Detection and isolation of false data injection attack for smart grids via unknown input observers

The emergence of cyber-physical attacks brings a key challenge to the existing system integrity protection schemes (SIPSs) of smart grids. As one of typical cyber-physical attacks, false data injection attacks (FDIAs) can bypass the existing Kalman filter-based $\chi <^>2$chi 2-detector detection techniques in SIPSs. To improve the detection performance against the FDIAs in SIPSs, this study proposes an unknown input observer (UIO)-based detection and isolation method. Taking the stealthy characteristics of FDIAs into account, this study presents a set of UIOs to detect the FDIA based on the internally physical dynamics. Furthermore, a UIO-based detection and isolation algorithm against the FDIAs is proposed based on the feature of residuals generated by UIOs. To detect the cyber attacks quickly and avoid missing detection, an adaptive threshold is designed to replace the precomputed threshold by taking the model linearised error and disturbance into account. Finally, comprehensive simulation results on the proposed algorithm are carried out, and the effectiveness of improving the detection performance in SIPSs is verified.