DOI:10.35833/MPCE.2020.000928 Detection and Estimation of False Data Injection Attacks for Load Frequency Control Systems Page view: 291        Net amount: 338 Author: Jun Ye1,Xiang Yu2 Author Affiliation: 1.the Hangzhou Innovation Institute, Beihang University, Hangzhou, China;2.the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China Foundation: This work was supported by the National Natural Science Foundation of China (No. 61833013) and Key Research and Development Project of Zhejiang Province (No. 2021C03158). 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 H∞ 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. Keywords: External disturbance ; false data injection attacks ; load frequency control ; robust adaptive observer ; unknown input observer Received:December 30, 2020               Online Time:2022/07/15 View Full Text       Download reader