Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection in Power Distribution Grids

The growing trend toward the modernization of power distribution systems has facilitated the installation of advanced measurement units and promotion of the cyber communication systems. However, these infrastructures are still prone to stealth cyber attacks. The existing data-driven anomaly detectio...

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Bibliographic Details
Published in:	IEEE Power & Energy Society General Meeting pp. 1 - 5
Main Authors:	Zideh, Mehdi Jabbari, Solanki, Sarika Khushalani
Format:	Conference Proceeding
Language:	English
Published:	IEEE 21.07.2024
Subjects:	Anomaly detection convolutional autoencoder cyber anomaly detection Cyberattack false data injection attacks Kirchhoff's Law Load modeling Loss measurement Mathematical models Neural networks Numerical models Physics-informed neural networks Power distribution power distribution grids Training unsupervised data-driven methods
ISSN:	1944-9933
Online Access:	Get full text
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