Research on Neural Network Predictive Control of Small Pressurized Water Reactor

被引：0

作者：

Xiao K. ^{[1
]}

Li J. ^{[1
]}

Zhao M. ^{[1
]}

Pu X. ^{[1
]}

Zheng Y. ^{[1
]}

Qing X. ^{[1
]}

机构：

[1] Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu

来源：

| 1600年 / Atomic Energy Press卷 / 41期

关键词：

Neural network predictive control; Reactor power control; Small pressurized water reactor;

D O I：

10.13832/j.jnpe.2020.S2.0050

中图分类号：

学科分类号：

摘要：

Considering the nonlinear and time-varying characteristics of reactor core and external disturbance, it is difficult for the traditional classical control method to achieve good control of reactor power in all operating conditions. Therefore, a neural network predictive control method for reactor power is proposed. Taking the International Reactor Innovative and Secure (IRIS) small pressurized water reactor (PWR) as the research object, the nonlinear model for the reactor core was established, and the neural network predictive control simulation platform of reactor power was built based on MATLAB/Simulink. The simulation results show that the designed neural network predictive controller can achieve good control of reactor power under variable load conditions and core inlet temperature disturbance. © 2020, Editorial Board of Journal of Nuclear Power Engineering. All right reserved.

引用

页码：50 / 53

页数：3

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