Distributed detection and isolation of false data injection attacks in smart grids via nonlinear unknown input observers

Abstract

In this paper, a distributed detection and isolation scheme against False Data Injection attacks FDIAs in smart grids is studied. Taking the stealthy characteristics of FDIAs into account, we propose a nonlinear unknown input observer UIO-based distributed detection method. Through the capabilities of designed UIO to deal with the effects of the interconnected relations among the grid subareas and external disturbance, we can obtain the accurate estimation of internally physical state. To detect the FDIAs more quickly and avoid missed detection, an adaptive threshold is computed to replace the traditional precomputed threshold. A distributed isolation scheme against the FDIAs is proposed with two steps, by considering the exchanged information of adjacent grid subareas. In the first-step, each local control center of subareas is to isolate the possible actuator attack set via the proposed local attack signature judgment logic matrix. In the second-step, the possible subarea attack set is isolated by the established global attack signature judgment logic matrix. The distributed isolation logic decision against the FDIAs relays on the combination of isolation results in the first-step and second-step. Finally, the performance of the proposed distributed detection method on the IEEE 28-bus smart grid system is evaluated. And the effectiveness of the proposed distributed detection and isolation scheme on large-scale IEEE 128-bus smart grid system is illustrated.