期刊
ANNALS OF NUCLEAR ENERGY
卷 85, 期 -, 页码 566-575出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.anucene.2015.06.009
关键词
Nuclear power plant (NPP); Time series; Back-propagation neural network (BPNN); Dynamic prediction
资金
- National Natural Science Foundation of China [51379046]
- Fundamental Research Funds for the Central Universities [DL13CB14]
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. (C) 2015 Elsevier Ltd. All rights reserved.
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