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

Abstract

The reliable operation of power grid 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 outages and prepare for system failures. However, false data injection attacks (FDIAs) have demonstrated the possibility of compromising sensor measurements and falsifying the estimated power system states. As a result, FDIAs may mislead system operations and other EMS applications including contingency analysis and optimal power flow. In this paper, we assess the effect of FDIAs and demonstrate that such attacks can affect the resulted number of contingencies. In order to mitigate the FDIA impact, 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 cases in which FDIAs can maliciously modify the contingencies.