A Particle Filter-Based Approach for the Detection of False Data Injection Attacks on Automatic Generation Control Systems

Abstract

Automatic Generation Control (AGC) systems adjust the power output of multiple generators at different power plants, in response to changes in the load. In addition to regulating the system frequency, AGC systems help to minimize the tie-line power deviation in multi-area systems. Given their reliance on communication links in order to send/receive mea-surements/control actions about frequency and power deviation in the power system, AGC systems are vulnerable to false data injection (FDI) attacks. Several works have considered the effect of these cyber attacks on AGC systems and many approaches have been proposed to detect FDI attacks against them. However, non of the previous works considered the nonlinearity of the AGC system and all the proposed solutions are only effective under the assumed linearity of the AGC model. In this work, we address this deficiency and propose a new particle filter-based approach to detect FDI attacks in AGC systems considering both the communication time-delay and governor dead-band nonlinearities. To confirm the effectiveness of this approach, a 2-area power system is simulated using MATLAB/Simulink. The results show that the utilized technique is capable of detecting various types of false data injection attacks against the considered AGC system.