Identification and Localization of False Data Injection Attacks in Smart Grids Using Graph Fourier Transform

Abstract

The increasing digitalization of the energy sector has enhanced efficiency in energy transfer and utilization but has also exposed smart grid operations to significant cybersecurity threats, particularly False Data Injection (FDI) attacks. These attacks manipulate measurement data within communication channels to compromise power system state estimation, thus disturbing grid reliability and security. This research proposes a detection approach based on the Graph Fourier Transform (GFT). The GFT-based method not only detects the presence of FDI attacks but also identifies the targeted buses within the power grid. The methodology uses the Degree Centrality technique to identify critical buses in the system as potential targets for FDI attacks, followed by the simulation of such attacks on these buses. The performance of the proposed approach is validated using MATLAB simulations on the IEEE 9-bus, 14-bus, 30-bus, and 118-bus system. To validate the results obtained with the proposed method, the Euclidean distance detector is also applied. The results demonstrate that the proposed GFT-based approach effectively detects and localizes FDI attacks across varying intensities, including weak, medium, and strong attacks. These findings highlight the potential of the GFT method as a robust solution for enhancing the cybersecurity of modern power systems.