Attack detection in cyber-physical power systems based on Kalman filtering state estimation

This study explores detection techniques for Dynamic Load Alteration Attacks (D-LAA) in power grids. First, a Cyber-Physical Power System (CPPS) affected by D-LAA is constructed, and its discrete system model is established, incorporating zero-mean Gaussian white noise to realistically reflect actual conditions. The Multi-Factor Adaptive Kalman Filter (MFAKF) is an advanced version of the Kalman filter, improved by introducing multiple diminishing factors. The enhanced Multi-Factor Mixed Adaptive Kalman Filter (MFMAKF) is used for real-time state estimation in smart grids, demonstrating stronger anti-interference capabilities and more accurate system estimates compared to the standard Kalman filter and MFAKF. Finally, this study combines the state estimation method with Euclidean Distance Ratio Detection (EDRD) and conducts simulation experiments on a smart grid system with 30 buses to verify the effectiveness and feasibility of the proposed method.