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Multi-layer perception based model predictive control for the thermal power of nuclear superheated-steam supply systems

机译:基于多层感知的核过热蒸汽供应系统火力模型预测控制

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摘要

Nuclear superheated-steam supply systems (Su-NSSS) produces superheated steam flow for electricity generation or process heat. Although the current Su-NSSS control law can guarantee satisfactory closed loop stability, which regulates the neutron flux, primary coolant temperature and live steam temperature by adjusting the control rod speed as well as primary and secondary flowrates, however, the control performance needs to be further optimized. Motivated by the necessity of optimizing the thermal power response, a novel multi-layer perception (MLP) based model predictive control (MPC) is proposed in this paper, which is constituted by a MLP-based prediction model and the control input designed along the direction opposite to the gradient of a given performance index. It is proved theoretically that this MLPbased MPC guarantees globally-bounded closed-loop stability. Finally, this newly-built MLP-based MPC is applied to the thermal power control of a Su-NSSS, whose implementation is given by forming a cascaded feedback control loop with the currently existing Su-NSSS power-level control in the inner loop for stabilization and with this new MPC in the outer loop for optimization. Numerical simulation results verify the correctness of theoretical result, and show the satisfactory improvement in optimizing the thermal power response. (C) 2018 Elsevier Ltd. All rights reserved.
机译:核过热蒸汽供应系统(Su-NSSS)产生过热蒸汽流,用于发电或过程热量。尽管当前的Su-NSSS控制法可以保证令人满意的闭环稳定性,通过调节控制杆速度以及一次和二次流量来调节中子通量,一次冷却剂温度和新鲜蒸汽温度,但是控制性能必须达到进一步优化。出于优化热功率响应的必要性,本文提出了一种新型的基于多层感知(MLP)的模型预测控制(MPC),该模型由基于MLP的预测模型和沿模型设计的控制输入组成。与给定性能指标的坡度相反的方向。从理论上证明,这种基于MLP的MPC保证了全局限制的闭环稳定性。最后,将这种基于MLP的新建MPC应用于Su-NSSS的热功率控制,其实现是通过在内部环路中使用当前现有的Su-NSSS功率级控制形成级联反馈控制环路来实现的。稳定,并在外部环路中使用此新MPC进行优化。数值仿真结果验证了理论结果的正确性,并在优化热功率响应方面取得了令人满意的改进。 (C)2018 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Energy》 |2018年第may15期|116-125|共10页
  • 作者单位

    Tsinghua Univ, Inst Nucl & New Energy Technol INET, Collaborat Innovat Ctr Adv Nucl Energy Technol Ch, Key Lab Adv Reactor Engn & Safety,Minist Educ, Beijing 100084, Peoples R China;

    Tsinghua Univ, Inst Nucl & New Energy Technol INET, Collaborat Innovat Ctr Adv Nucl Energy Technol Ch, Key Lab Adv Reactor Engn & Safety,Minist Educ, Beijing 100084, Peoples R China;

    Tsinghua Univ, Inst Nucl & New Energy Technol INET, Collaborat Innovat Ctr Adv Nucl Energy Technol Ch, Key Lab Adv Reactor Engn & Safety,Minist Educ, Beijing 100084, Peoples R China;

    Tsinghua Univ, Inst Nucl & New Energy Technol INET, Collaborat Innovat Ctr Adv Nucl Energy Technol Ch, Key Lab Adv Reactor Engn & Safety,Minist Educ, Beijing 100084, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Nuclear energy; Optimization; Model predictive control; Neural network;

    机译:核能;优化;模型预测控制;神经网络;

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