Detection and isolation of false data injection attack via adaptive Kalman filter bank

Due to the integration of cyber-physical systems, smart grids have faced the new security risks caused by false data injection attacks (FDIAs). FDIAs can bypass the traditional bad data detection techniques by falsifying the process of state estimation. For this reason, this paper studies the detection and isolation problem of FDIAs based on the adaptive Kalman filter bank (AKFB) in smart grids. Taking the covert characteristics of FDIAs into account, a novel detection method is proposed based on the designed AKF. Moreover, the adaptive threshold is proposed to solve the detection delay caused by a priori threshold in the current detection methods. Considering the case of multiple attacked sensor nodes, the AKFB-based isolation method is developed. To reduce the number of isolation iterations, a logical decision matrix scheme is designed. Finally, the effectiveness of the proposed detection and isolation method is demonstrated on an IEEE 22-bus smart grids.