Short-Term State Forecasting-Aided Method for Detection of Smart Grid General False Data Injection Attacks

TLDR: The approximate dc model is extended to a more general linear model that can handle both supervisory control and data acquisition and phasor measurement unit measurements and a general FDIA based on this model is derived and the error tolerance of such attacks is discussed.

Abstract: Successful detection of false data injection attacks (FDIAs) is essential for ensuring secure power grids operation and control. First, this paper extends the approximate dc model to a more general linear model that can handle both supervisory control and data acquisition and phasor measurement unit measurements. Then, a general FDIA based on this model is derived and the error tolerance of such attacks is discussed. To detect such attacks, a method based on short-term state forecasting considering temporal correlation is proposed. Furthermore, a statistics-based measurement consistency test method is presented to check the consistency between the forecasted measurements and the received measurements. This measurement consistency test is further integrated with ${\infty }$ -norm and ${L}_{{2}}$ -norm-based measurement residual analysis to construct the proposed detection metric. The proposed detector addresses the shortcoming of previous detectors in terms of handling critical measurements. Besides, the removal problem of attacked measurements, which may cause the system to become unobservable, is addressed effectively by the proposed method through forecasted measurements. Numerical tests on IEEE 14-bus and 118-bus test systems verify the effectiveness and performance of the proposed method.