Water level control of nuclear steam generators using intelligent hierarchical autonomous controller

Abstract

The challenge of water level control in steam generators, particularly at low power levels, has always been a critical aspect of nuclear power plant operation. To address this issue, this paper introduces an IHA controller. This controller employs a CPI controller as the primary controller for direct water level control, coupled with an agent-based controller optimized through a DRL algorithm. The agent dynamically optimizes the parameters of the CPI controller in real-time based on the system’s state, resulting in improved control performance. Firstly, a new observer information is obtained to get the accurate state of the system, and a new reward function is constructed to evaluate the status of the system and guide the agent’s learning process. Secondly, a deep ResNet with good generalization performance is used as the approximator of action value function and policy function. Then, the DDPG algorithm is used to train the agent-based controller, and an advanced controller with good performance is obtained after training. Finally, the popular UTSG model is used to verify the effectiveness of the algorithm. The results demonstrate that the proposed method achieves rise times of 73.9 s, 13.6 s, and 16.4 s at low, medium, and high power levels, respectively. Particularly, at low power levels, the IHA controller can restore the water level to its normal state within 200 s. These performances surpass those of the comparative methods, indicating that the proposed method excels not only in water level tracking but also in anti-interference capabilities. In essence, the IHA controller can autonomously learn the control strategy and reduce its reliance on the expert system, achieving true autonomous control and delivering excellent control performance.