  The reliable operation of the electric power systems is supported by energy
management systems (EMS) that provide monitoring and control functionalities.
Contingency analysis is a critical application of EMS to evaluate the impacts
of outage events based on the grid state variables, and allow system operators
to prepare for potential system failures. However, false data injection attacks
(FDIAs) against state estimation have demonstrated the possibility of
compromising sensor measurements and consequently falsifying the estimated
power system states. As a result, FDIAs may mislead the system operations and
other EMS applications including contingency analysis and optimal power flow
routines. In this paper, we assess the effect of FDIAs on contingency analysis
and demonstrate that such attacks can affect the resulted number of
contingencies in power systems. In order to mitigate the FDIA impact on
contingency analysis algorithms, we propose CHIMERA, a hybrid attack-resilient
state estimation approach that integrates model-based and data-driven methods.
CHIMERA combines the physical grid information with a Long Short Term Memory
(LSTM)-based deep learning model by considering a static loss of weighted least
square errors and a dynamic loss of the difference between the temporal
variations of the actual and the estimated active power. Our simulation
experiments based on the load data from New York state demonstrate that CHIMERA
can effectively mitigate 91.74% of the attack cases in which FDIAs can
maliciously modify the contingency results.
