Real-Time Identification of False Data Injection Attacks: A Novel Dynamic-Static Parallel State Estimation Based Mechanism

Abstract

Although cyber-physical system (CPS) enhances the monitoring ability of power systems, it also raises the threats of cyber-attacks. False data injection attacks (FDIAs) can evade the bad data detection (BDD) module to inject pre-designed false data into a subset of measurements without being observed. To mitigate the threats, this paper develops a real-time FDIAs identification mechanism for AC state estimation (SE) based on dynamic-static parallel SE. When the system is compromised by FDIAs, the decrease of temporal correlation of the parallel SE time series can effectively reveal the potential FDIAs. To further capture these sequential uncorrelation features presented in the system states and enhance the detection accuracy, we also employ the cross wavelet transform (XWT) to execute the time-frequency domain decomposition and cross-examination with the parallel SE time series. Case studies on several IEEE standard test systems verify the validity of the proposed mechanism. In addition, we conduct sensitivity tests of two influence factors of the proposed mechanism and analyze in depth.

Keywords

• False data injection attacks (FDIAs)
• parallel state estimation (SE)
• cross wavelet transform (XWT)
• anomaly identification mechanism