Detection and Isolation of False Data Injection Attacks in Smart Grid via Unknown Input Interval Observer

Abstract

This article investigates the detection and isolation of false data injection (FDI) attacks in a smart grid based on the unknown input (UI) interval observer. Recent studies have shown that the FDI attacks can bypass the traditional bad data detection methods by using the vulnerability of state estimation. For this reason, the emergency of FDI attacks brings enormous risk to the security of smart grid. To solve this crucial problem, an UI interval observer-based detection and the isolation scheme against FDI attacks are proposed. We first design the UI interval observers to obtain interval state estimation accurately, based on the constructed physical dynamics grid model. Through the capabilities of the designed UI interval observers, the accurate interval estimation state can be decoupled from unknown disturbances. Based on the characteristics of the interval residuals, a UI interval observer-based global detection algorithm was proposed. Particularly, the interval residual-based detection criteria can address the limitation of the precomputed threshold in traditional bad data detection methods. On this basis, we further consider the detection and isolation of FDI attacks under structure vulnerability. Namely, there exist undetectable FDI attacks in the grid system. Taking the attack undetectability problem into account, a logic judgment matrix-based local detection and isolation algorithm against FDI attacks are developed. Based on the combinations of observable sensor cases, local control centers can further detect and isolate the attack set under structure vulnerability. Finally, the effectiveness of the developed detection and isolation algorithms against FDI attacks is demonstrated on the IEEE 8-bus and IEEE 118-bus smart grid system, respectively.