Research on reactor power prediction of nuclear power plant based on multivariate optimization GRU model

被引：0

作者：

Tan, Canyi ^{[1
,2
]}

Wang, Bo ^{[1
,2
]}

Li, Jiangkuan ^{[1
,2
]}

Chen, Jie ^{[3
]}

Liang, Biao ^{[1
,2
]}

Zheng, Shangcai ^{[1
,2
]}

Han, Rui ^{[1
,2
]}

Tian, Ruifeng ^{[1
,2
]}

Tan, Sichao ^{[1
,2
]}

机构：

[1] College of Nuclear Science and Technology, Harbin Engineering University, Harbin,150001, China

[2] Heilongjiang Provincial Key Laboratory of Nuclear Power System & Equipment, Harbin Engineering University, Harbin,150001, China

[3] Nuclear Power Institute of China, Chengdu,610213, China

来源：

International Journal of Advanced Nuclear Reactor Design and Technology | 2024年 / 6卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Nuclear power plants;

D O I：

10.1016/j.jandt.2024.10.001

中图分类号：

学科分类号：

摘要：

In the operation of nuclear power plants, the accurate prediction of power change trends is crucial for ensuring safety and stability. In this work, a ML-GRU-RS method, based on model-agnostic meta-learning (MAML), gate recurrent unit (GRU), and random search optimization, is proposed for the long-term prediction of key parameters of nuclear power plants. This method combines the fast adaptability of MAML, the time series data processing capability of GRU, and the optimization efficiency of random search to achieve high-precision predictions under varying power conditions. The results demonstrate that this method can effectively predict the future trends of key parameters in nuclear power plants. The ability of operators to anticipate these trends has been significantly enhanced, contributing to the overall safety of the nuclear power plants. © 2024 Xi'an Jiaotong University

引用

页码：78 / 89

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