Detection of False Data Injection Attacks in Smart Grid Based on Joint Dynamic and Static State Estimation

Power system state estimation is an essential component of the modern power system energy management system (EMS), and accurate state estimation is an indispensable basis for subsequent work. However, the attacker can inject biases into measurements to launch false data injection attacks (FDIAs) in smart grids, which ultimately cause state estimates to deviate from security values. This paper proposed the joint use of static state estimation and dynamic state estimation to detect the FDIA, i.e. the joint use of weighted least squares (WLS) and extended Kalman filter (EKF) with exponential weighting function (WEKF), which improves the robustness of state estimation. Since the WLS estimation considers only the measurements at the current moment, the recursive feature of the WEKF enables the estimation process to involve both historical state and current measurements. Therefore, consistency tests and residual tests were performed using the estimations of WLS and WEKF to effectively detect FDIA. In addition, a cluster partitioning approach with approximate equal redundancy of subsystems is proposed to locate the FDIA. The detection of FDIA triggers the partitioning of the network system, and then the chi-square test is used separately in each sub-network to determine the location of FDIA. Finally, the experimental results in the IEEE-14 bus system and the IEEE-30 bus system demonstrate that the approach can effectively detect and locate FDIAs.