Autocorrelation Feature Analysis for Dynamic Process Monitoring of Thermal Power Plants

Accurate process monitoring plays a crucial role in thermal power plants since it constitutes large-scale industrial equipment and its production safety is of great significance. Therefore, accurate process monitoring is very important for thermal power plants. The vigorous nature of the production...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2023-08, Vol.53 (8), p.5387-5399
Main Authors: Ma, Xin, Wu, Dehao, Gao, Shaoxu, Hou, Tongze, Wang, Youqing
Format: Article
Language:English
Subjects:
Algorithms
Autocorrelation
Complexity
Computational complexity
Continuously stirred tank reactors
dynamic process
fault detectability analysis
Heuristic algorithms
Monitoring
multivariate statistics
Nuclear safety
Power generation
Power plants
Principal component analysis
Principal components analysis
Process monitoring
Redundancy
Signal processing algorithms
Thermal power plants
Thermoelectricity
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Summary:Accurate process monitoring plays a crucial role in thermal power plants since it constitutes large-scale industrial equipment and its production safety is of great significance. Therefore, accurate process monitoring is very important for thermal power plants. The vigorous nature of the production process requires dynamic algorithms for monitoring. Since the common dynamic algorithm is mainly based on data expansion, the online computing complexity is too high because of data redundancy. Accordingly, this article proposes an innovative, dynamic process monitoring algorithm called autocorrelation feature analysis (AFA). AFA mines the dynamic information of continuous samples by calculating the correlation between the current time and past time features. While improving the monitoring effect, the AFA algorithm also has extremely low online computational complexity, even lower than common static algorithms, such as principal component analysis. Furthermore, this study exhibits the general form of dynamic additive faults for the first time and verifies the reliability of the algorithm through fault detectability analysis. Conclusively, the superiority of the AFA algorithm is verified on a numerical example, continuous stirred tank reactor (CSTR), and real data measured from a 1000-MW ultrasupercritical thermal power plant.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2022.3228861