Detection of False Data Injection Attack in Smart Grid Based on ASTCKF

With the rapid development of smart grids, the high integration and openness of power grid information systems pose more risks of network attacks. Fake Data Injection Attack (FDIA) is one of the most threatening types, which can covertly affect the operation status of the power grid by tampering with measurement data. In order to effectively and timely detect and alleviate FDIA events during the operation of the power grid, this paper proposes an improved state estimation method based on the Adaptive Strong Tracking Volume Kalman Filter (ASTCKF). By combining the adaptive volume point generation technology and the gain adjustment mechanism of the strong tracking filter, it can effectively cope with the nonlinear characteristics and rapid changes in the power system. And based on the central limit theorem, normal distribution detection was implemented to achieve FDIA detection.