Mitigating the Effect of FDI Attacks on State Estimation in the Smart Grid

The integration of Information and Communication Technology (ICT) in Electric Power Systems (EPS) enhances monitoring and control. Wide Area Monitoring Systems (WAMSs) play a key role in grid security. In smart grids, State Estimation (SE) is often performed using Kalman Filters (KFs) to deal with noisy measurements and detect bad data due to failures in the system. Attackers can exploit the fact that SE processes the data and manipulate the sensor data which can mislead SE and trigger incorrect control actions. In this paper, we show how False Data Injection (FDI) attacks on Phasor Measurement Unit (PMU) data affect SE. In addition, we analyze the performance of residual-based detection, Kullback-Leibler divergence (KLD) for distribution-based analysis, and Weighted Voting as a combination method for mitigating the effect of FDI attacks on SE.