CHIMERA: A Hybrid Estimation Approach to Limit the Effects of False Data Injection Attacks

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.