摘要
小型模块化核反应堆具有建造周期短、安全性高、运维成本低、适应性强、应用领域广等显著优势,广受世界各国关注,也是我国的战略性需求.发展具有自适应、强鲁棒、高可控和高可信特性的新型控制方法,有效降低甚至消除对控制人员值守的依赖,是小型模块化核反应堆的一个重要发展趋势.智能化、自动化的反应堆控制系统通过高效的控制动作来实时跟踪负荷需求,进而有效提高反应堆的稳定性、可靠性和安全性.本文对小型模块化核反应堆控制方法的研究现状进行了综述.本文首先回顾了基于经典控制理论的传统PID控制方法的原理及其优缺点,然后总结了当前应用于反应堆控制系统的一些高精度、高效率智能控制方法,如模糊控制、神经网络控制、智能优化控制、复合控制方法等的主要特点.最后,针对当前小型模块化反应堆控制系统的应用需求和技术难点,本文对智能控制方法的可能发展方向进行了展望.
Due to the significant advantages such as short construction cycle,high safety performance,low operation and maintainence costs and strong adaptability,the small modular reactors(SMRs)have long been a focus of researchers around the world.Nowadays,it has also become a strategic need of our country.Recently,it has been clear that one of the promising development directions of intelligent control systems of the SMRs lies in the unmanned control systems therein some advanced control methods are applied with high robustness and reliability.These control systems can track load demand in real-time through efficient control actions and thus effectively improve the stability,reliability and safety of SMRs.Meanwhile,these systems can also reduce or even eliminate the dependence on operators significantly.In this paper,the mainstream control methods applied in SMRs are briefly reviewed.Firstly,principles and characteristics of the traditional PID control method based on the classical cybernetics are surveyed.Then,some intelligent control methods with higher accuracy and efficiency implemented in the reactor control systems,such as the fuzzy logic inference method,the neural network control method,the compound control method and the composite control method are summarized.Finally,facing to the requirements and technical problems of control systems in the SMRs,some potential research directions of intelligent control methods are prospected.
作者
张薇薇
何正熙
万雪松
刘方圆
邓科
肖凯
罗懋康
ZHANG Wei-Wei;HE Zheng-Xi;WAN Xue-Song;LIU Fang-Yuan;DENG Ke;XIAO Kai;LUO Mao-Kang(School of Mathematics,Sichuan University,Chengdu 610064,China;Science and Technology on Reactor System Design Technology Laboratory,Nuclear Power Institute of China,Chengdu 610213,China;Science and Technology on Reactor Fuel and Materials Laboratory,Nuclear Power Institute of China,Chengdu 610213,China)
出处
《四川大学学报(自然科学版)》
CAS
CSCD
北大核心
2024年第2期1-12,共12页
Journal of Sichuan University(Natural Science Edition)
基金
基于机器学习的复杂系统模型机理数据融合技术研究(SCU&DRSI-LHCX-6)。