Anomaly Detection and Resilience-Oriented Countermeasures against Cyberattacks in Smart Grids

Abstract

Security in smart grids has been investigated by many scholars so far. Among the existing security issues, False Data Injection (FDI) attacks in energy, computers, and communication domains are still an ongoing challenge. These attacks have the ability to sabotage the grid through causing misfunctioning of measurements devices as well as changing the state estimation appraisal so that these changes, known as false data, cannot be easily recognized and identified using conventional approaches. In this paper, the degree of network resilience against FDI attacks is analyzed by simulating a randomly generated sample FDI attack, in which the false data vector has different intensity and different quantity. A steady-state AC power flow in accordance with the outage model is employed to simulate and predict the power system response after the incidence of an FDI attack, and the ability of this attack for blackout and shutting down the transmission network has been investigated. In the proposed model, the transmission line outage, load shedding, as well as voltage instability metrics are tested and analyzed on the IEEE 300- bus test network. Given that FDI attacks are considered a serious threat to power systems, the preliminary results imply that the targeted electricity grid is resilient against these attacks in terms of the probability of outage and chain blackouts, but the transient voltage stability can be affected.