Open AccessThe application of Deep Reinforcement Learning in Coordinated Control of Nuclear Reactors
Author(s) -
Jing Li,
Yanyang Liu,
Xianguo Qing,
Kai Xiao,
Ying Zhang,
Pengcheng Yang,
Yang Yue
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2113/1/012030
Subject(s) - reinforcement learning , nuclear power , control (management) , computer science , nuclear reactor , nuclear power plant , reinforcement , control system , control engineering , control theory (sociology) , artificial intelligence , engineering , nuclear engineering , ecology , physics , electrical engineering , structural engineering , nuclear physics , biology
The nuclear reactor control system plays a crucial role in the operation of nuclear power plants. The coordinated control of power control and steam generator level control has become one of the most important control problems in these systems. In this paper, we propose a mathematical model of the coordinated control system, and then transform it into a reinforcement learning model and develop a deep reinforcement learning control algorithm so-called DDPG algorithm to solve the problem. Through simulation experiments, our proposed algorithm has shown an extremely remarkable control performance.