An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new so...

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Bibliographic Details

Published in:	Energy and AI Vol. 17; p. 100377
Main Authors:	Zhang, Jiahao, Cheng, Lan, Yang, Zhile, Xiao, Qinge, Khan, Sohail, Liang, Rui, Wu, Xinyu, Guo, Yuanjun
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.09.2024 Elsevier
Subjects:	Data driven Deep learning Power grid fault detection Semi-supervised learning Smart grid Deep learning Data driven Power grid fault detection Semi-supervised learning Smart grid
ISSN:	2666-5468, 2666-5468
Online Access:	Get full text
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