Hybrid Deep Learning Framework for Anomaly Detection in Power Plant Systems

Currently, thermal power units undertake the task of peak and frequency regulation, and their internal equipment is in a non-conventional environment, which could very easily fail and thus lead to unplanned shutdown of the unit. To realize the condition monitoring and early warning of the key equipm...

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Bibliographic Details
Published in:	Algorithms Vol. 18; no. 11; p. 704
Main Authors:	Wang, Shuchong, Zhao, Changxiang, Liu, Xingchen, Ni, Xianghong, Chen, Xu, Gao, Xinglong, Sun, Li
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.11.2025
Subjects:	Analysis Anomalies anomaly detection coal-fired power plant Coal-fired power plants Condition monitoring deep autoencoder Deep learning Electric power-plants Failure times False alarms Gaussian mixture model Laws, regulations and rules Learning Methods Nuclear energy Nuclear power plants Power plants Probabilistic models Superheaters Thermal power plants Transformer Turbines
ISSN:	1999-4893, 1999-4893
Online Access:	Get full text
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