首页|基于PSO-BP神经网络的游泳池式反应堆堆芯功率调节系统优化研究

基于PSO-BP神经网络的游泳池式反应堆堆芯功率调节系统优化研究

Research on Optimization of Core Power Regulation System of Swimming Pool Reactor Based on PSO-BP Neural Network

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基于MATLAB/Simulink平台构建49-2游泳池式反应堆堆芯功率调节系统和一回路传热系统的仿真模型,开展外界反应性扰动仿真试验验证模型的准确性.采用粒子群算法(PSO)与反向传播(BP)神经网络相结合的比例-积分-微分(PID)控制器作为主控制器,模拟堆芯反应性和堆芯进口温度扰动下调节系统的响应情况,与游泳池式反应堆原控制器和传统BP神经网络控制器的响应情况相比较.结果表明,外界存在扰动时,基于PSO-BP神经网络的PID控制器可以使堆芯迅速达到稳定状态,调节时间更短、超调量更小,具有更好的鲁棒性和稳定性.
Based on the MATLAB/Simulink,the simulation model of the power regulation system and the primary heat transfer system of the 49-2 swimming pool reactor was constructed,and the external reactive disturbance simulation test was carried out to verify the accuracy of the model.The proportion integration differentiation(PID)controller combined with particle swarm optimization(PSO)and BP neural network was used as the main controller,and the response of the regulating system under core reactivity and core inlet temperature disturbance was simulated,which was compared with that of the original controller of swimming pool reactor and the traditional BP neural network controller.The results show that the PID controller based on PSO-BP neural network can make the core reach a stable state quickly,with shorter regulating time and smaller overshoot,and has better robustness and stability.

Particle swarm optimization(PSO)BP neural networkPID controllerReactor power regulation

彭治文、陈晓亮、朱珈辰、王峰

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中国原子能科学研究院反应堆工程技术研究所,北京,102413

粒子群算法(PSO) 反向传播(BP)神经网络 比例-积分-微分(PID)控制器 反应堆功率调节

2024

核动力工程
中国核动力研究设计院

核动力工程

CSTPCD北大核心
影响因子:0.3
ISSN:0258-0926
年,卷(期):2024.45(4)