of: IET smart grid. Volume 3:Issue 4(2020)
• Page Start: 445
• Page End: 453
• Publication Date: 2020-04-15
• Subjects: sensor fusion -- power engineering computing -- power grids -- power system state estimation -- smart power grids -- security of data -- power system security -- state estimation -- telecommunication security; FDI detection -- data‐driven anomaly detection methods -- physics model‐based bad data detection -- data fusion -- multiple anomaly detection methods -- IET Smart Grid -- hybrid data‐driven physics model -- enhanced cyber‐physical -- real‐time monitoring -- power grid transitions -- smart grid technology -- cyber‐attacks -- false data injections -- smart grids cyber‐physical security -- FDI attacks -- companion statistical modelling error -- coordinated FDI attack; B0240Z Other topics in statistics -- B0260 Optimisation techniques -- C1140Z Other topics in statistics -- C6130S Data security -- C7410B Power engineering computing; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0272
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : This study 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 (FDIs). Although smart grids cyber‐physical security has an extensive scope, this study focuses on FDI attacks, which are modelled as bad data. State‐of‐the‐art strategies for FDI detection in real‐time monitoring rely on physics model‐based weighted least‐squares state estimation solution and statistical tests. This strategy is inherently vulnerable by the linear approximation and the companion statistical modelling error, which means it can be exploited by a coordinated FDI attack. In order to enhance the robustness of FDI detection, this study presents a framework which explores the use of data‐driven anomaly detection methods in conjunction with physics model‐based bad data detection via data fusion. Multiple anomaly detection methods working at both the system level and distributed local detection level are fused. The fusion takes into consideration the confidence of the various anomaly detection methods to provide the best overall detection results. Validation considers tests on the IEEE 118‐bus system.