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.
Highlights
Hackers launch False data injection attacks (FDIAs) by injecting false data called attack vector a into the natural measurement vector z=[z1, z2, . . . , zm]T to disrupt the AC state estimation (SE) in (1), where z generally contains voltage magnitudes and phase angles, complex power injections at buses and complex power flows on branches. x=[x1, x2, . . . , xn]T is the state vector, which composed of voltage magnitudes and phase angles. h(·) represents the nonlinear measurement function between state vector x and measurement vector z, which depends on the network topology and physical parameters of the system
In this paper, the risk of the stealthy FDIAs that can compromise the system states and cause trigger cascading accidents are addressed by a dynamic-static parallel SE based realtime identification mechanism
We carry out the dynamic-static parallel SE to divulge the dynamic correlation feature anomalies caused by attacks to expose the potential FDIAs
Summary
The deep integration of communication and information technology is driving the transformation of the power system into a typical CPS [1], [2]. Hackers can modify real-time topology to mislead the control center, gain illegal profit from the confusing electricity market, and even cause the cascading failures [8]–[10] These impacts accelerate the studies of the identification and prevention of FDIAs to become a research hotspot for CPS cyber-security in recent years. (3) The priority of the detection of strong-strength attacks is not emphasized To address these problems, this paper develops a real-time FDIAs identification mechanism, which focuses on the static-spatial data relationship of system states in the discrete time and derives from the dynamic-continuous correlation features contained in the consecutive system states. It is paramount to understand the attack mode of AC FDIAs that can give opportunities to power CPS to improve the reliability and economy of operation by formulating appropriate countermeasures
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