Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network

Chen Jing; Chen Yan; Jiang Hao; Duan Pengbin; Lin Weiqing; Qiu Xinghua; Xu Yong

doi:10.13832/j.jnpe.2024.090021

Volume 46 Issue 2

Apr. 2025

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Article Navigation > Nuclear Power Engineering > 2025 > 46(2): 239-247

Chen Jing, Chen Yan, Jiang Hao, Duan Pengbin, Lin Weiqing, Qiu Xinghua, Xu Yong. Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network[J]. Nuclear Power Engineering, 2025, 46(2): 239-247. doi: 10.13832/j.jnpe.2024.090021

Citation:

Chen Jing, Chen Yan, Jiang Hao, Duan Pengbin, Lin Weiqing, Qiu Xinghua, Xu Yong. Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network[J]. Nuclear Power Engineering, 2025, 46(2): 239-247. doi: 10.13832/j.jnpe.2024.090021

Citation:

Chen Jing, Chen Yan, Jiang Hao, Duan Pengbin, Lin Weiqing, Qiu Xinghua, Xu Yong. Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network[J]. Nuclear Power Engineering, 2025, 46(2): 239-247. doi: 10.13832/j.jnpe.2024.090021

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Research on Prediction Method of Reactor Axial Power Deviation Based on Combined Feature Selection and Temporal Convolutional Network

doi: 10.13832/j.jnpe.2024.090021

1.
College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China
2.
China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong, 518000, China
3.
China National Nuclear Power Operation Maintenance Technology Co., Ltd., Hangzhou, 311200, China

Received Date: 2024-09-11
Rev Recd Date: 2024-09-28

Available Online: 2025-01-23

Publish Date: 2025-04-15

Abstract

Abstract

The axial power deviation of a reactor can reflect the axial power distribution of the core and the operation of the reactor. Aiming at the difficulties in predicting the axial power deviation under variable operating conditions, this paper proposes a prediction method of reactor axial power deviation based on the combined feature selection and temporal convolutional network (TCN). Taking the basic principle of axial power deviation control as the starting point, this paper analyzes the factors affecting the change of axial power deviation, comprehensively analyzes the redundancy and correlation among multi-dimensional features, uses the combined feature selection strategy to form the optimal feature subset for axial power deviation prediction, constructs the key correlation feature data for axial power deviation prediction, and inputs it into TCN to capture dynamic causality, so as to achieve the prediction of reactor axial power deviation. Experimental studies show that the proposed method can deeply explore the temporal causal change characteristics of the parameters related to the axial power deviation of the reactor, accurately predict the development trend of the axial power deviation, solve the problem that the traditional prediction model does not predict and track in time under complex operating conditions, and provide an auxiliary reference basis for the reactor status monitoring and safe operation of nuclear power plants.
- Parameter prediction,
- Axial power deviation,
- Temporal convolutional network (TCN),
- Combined feature selection

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References(22)

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