相关论文: Time Stepped Cyber Physical Simulation of DoS, DoD…
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.…
In this paper, we present a novel distributed state estimation approach in networked DC microgrids to detect the false data injection in the microgrid control network. Each microgrid monitored by a distributed state estimator will detect if…
False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Detection of stealthy FDI attacks is impossible by the current bad data detection systems. Machine learning is one of the alternative methods…
Over the last decade, the number of cyberattacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, False Data Injection Attacks (FDIAs) is a class of cyberattacks against power grid…
Resilience assessment is a critical requirement of a power grid to maintain high availability, security, and quality of service. Most grid research work that is currently pursued does not have the capability to have hardware testbeds.…
Data injection attacks (DIAs) pose a significant cybersecurity threat to the Smart Grid by enabling an attacker to compromise the integrity of data acquisition and manipulate estimated states without triggering bad data detection…
Once critical substations are compromised, attack agents can coordinate among their peers to plot for maximizing disruption using local control devices. For defenders, it is critical to enumerate and identify all digital relays to determine…
Cyberattacks can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the…
Most traditional false data injection attack (FDIA) detection approaches rely on a key assumption, i.e., the power system can be accurately modeled. However, the transmission line parameters are dynamic and cannot be accurately known during…
Electric vehicles (EVs) in Vehicle-to-Grid (V2G) systems act as distributed energy resources that support grid stability. Centralized coordination such as the extended State Space Model (eSSM) enhances scalability and estimation efficiency…
The growing digitalization and the rapid adoption of high-powered Internet-of-Things (IoT)-enabled devices (e.g., EV charging stations) have increased the vulnerability of power grids to cyber threats. In particular, the so-called Load…
Power grids worldwide are increasingly victims of cyberattacks, where attackers can cause immense damage to critical infrastructure. The growing digitalization and networking in power grids combined with insufficient protection against…
This paper proposes a cyber-resilient secure control framework for autonomous vehicles (AVs) subject to false data injection (FDI) threats as actuator attacks. The framework integrates data-driven modeling, event-triggered communication,…
This paper presents the development of a real-time T&D co-simulation testbed for simulating large grids under high DER penetration. By integrating bulk power system, distribution feeders, and distributed energy resources (DER) models into…
Cyber-physical system (CPS) security for the smart grid enables secure communication for the SCADA and wide-area measurement system data. Power utilities world-wide use various SCADA protocols, namely DNP3, Modbus, and IEC 61850, for the…
Recently, moving target defence (MTD) has been proposed to thwart false data injection (FDI) attacks in power system state estimation by proactively triggering the distributed flexible AC transmission system (D-FACTS) devices. One of the…
Fast and accurate detection of cyberattacks is a key element for a cyber-resilient power system. Recently, data-driven detectors and physics-based Moving Target Defences (MTD) have been proposed to detect false data injection (FDI) attacks…
This paper introduces a novel, deep learning-based predictive model tailored to address wind curtailment in contemporary power systems, while enhancing cybersecurity measures through the implementation of a Dynamic Defense Mechanism (DDM).…
Network Intrusion Detection (NID) systems can benefit from Machine Learning (ML) models to detect complex cyber-attacks. However, to train them with a great amount of high-quality data, it is necessary to perform reliable simulations of…
Inverter-interfaced microgrids results in challenges when designing protection systems. Traditional time-overcurrent, admittance, and differential protection methods are unsuitable on account of lack of fault current, excessively short…