Neural network model predictive control of core power of Qinshan nuclear power plant based on reinforcement learning

被引：2

作者：

Lv, Wei ^{[1
,2
]}

Chen, Jie ^{[3
]}

Tong, Li ^{[1
,2
]}

Liu, Yongchao ^{[1
,2
]}

Tan, Sichao ^{[1
,2
]}

Wang, Bo ^{[1
,2
]}

He, Zhengxi ^{[3
]}

Tian, Ruifeng ^{[1
,2
]}

Shen, Jihong ^{[4
]}

机构：

[1] Harbin Engn Univ, Coll Nucl Sci & Technol, Heilongjiang Prov Key Lab Nucl Power Syst & Equipm, Harbin 150001, Peoples R China

[2] Harbin Engn Univ, Key Lab Nucl Safety & Adv Nucl Energy Technol, Sch Nucl Sci & Technol, Harbin 150001, Peoples R China

[3] Nucl Power Inst China, Chengdu 610213, Peoples R China

[4] Harbin Engn Univ, Sch Math Sci, Harbin 150001, Peoples R China

来源：

ANNALS OF NUCLEAR ENERGY | 2024年 / 207卷

关键词：

Power control; Model predictive control; Reinforcement learning; Neural network; INTELLIGENT CONTROL; CONTROL-SYSTEM; OPERATION; OPTIMIZATION; ALGORITHM; DESIGN;

D O I：

10.1016/j.anucene.2024.110702

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：

The power change of Pressurized water reactors (PWRs) is a highly complex nonlinear coupling process, which requires the power controller to have a high nonlinear control capability. Since the traditional model predictive control (MPC) algorithm has the problems of insufficient robustness and adaptivity, this study proposes a neural network-based neural network model predictive control method based on model predictive control and optimizes its control parameters in real time by using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The designed control method is validated on the simulation model of the Qinshan nuclear power plant (NPP). Simulation results show that the proposed control method can realize effective control of power under a variety of power changing conditions and dynamic adjustment of control parameters.

引用

页数：15

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