Dynamic Phasor Estimation Method Considering False Data Attack Detection

Abstract

In this paper proposed was a dynamic phasor estimation method considering false data attack. First, an electrical signal model for dynamic conditions is established by Taylor series expansion of electrical signals with harmonic and noise conditions. Then, the phasor is detected, tracked, and estimated by using double suboptimal-scaling factor adaptive strong tracking Kalman filter (DSTKF) and false data attack detection method based on Euclidean distance. Finally, the proposed estimation method is used to simulate the electrical signal with noise and false data attack and the fault signal of IEEE39 node. The results show that the method not only detects the false data injection attack and mutation in the electrical signal, but also has high estimation accuracy and tracking ability for the dynamic phasor.

Keywords

• false data
• attack detection
• euclidean distance
• kalman filter
• phasor estimation
• dynamic performance