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http://dx.doi.org/10.1016/j.net.2019.12.025

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)
Publication Information
Nuclear Engineering and Technology / v.52, no.7, 2020 , pp. 1429-1435 More about this Journal
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
LSTM; Parameter prediction; Cost sensitive; Pressurizer; Pressurized water reactor; Time series;
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