Dynamic modeling and intelligent hybrid control of pressurized water reactor NPP power transient operation

被引：11

作者：

Ejigu, Derjew Ayele ^{[1
]}

Liu, Xiaojing ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Sch Nucl Sci & Engn, Shanghai 200240, Peoples R China

来源：

ANNALS OF NUCLEAR ENERGY | 2022年 / 173卷

关键词：

Pressurized water reactor; Artificial neural network; Intelligent control; Hybrid algorithm; ADAPTIVE DISTURBANCE REJECTION; ORDER PID CONTROLLER; ANN; ALGORITHM; DESIGN;

D O I：

10.1016/j.anucene.2022.109118

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 pressurized water reactor (PWR) is a combined system of several interdependent subsystems that face uncertainties and consist of critical parameters that initiate potential accidents. Therefore, a PWR needs to be controlled continuously for safe operation over its lifetime. To this end, a particle swarm optimization algorithm (PSO) optimized radial basis function neural network (RBF) proportional integral derivative (PID) control approach (PSO-RBF-PID) is proposed to regulate the PWR power at the desired level. The controller computes the control rod speed to optimize the output power to track the reference value. The performance, sensitivity, and stability of the controller are evaluated. The simulation results verified that the control strategy monitors the PWR power successfully and smoothly under different power levels as compared to the PSO-PID method. This study gives the benefit to apply the PSO-RBFPID control technique for control applications in other nuclear engineering fields. (c) 2022 Elsevier Ltd. All rights reserved.

引用

页数：11

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