Quickest attack detection in smart grid based on sequential Monte Carlo filtering

Abstract

Quick and accurate detection of cyber-attacks is key to the normal operation of the smart grid system. In this study, joint state estimation and sequential attack detection method for a given bus with grid frequency drift is proposed that utilises the commonly monitored output voltage. In particular, based on a non-linear state-space model derived from the three-phase sinusoidal voltage equations, the authors employ the sequential Monte Carlo (SMC) filtering to estimate the system state. The output of the SMC filter is fed into a cumulative sum control chart test to detect the attack in the fastest way. Moreover, an adaptive sampling strategy is proposed to reduce the rate of taking measurements and communicating with the controller. Extensive simulation results demonstrate that the proposed method achieves high adaptivity and efficient detection of various types of attacks in power systems.

Keywords

• estimation theory
• computer network security
• filtering theory
• state-space methods
• statistical distributions
• state estimation
• monte carlo methods
• smart power grids
• control charts
• power systems
• quickest attack detection
• sequential monte carlo filtering
• cyber-attacks
• normal operation
• smart grid system
• joint state estimation
• sequential attack detection method
• given bus
• grid frequency drift
• commonly monitored output voltage
• state-space model
• three-phase sinusoidal voltage equations
• system state
• smc filter
• cumulative sum control chart test
• adaptive sampling strategy