针对核电厂意外停堆停机事件的智能监测预警方法研究  

Research on Intelligent Monitoring and Warning Algorithms for Unexpected Reactor Shutdown Events in Nuclear Power Plants

作　　者：李淅 王健生 杨森权 薛威 Li Xi;Wang Jiansheng;Yang Senquan;Xue Wei(China Nuclear Power Operation Technology Co.,Ltd.,Wuhan,430070,China;Fujian Fuqing Nuclear Power Co.,Ltd.,Fuqing,Fujian,350300,China)

机构地区：[1]中核武汉核电运行技术股份有限公司,武汉430070 [2]福建福清核电有限公司,福建福清350300

出　　处：《核动力工程》2025年第2期222-229,共8页Nuclear Power Engineering

摘　　要：当前核电机组运行时的异常情况发现主要依赖于核电厂数字化仪控系统(DCS)的阈值报警信息,缺乏对趋势的分析。本文通过事件逻辑建立变量间的逻辑关系,并基于此利用自关联神经网络(AANN)建模对关联变量进行异常检测,最后利用经验模态分解(EMD)趋势提取算法与自适应滑动窗口霍尔特线性趋势(HOLT)模型对异常变量进行预测。能够提前对停堆停机事件进行预警,使核电厂运维人员能够更早地发现并解决问题,提高核电运行安全性。利用仿真数据与机组真实异常数据进行测试实验,得到真实数据实验结果的均方误差(MSE)为0.1,拟合优度(R2)为0.99,并且可至少提前1 h对停机动作进行预警,验证了所提出的AANN-HOLT预警算法的准确性与提前预警的能力。The detection of abnormal conditions during the operation of nuclear power plant units mainly relies on threshold alarm information from the Digital Control System(DCS),with a lack of trend analysis.This paper investigates the establishment of logical relationships between variables through event logic,and based on this,employs an Auto-Associative Neural Network(AANN)model for anomaly detection of correlated variables.Finally,it uses the Empirical Mode Decomposition(EMD)trend extraction algorithm and the Adaptive Sliding Window Holt Linear Trend(HOLT)model to predict abnormal variables.This approach can provide early warnings for shutdown and reactor trip events,enabling plant operators to detect and resolve issues earlier,thus improving the operational safety of nuclear power plants.Testing experiments were conducted using both simulated data and actual unit anomaly data.The results from real data experiments show a Mean Squared Error(MSE)of 0.1 and a Goodness of Fit(R2)of 0.99,with at least 1 hour of advance warning before shutdown actions.This confirms the accuracy and early warning capabilities of the proposed AANN-HOLT warning algorithm.

关 键 词：智能监测 时间序列 趋势预测 停堆停机预警 

分 类 号：TL382[核科学技术—核技术及应用]