Application of Generalized Predictive Control in Reactor Core Variable Power Control

被引：0
|
作者：
Pan Y. [1 ,2 ]
Qian H. [1 ,2 ]
Jiang C. [1 ,2 ]
Liu X. [3 ]
机构：
[1] College of Automation Engineering, Shanghai University of Electric Power, Shanghai
[2] Shanghai Key Laboratory of Power Plant Automation Technology, Shanghai
[3] School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai
来源：
Hedongli Gongcheng/Nuclear Power Engineering | 2020年 / 41卷 / 02期
关键词：
Core power; Generalized predictive control; Non-linear model; On-line identification; Variable working condition;
D O I：
10.13832/j.jnpe.2020.02.0096
中图分类号：
学科分类号：
摘要：
Aiming at the nonlinear problem of the core power model caused by steady-state neutron density at different power levels, generalized predictive control (GPC) is applied to the core power control to realize the automatic control of the core power under variable working conditions. This paper firstly establishes a core power model based on zero-power core model and temperature feedback model. The prediction time domain is designed based on the order of the model, and the model parameters at different power levels are identified online by the least square method with forgetting factor in the GPC correction link according to the input and output data of the system. In order to verify the robustness of the controller, the reactive disturbance is added at full power smooth operation. The performance of the controller is verified by simulation based on MATLAB platform, and the results show that the GPC designed in this paper can quickly and accurately track the set value when the core is in variable working condition, and can identify the core model parameters of different power levels on-line, and has certain anti-interference ability. © 2020, Editorial Board of Journal of Nuclear Power Engineering. All right reserved.
引用
收藏
页码：96 / 101
页数：5
相关论文
共 6 条
  • [1] Hatami E., Vosoughi N., Salarieh H., Design of a fault tolerated intelligent control system for load following operation in a nuclear power plant, International Journal of Electrical Power & Energy Systems, 78, pp. 864-872, (2016)
  • [2] Tao X., Li N., Li S., Multiple model predictive control for large envelope flight of hypersonic vehicle systems, Information Sciences, 328, pp. 115-126, (2015)
  • [3] Ansarifar G.R., Rafiei M., Second-order sliding- mode control for a pressurized water nuclear reactor considering the xenon concentration feedback, Nuclear Engineering and Technology, 36, 1, pp. 94-101, (2015)
  • [4] Zhang Y.J., Chai T.Y., Wang H., An Adaptive Generalized Predictive Control Method for Nonlinear Systems Based on ANFIS and Multiple Models, Ieee Transactions on Fuzzy Systems, 18, pp. 1070-1082, (2010)
  • [5] Shi K., Wang B., Yang L., Takagi-Sugeno fuzzy generalized predictive control for a class of nonlinear systems, Nonlinear Dynamics, 89, 1, pp. 169-177, (2017)
  • [6] Ye Q., Lou X.Y., Sheng L., Generalized predictive control of a class of MIMO models via a projection neural network, Neurocompuing, 34, pp. 192-197, (2017)
1