Detection of False Data Injection Attack in Power System Based on Hellinger Distance

The deep integration of sensor, computer, and communication networks has dramatically improved the efficiency and operational performance of the electric grid. However, it also exposes the grid to rising threats of cyber-physical attacks. False data injection attack (FDIA) is one of the most representative attacks. To improve the security of grid operation, we propose a Hellinger-distance-based FDIA detection method by tracking the dynamic characteristics of measurement variations at adjacent moments. First, the irrelevant components of measured data are sieved out by empirical modal decomposition. Second, the image transform algorithms are used to deal with the mapping of measurement variations to refine the distribution characteristics. Last, the discrepancies between the probability distributions are derived based on Hellinger distance to determine whether FDIA exists. Concerning state-variable attacks on different nodes, the method is tested using the IEEE 14-bus system. The results indicate that the proposed scheme has high-level detection precision for false data injection attacks.