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System-level modeling, analysis and coordinated control design for the pressurized water reactor nuclear power system

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  • Cui, Chengcheng
  • Zhang, Junli
  • Shen, Jiong

Abstract

A flexible operation of the nuclear power system (NPS) over a large operating range is emphasized when it is used as marine power. The current study exposes two bottlenecks in this field: (1) lack of the system-level high-quality dynamic model whose structure is simple enough for control design limits the implementation of the model-based controller; (2) nonlinearity problem due to the large load variation poses challenges for NPS to maintain its performance over a wide operating range. For these reasons, a first-hand simplified nonlinear model is developed and validated for the pressurized water reactor nuclear power system (PWRNPS) through mixed modeling methods of mechanism analysis and data identification. An optimal 50% full-power compromise steady-state operation scheme is first proposed for PWRNPS to balance its primary coolant temperature and secondary steam pressure. Using the simplified nonlinear model as the predictive model, a novel nonlinear model predictive controller (NMPC) is designed to realize the wide-range flexible operation of PWRNPS. Especially, an integrating tracking error is innovatively added to the cost function of NMPC to achieve offset-free control. Two groups of simulations on a complex PWRNPS process developed on the MATLAB/SIMULINK platform demonstrate the effectiveness of the proposed methods.

Suggested Citation

  • Cui, Chengcheng & Zhang, Junli & Shen, Jiong, 2023. "System-level modeling, analysis and coordinated control design for the pressurized water reactor nuclear power system," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223018662
    DOI: 10.1016/j.energy.2023.128472
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    2. Zhang, Tianhao & Dong, Zhe & Huang, Xiaojin, 2024. "Multi-objective optimization of thermal power and outlet steam temperature for a nuclear steam supply system with deep reinforcement learning," Energy, Elsevier, vol. 286(C).

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