Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Application of cost-sensitive LSTM in water level prediction for nuclear reactor pressurizer

  • Zhang, Jin (Command and Control Engineering College, Army Engineering University of PLA) ;
  • Wang, Xiaolong (College of Nuclear Science and Technology, Naval University of Engineering) ;
  • Zhao, Cheng (Command and Control Engineering College, Army Engineering University of PLA) ;
  • Bai, Wei (Command and Control Engineering College, Army Engineering University of PLA) ;
  • Shen, Jun (Zhenjiang Campus, Army Military Transportation University of PLA) ;
  • Li, Yang (Command and Control Engineering College, Army Engineering University of PLA) ;
  • Pan, Zhisong (Command and Control Engineering College, Army Engineering University of PLA) ;
  • Duan, Yexin (Zhenjiang Campus, Army Military Transportation University of PLA)
  • Received : 2019.05.21
  • Accepted : 2019.12.24
  • Published : 2020.07.25
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Abstract

Applying an accurate parametric prediction model to identify abnormal or false pressurizer water levels (PWLs) is critical to the safe operation of marine pressurized water reactors (PWRs). Recently, deep-learning-based models have proved to be a powerful feature extractor to perform high-accuracy prediction. However, the effectiveness of models still suffers from two issues in PWL prediction: the correlations shifting over time between PWL and other feature parameters, and the example imbalance between fluctuation examples (minority) and stable examples (majority). To address these problems, we propose a cost-sensitive mechanism to facilitate the model to learn the feature representation of later examples and fluctuation examples. By weighting the standard mean square error loss with a cost-sensitive factor, we develop a Cost-Sensitive Long Short-Term Memory (CSLSTM) model to predict the PWL of PWRs. The overall performance of the CSLSTM is assessed by a variety of evaluation metrics with the experimental data collected from a marine PWR simulator. The comparisons with the Long Short-Term Memory (LSTM) model and the Support Vector Regression (SVR) model demonstrate the effectiveness of the CSLSTM.

Keywords

References

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