Research on Fast Prediction of Key Parameters of Containment Based on Time Series Deep Learning Model

Feng Qianyi; Guo Zhangpeng; Li Zhongchun; Zhang Jiayu; Zhao Houjian; Ruan Yanghui; Yu Yu

doi:10.13832/j.jnpe.2022.06.0079

Volume 43 Issue 6

Dec. 2022

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Article Navigation > Nuclear Power Engineering > 2022 > 43(6): 79-84

Feng Qianyi, Guo Zhangpeng, Li Zhongchun, Zhang Jiayu, Zhao Houjian, Ruan Yanghui, Yu Yu. Research on Fast Prediction of Key Parameters of Containment Based on Time Series Deep Learning Model[J]. Nuclear Power Engineering, 2022, 43(6): 79-84. doi: 10.13832/j.jnpe.2022.06.0079

Citation:

Feng Qianyi, Guo Zhangpeng, Li Zhongchun, Zhang Jiayu, Zhao Houjian, Ruan Yanghui, Yu Yu. Research on Fast Prediction of Key Parameters of Containment Based on Time Series Deep Learning Model[J]. Nuclear Power Engineering, 2022, 43(6): 79-84. doi: 10.13832/j.jnpe.2022.06.0079

Citation:

Feng Qianyi, Guo Zhangpeng, Li Zhongchun, Zhang Jiayu, Zhao Houjian, Ruan Yanghui, Yu Yu. Research on Fast Prediction of Key Parameters of Containment Based on Time Series Deep Learning Model[J]. Nuclear Power Engineering, 2022, 43(6): 79-84. doi: 10.13832/j.jnpe.2022.06.0079

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Research on Fast Prediction of Key Parameters of Containment Based on Time Series Deep Learning Model

doi: 10.13832/j.jnpe.2022.06.0079

1.
Beijing Key Laboratory of Passive Safety Technology for Nuclear Energy, North China Electric Power University, Beijing, 102206, China
2.
Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2021-11-06
Rev Recd Date: 2022-06-07
Publish Date: 2022-12-14

Abstract

Abstract

The Main Steam Line Break (MSLB) accident threatens the safe operation of nuclear power plant. In this paper, the time-dependent transient response of key safety parameters of passive containment cooling system (PCCS) in nuclear power plant under MSLB accident is predicted based on time series deep learning model. The transient safety parameters are taken as the research objects. The data are preprocessed by linear normalization and feature label segmentation, trained by short-term data sets, and the time series deep learning model of single parameter and multi parameter coordination is established by using long short-term memory (LSTM) and recurrent neural network (RNN); long-term untrained data sets are predicted by a multi-parameter coordination model. The research shows that the prediction based on time series deep learning model is applicable under the same accident and different working conditions; it is feasible to predict long-term data based on short-term training data; the prediction accuracy of the single-parameter model or multi-parameter coordination model using LSTM is higher than that of RNN. The deep learning model based on LSTM can effectively, accurately and quickly predict the transient safety parameter response characteristics of PCCS under MSLB accidents, and can provide a fast prediction and analysis model for accident safety analysis.
- Main Steam Line Break (MSLB),
- Time series deep learning model,
- Safety analysis

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References(18)

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