Open AccessQuickest attack detection in smart grid based on sequential Monte Carlo filtering
Author(s) -
Chen Leian,
Wang Xiaodong
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0320
Subject(s) - columbia university , computer science , smart grid , chen , library science , operations research , electrical engineering , engineering , paleontology , sociology , media studies , biology
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.