基于组合特征筛选与时序卷积网络的反应堆轴向功率偏差预测方法研究  

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

作　　者：陈静 陈彦 江灏[1] 段鹏斌 林蔚青 邱星华 许勇 Chen Jing;Chen Yan;Jiang Hao;Duan Pengbin;Lin Weiqing;Qiu Xinghua;Xu Yong(College of Electrical Engineering and Automation,Fuzhou University,Fuzhou,350108,China;China Nuclear Power Technology Research Institute Co.,Ltd.,Shenzhen,Guangdong,518000,China;China National Nuclear Power Operation Maintenance Technology Co.,Ltd.,Hangzhou,311200,China)

机构地区：[1]福州大学电气工程与自动化学院,福州350108 [2]中广核研究院有限公司,广东深圳518000 [3]中核运维技术有限公司,杭州311200

出　　处：《核动力工程》2025年第2期239-247,共9页Nuclear Power Engineering

基　　金：福建省自然科学基金面上项目(2022J01566)。

摘　　要：反应堆轴向功率偏差能够反映堆芯轴向功率分布和反应堆的运行情况,针对轴向功率偏差在变工况下预测困难的问题,该文提出一种基于组合特征筛选与时序卷积网络(TCN)的反应堆轴向功率偏差预测方法。以轴向功率偏差控制的基本原则为出发点,分析影响轴向功率偏差变化的因素,综合分析多维特征间的冗余度与相关性,利用组合特征筛选策略形成面向轴向功率偏差预测的最优特征子集,构建面向轴向功率偏差预测的关键关联特征数据,输入至TCN捕捉动态因果关系,以实现反应堆轴向功率偏差预测。实验研究表明,该文所提轴向功率偏差预测方法可深度挖掘反应堆轴向功率偏差相关参量的时序因果变化特性,准确预测轴向功率偏差发展态势,解决传统预测模型在复杂工况下预测跟踪不及时的问题,对核电厂反应堆状态监测和安全运行提供辅助参考的依据。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.

关 键 词：参量预测 轴向功率偏差 时序卷积网络(TCN) 组合特征筛选 

分 类 号：TL334[核科学技术—核技术及应用]