Multivariate physics-informed convolutional autoencoder for anomaly detection in power distribution systems with widespread deployment of distributed energy resources

Despite the relentless progress of deep learning models in analyzing the system conditions under cyber-physical events, their abilities are limited in the power system domain due to data availability issues, cost of data acquisition, and lack of interpretation and extrapolation of the data beyond th...

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Veröffentlicht in:	Sustainable Energy, Grids and Networks Jg. 44; S. 102022
Hauptverfasser:	Jabbari Zideh, Mehdi, Khushalani Solanki, Sarika
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.12.2025
Schlagworte:	Anomaly detection Convolutional autoencoder Distributed energy resources Physics-informed neural network Power distribution grids Unsupervised data-driven method Physics-informed neural network Power distribution grids Unsupervised data-driven method Convolutional autoencoder Distributed energy resources Anomaly detection
ISSN:	2352-4677, 2352-4677
Online-Zugang:	Volltext
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