Related papers: Analyzing the Impact of AC False Data Injection At…
Intelligently designed false data injection (FDI) attacks have been shown to be able to bypass the $\chi^2$-test based bad data detector (BDD), resulting in physical consequences (such as line overloads) in the power system. In this paper,…
Accurate and reliable dynamic state quantities of generators are very important for real-time monitoring and control of the power system. The emergence of cyber attacks has brought new challenges to the state estimation of generators.…
Smart Grid has rapidly transformed the centrally controlled power system into a massively interconnected cyber-physical system that benefits from the revolutions happening in the communications (e.g. 5G) and the growing proliferation of the…
Modern urban railways extensively use computerized sensing and control technologies to achieve safe, reliable, and well-timed operations. However, the use of these technologies may provide a convenient leverage to cyber-attackers who have…
The smart grid combines the classical power system with information technology, leading to a cyber-physical system. In such an environment the malicious injection of data has the potential to cause severe consequences. Classical…
The advent of smart power grid which plays a vital role in the upcoming smart city era is accompanied with the implementation of a monitoring tool, called state estimation. For the case of the unbalanced residential distribution grid, the…
The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Concurrently the increasing distributed energy resources (DER) and advanced metering infrastructures (AMI) have transformed the…
This paper studies false data injection (FDI) attacks against phasor measurement units (PMUs). As compared to the conventional bad data detector (BDD), an enhanced BDD utilizing the effect of zero injection buses is proposed. Feasible…
With the deeper penetration of inverter-based resources in power systems, false data injection attacks (FDIA) are a growing cyber-security concern. They have the potential to disrupt the system's stability like frequency stability, thereby…
Artificial neural network (ANN) provides superior accuracy for nonlinear alternating current (AC) state estimation (SE) in smart grid over traditional methods. However, research has discovered that ANN could be easily fooled by adversarial…
Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vulnerability of the electricity grid to false data injection…
In the recent years cyberattacks to smart grids are becoming more frequent Among the many malicious activities that can be launched against smart grids False Data Injection FDI attacks have raised significant concerns from both academia and…
Physical consequences to power systems of false data injection cyber-attacks are considered. Prior work has shown that the worst-case consequences of such an attack can be determined using a bi-level optimization problem, wherein an attack…
State estimation estimates the system condition in real-time and provides a base case for other energy management system (EMS) applications including real-time contingency analysis and security-constrained economic dispatch. Recent work in…
Power system functionality is determined on the basis of the power system state estimation (PSSE). Thus, corruption of the PSSE may lead to severe consequences, such as financial losses, maintenance damage, and disruptions in electricity…
This paper demonstrates that false data injection (FDI) attacks are extremely limited in their ability to cause physical consequences on $N-1$ reliable power systems operating with real-time contingency analysis (RTCA) and security…
This paper studies the vulnerability of phasor measurement units (PMUs) to false data injection (FDI) attacks. Prior work demonstrated that unobservable FDI attacks that can bypass traditional bad data detectors based on measurement…
The rise of cyber-security concerns has brought significant attention to the analysis and design of cyber-physical systems (CPSs). Among the various types of cyberattacks, denial-of-service (DoS) attacks and false data injection (FDI)…
Deep Neural Networks have proven to be highly accurate at a variety of tasks in recent years. The benefits of Deep Neural Networks have also been embraced in power grids to detect False Data Injection Attacks (FDIA) while conducting…
The rapid growth of AI-driven data centers and large-scale energy storage systems is increasing the reliance of power system operation on real-time measurement data and automated decision-making. However, many existing detection methods…