DocumentCode :
648074
Title :
False data injection attacks against nonlinear state estimation in smart power grids
Author :
Rahman, Md Arifur ; Mohsenian-Rad, Hamed
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Tech Univ., Lubbock, TX, USA
fYear :
2013
fDate :
21-25 July 2013
Firstpage :
1
Lastpage :
5
Abstract :
False data injection attacks are recently introduced as a class of cyber attacks against smart grid´s monitoring systems. They aim to compromise the readings of grid sensors and phasor measurement units. Recent studies have shown that if the operator uses the DC, i.e., linear, state estimation to determine the current states of the power system, the attacker can adjust the attack vector such that the attack remains undetected and successfully passes the commonly used residue-based bad data detection tests. However, in this paper, we examine the possibility of implementing a false data injection attack when the operator uses the more practical AC, i.e., nonlinear, state estimation. We characterize such attacks when the attacker has perfect and imperfect knowledge of the current states of the system. To the best of our knowledge, this is the first paper to address false data injection attacks against non-linear state estimation.
Keywords :
phasor measurement; power system security; power system state estimation; smart power grids; attack vector; cyber attacks; false-data injection attacks; grid sensors; linear estimation; nonlinear state estimation; phasor measurement units; power system state estimation; residue-based bad data detection test; smart grid monitoring systems; smart power grids; Equations; Noise; Noise measurement; Sensors; State estimation; Transmission line measurements; Vectors; Smart grid security; false data injection attacks; non-linear state estimation; perfect and imperfect attacks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting (PES), 2013 IEEE
Conference_Location :
Vancouver, BC
ISSN :
1944-9925
Type :
conf
DOI :
10.1109/PESMG.2013.6672638
Filename :
6672638
Link To Document :
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