Nuclear power plant sensor signal reconstruction based on deep learning methods

被引:5
作者
Yang, Zixiao [1 ]
Xu, Peng [1 ]
Zhang, Biao [1 ]
Xu, Chuanlong [1 ]
Zhang, Liming [2 ]
Xie, Hongyun [2 ]
Duan, Qizhi [2 ]
机构
[1] Southeast Univ, Sch Energy & Environm, Nanjing 210096, Peoples R China
[2] China Nucl Power Engn Co Ltd, State Key Lab Nucl Power Safety Monitoring Techno, Shenzhen 518172, Peoples R China
关键词
Sensor data; Signal reconstruction; Convolutional neural network; Nuclear power plant; Steam generator water level; NEURAL-NETWORKS; VALIDATION; MODEL;
D O I
10.1016/j.anucene.2021.108765
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN's reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP's sensors. (c) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:12
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