Hybrid data-driven physics model-based framework for enhanced cyber-physical smart grid security

Hybrid data-driven physics model-based framework for enhanced cyber-physical smart grid security

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...

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Bibliographic Details
Main Authors: Cody Ruben, Surya Dhulipala, Keerthiraj Nagaraj, Sheng Zou, Allen Starke, Arturo Bretas, Alina Zare, Janise McNair
Format: Article
Language:English
Published: Wiley 2019-12-01
Series:IET Smart Grid
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
Online Access:https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0272

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https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0272

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