Anomaly detection based on a deep graph convolutional neural network for reliability improvement

Anomaly detection based on a deep graph convolutional neural network for reliability improvement

Effective anomaly detection in power grid engineering is essential for ensuring the reliability of dispatch and operation. Traditional anomaly detection methods based on manual review and expert experience cannot be adapted to the current rapid increases in project data. In this work, to address thi...

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Bibliographic Details
Main Authors:	Gang Xu, Jie Hu, Xin Qie, Jingguo Rong
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2024-01-01
Series:	Frontiers in Energy Research
Subjects:	reliability improvement graph convolutional network anomaly detection skip connection mechanism artificial intelligence
Online Access:	https://www.frontiersin.org/articles/10.3389/fenrg.2024.1345361/full

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