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Found 4 papers, 0 papers with code
An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids
Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems.
Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids
This paper discusses the applications of deep learning (DL) to predict PF solutions for three-phase unbalanced power distribution grids.
Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection in Power Distribution Grids
Simulations are performed on the modified IEEE 13-bus and 123-bus systems using OpenDSS software to validate the efficacy of the proposed model for stealth attacks.
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward
Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs ($\mu$-PMUs), and smart meters.
