Detection and Estimation of False Data Injection Attacks for Load Frequency Control Systems

Abstract

False data injection attacks (FDIAs) against the load frequency control (LFC) system can lead to unstable operation of power systems. In this paper, the problems of detecting and estimating the FDIAs for the LFC system in the presence of external disturbances are investigated. First, the LFC system model with FDIAs against frequency and tie-line power measurements is established. Then, a design procedure for the unknown input observer (UIO) is presented and the residual signal is generated to detect the FDIAs. The UIO is designed to decouple the effect of the unknown external disturbance on the residual signal. After that, an attack estimation method based on a robust adaptive observer (RAO) is proposed to estimate the state and the FDIAs simultaneously. In order to improve the performance of attack estimation, the <tex>$H_{\infty}$</tex> technique is employed to minimize the effect of external disturbance on estimation errors, and the uniform boundedness of the state and attack estimation errors is proven using Lyapunov stability theory. Finally, a two-area interconnected power system is simulated to demonstrate the effectiveness of the proposed attack detection and estimation algorithms.