Detection and Isolation of False Data Injection Attacks in Smart Grids via Nonlinear Interval Observer

The detection and isolation problem of false data injection (FDI) attacks in large-scale smart grid systems, is investigated in this paper. The FDI attacks can bypass the traditional bad data detection techniques, by falsifying the process of state estimation. For this reason, the emergency of FDI attacks brings great risk to the security of smart grids. To address this crucial problem, a novel detection and isolation scheme against the FDI attacks for the large-scale smart grid system is proposed. We first design an interval observer to estimate the interval state of internally physical system accurately, based on the constructed physical dynamics of grid systems. Taking the bounds of internal state and external disturbance into account, the detection criterion that an alarm is generated when the interval residuals does not include the zero value is proposed. To address the limitation of precomputed threshold, we use the interval residuals regarded as a nature detection threshold to replace the evaluation function and detection threshold used in traditional attack detection methods. Furthermore, an attack signature logical judgment matrix-based isolation algorithm is further proposed to isolate the sensors, in which the FDI attacks may be injected into the attacked subarea. Finally, the effectiveness of the developed detection and isolation scheme is demonstrated by using detailed case studies on the IEEE 128-bus smart grid system.