  State estimation is a data processing algorithm for converting redundant
meter measurements and other information into an estimate of the state of a
power system. Relying heavily on meter measurements, state estimation has
proven to be vulnerable to cyber attacks. In this paper, a novel targeted false
data injection attack (FDIA) model against AC state estimation is proposed.
Leveraging on the intrinsic load dynamics in ambient conditions and important
properties of the Ornstein-Uhlenbeck process, we, from the viewpoint of
intruders, design an algorithm to extract power network parameters purely from
PMU data, which are further used to construct the FDIA vector. Requiring no
network parameters and relying only on limited phasor measurement unit (PMU)
data, the proposed FDIA model can target specific states and launch large
deviation attacks. Sufficient conditions for the proposed FDIA model are also
developed. Various attack vectors and attacking regions are studied in the IEEE
39-bus system, showing that the proposed FDIA method can successfully bypass
the bad data detection and launch targeted large deviation attacks with very
high probabilities.
