Detection of false data injection attack on load frequency control in distributed power systems

The False Data Injection (FDI) attack on Load Frequency Control (LFC) caused by the adversary can destabilize the power system. This could cause potential economic and life damages. Therefore, the real-time detection of FDI attacks is necessary and essential to compensate negative effects of such attacks. This paper presents a neural network-based detection (NND) approach to estimate and detect the FDI attacks injected to sensing loop (SL) of the system. A two-area distributed power system is considered as our case study to demonstrate the effectiveness of NND strategy. The simulation results clearly show that the FDI attack can be detected and estimated in real-time with sufficient accuracy.