Diagnosis and Prediction for Loss of Coolant Accidents in Nuclear Power Plants Using Deep Learning Methods

Diagnosis and Prediction for Loss of Coolant Accidents in Nuclear Power Plants Using Deep Learning Methods

A combination of Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Convolutional LSTM (ConvLSTM) is constructed in this work for the fault diagnosis and post-accident prediction for Loss of Coolant Accidents (LOCAs) in Nuclear Power Plants (NPPs). The advantages of ConvLSTM, suc...

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Bibliographic Details
Main Authors: Jingke She, Tianzi Shi, Shiyu Xue, Yan Zhu, Shaofei Lu, Peiwei Sun, Huasong Cao
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-05-01
Series:Frontiers in Energy Research
Subjects:
CNN
LSTM
ConvLSTM
LOCA
diagnosis
prediction
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2021.665262/full

Internet

https://www.frontiersin.org/articles/10.3389/fenrg.2021.665262/full

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