State-Space Model Predictive Control Method for Core Power Control in Pressurized Water Reactor Nuclear Power Stations

• Main Authors: Guoxu Wang, Jie Wu, Bifan Zeng, Zhibin Xu, Wanqiang Wu, Xiaoqian Ma
• Format: Article
• Language: English
• Published: Elsevier 2017-02-01
• Series: Nuclear Engineering and Technology
• Subjects: Core Power Control; Pressurized Water Reactor; Quadratic Programming; State-Space Model Predictive Control
• Online Access: http://www.sciencedirect.com/science/article/pii/S1738573316301292

A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.