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

• DOI: 10.1016/j.anucene.2015.06.009
• Publication Date: 2015-07-03T07:31:25Z
• Authors: Yong-kuo Liu; Fei Xie; Chun-li Xie; Min-jun Peng; Guo-hua Wu; Hong Xia
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Abstract 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.