A neural network predictive control method for power control of small pressurized water reactors

被引：13

作者：

Xiao, Kai ^{[1
]}

Wu, Qiaofeng ^{[1
]}

Chen, Jie ^{[1
]}

Pu, Xiaofei ^{[1
]}

Zhang, Ying ^{[1
]}

Yang, Pengcheng ^{[1
]}

机构：

[1] Nucl Power Inst China, Sci & Technol Reactor Syst Design Technol Lab, Chengdu 610213, Peoples R China

来源：

ANNALS OF NUCLEAR ENERGY | 2022年 / 169卷

基金：

国家重点研发计划;

关键词：

Small pressurized water reactor; Neural network predictive control; Reactor power control system; Multi-model method; DESIGN;

D O I：

10.1016/j.anucene.2021.108946

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

Compared with large pressurized water reactors (PWRs), small PWRs have flexible operating conditions and complex operating environment, putting forward higher requirements on the power control system. Considering the low model identification accuracy of the widely-used model predictive control algorithm in reactor control, a neural network predictive (NNP) power control method is proposed for small PWRs in this paper. The local models under five typical operating conditions are weighted by a multi-model method to establish a core multi-model system, based on which a global NNP power control system is designed. The neural network is used to identify the core model first, and then a performance criterion function is optimized to determine the optimal control input to the reactor core. Simulation results of a small PWR core under typical transient conditions demonstrate the good load-following performance and strong anti-interference capability of the proposed NNP power control method for small PWRs. (C) 2022 Elsevier Ltd. All rights reserved.

引用

页数：10