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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Summary: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 equipment inside coal power units, this study proposes a deep learning-based equipment condition anomaly detection model, which combines the deep autoencoder (DAE), Transformer, and Gaussian mixture model (GMM) to establish an anomaly detection model. DAE and the Transformer encoder extract static and time-series features from multi-dimensional operation data, and GMM learns the feature distribution of normal data to realize anomaly detection. Based on the data verification of boiler superheater equipment and turbine bearings in real power plants, the model is more capable of detecting equipment anomalies in advance than the traditional method and is more stable with fewer false alarms. When applied to the superheater equipment, the proposed model triggered early warnings approximately 90 h in advance compared to the actual failure time, with a lower false negative rate, reducing the missed detection rate by 70% compared to the Transformer-GMM (TGMM) model, which verifies the validity of the model and its early warning capability.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a18110704