Title :
On the nonlinearity effects on malicious data attack on power system
Author :
Liyan Jia ; Thomas, R.J. ; Lang Tong
Author_Institution :
Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
Abstract :
There has been a growing literature on the malicious data attack (or data injection attack) on power systems. Most existing work focuses on the DC (linear) model with linear state estimators. This paper examines the effects of nonlinearity in the power systems on the effectiveness of malicious data attack on state estimation and real-time market. It is demonstrated that attack algorithms designed for the DC model may not be effective when they are applied to nonlinear system with nonlinear state estimators. Discussion and experiments results about nonlinearity are provided.
Keywords :
power markets; power system security; power system simulation; DC model; data injection attack; linear model; malicious data attack; nonlinear state estimators; nonlinear system; nonlinearity effects; power systems nonlinearity; real-time market; state estimation; Detectors; Electricity supply industry; Nonlinear systems; Power systems; Real-time systems; State estimation; Vectors; Bad Data Detection; Electricity Market; Malicious Data Attack; Nonlinear System; State Estimation;
Conference_Titel :
Power and Energy Society General Meeting, 2012 IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4673-2727-5
Electronic_ISBN :
1944-9925
DOI :
10.1109/PESGM.2012.6345685