A Multi-Variable Coupled Control Strategy Based on a Deep Deterministic Policy Gradient Reinforcement Learning Algorithm for a Small Pressurized Water Reactor

Jie Chen (), Kai Xiao, Ke Huang, Zhen Yang, Qing Chu and Guanfu Jiang
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Jie Chen: National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu 610213, China
Kai Xiao: National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu 610213, China
Ke Huang: National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu 610213, China
Zhen Yang: National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu 610213, China
Qing Chu: National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu 610213, China
Guanfu Jiang: National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu 610213, China

Energies, 2025, vol. 18, issue 6, 1-26

Abstract: The reactor system has multivariate, nonlinear, and strongly coupled dynamic characteristics, which puts high demands on the robustness, real-time demand, and accuracy of the control strategy. Conventional control approaches depend on the mathematical model of the system being controlled, making it challenging to handle the reactor system’s dynamic complexity and uncertainties. This paper proposes a multi-variable coupled control strategy for a nuclear reactor steam supply system based on a Deep Deterministic Policy Gradient reinforcement learning algorithm, designs and trains a multi-variable coupled intelligent controller to simultaneously realize the coordinated control of multiple parameters, such as the reactor power, average coolant temperature, steam pressure, etc., and performs a simulation validation of the control strategy under the typical transient variable load working conditions. Simulation results show that the reinforcement learning control effect is better than the PID control effect under a ±10% FP step variable load condition, a linear variable load condition, and a load dumping condition, and that the reactor power overshooting amount and regulation time, the maximum deviation of the coolant average temperature, the steam pressure, the pressure of pressurizer and relative liquid level, and the regulation time are improved by at least 15.5% compared with the traditional control method. Therefore, this study offers a theoretical framework for utilizing reinforcement learning in the field of nuclear reactor control.

Keywords: reinforcement learning; deep deterministic policy gradient; small pressurized water reactor; multivariate control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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