Prediction of time series of NPP operating parameters using dynamic model based on BP neural network

•A dynamic prediction model for NPP operating parameters was proposed.•The structure of continuous dynamic prediction system was designed.•Multi-threading technology was used in the system.•The system can predict the fluctuating data with high accuracy. A dynamic model was developed using two back-p...

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Bibliographic Details
Published in:Annals of nuclear energy 2015-11, Vol.85, p.566-575
Main Authors: Liu, Yong-kuo, Xie, Fei, Xie, Chun-li, Peng, Min-jun, Wu, Guo-hua, Xia, Hong
Format: Article
Language:English
Subjects:
Back-propagation neural network (BPNN)
Coolants
Dynamic models
Dynamic prediction
Fluctuation
Mathematical models
Neural networks
Nuclear engineering
Nuclear power plant (NPP)
Nuclear reactor components
Time series
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Summary:•A dynamic prediction model for NPP operating parameters was proposed.•The structure of continuous dynamic prediction system was designed.•Multi-threading technology was used in the system.•The system can predict the fluctuating data with high accuracy. A dynamic model was developed using two back-propagation neural networks of the same structure, one for online training and the other for prediction, and proposed for continuous dynamic prediction of the time series of NPP operating parameters. The proposed prediction model was validated by predicting such time series of NPP operating parameters as coolant void fraction, water level in SG and pressurizer. Validation results indicated the proposed model could be used to achieve a stable prediction effect with high prediction accuracy for the prediction of fluctuating data.
ISSN:0306-4549
1873-2100
DOI:10.1016/j.anucene.2015.06.009