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Found 10 papers, 0 papers with code
A Game-Theoretic Approach for PMU Deployment Against False Data Injection Attacks
Phasor Measurement Units (PMUs) are used in the measurement, control and protection of power grids.
The Impact of Load Altering Attacks on Distribution Systems with ZIP Loads
Load-altering attacks (LAAs) pose a significant threat to power systems with Internet of Things (IoT)-controllable load devices.
Communication Reduction for Power Systems: An Observer-Based Event-Triggered Approach
This paper presents an event detection mechanism that significantly reduces the volume of data transmission to perform necessary control actions, using a scalable scheme that enhances the stability and reliability of power grids.
Optimal Placement and Power Supply of Distributed Generation to Minimize Power Losses
Additionally, the reactive power supply from the DGs can further reduce power losses in the distribution grid and improve power transmission efficiency.
Uncovering Load-Altering Attacks Against N-1 Secure Power Grids: A Rare-Event Sampling Approach
In this work, we aim to uncover spatiotemporal characteristics of LAAs that can lead to serious impact.
Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning
As one of the most sophisticated attacks against power grids, coordinated cyber-physical attacks (CCPAs) damage the power grid's physical infrastructure and use a simultaneous cyber attack to mask its effect.
Analysis of Load-Altering Attacks Against Power Grids: A Rare-Event Sampling Approach
By manipulating tens of thousands of internet-of-things (IoT) enabled high-wattage electrical appliances (e. g., WiFi-controlled air-conditioners), large-scale load-altering attacks (LAAs) can cause severe disruptions to power grid operations.
Data-Driven Detection and Identification of IoT-Enabled Load-Altering Attacks in Power Grids
Timely detection and identification of any compromised nodes are essential to minimise the adverse effects of these attacks on power grid operations.
A Comparison of Data-Driven Techniques for Power Grid Parameter Estimation
Our results show that the SINDy algorithm outperforms the PINN and UKF algorithms in being able to accurately estimate the power grid parameters over a wide range of system parameters (including high and low inertia systems).
A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations
To overcome this drawback, we propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach.
