摘要
压水反应堆口WR）功率系统是受约束的本质非线性系统，其负荷跟踪能力是核电机组安全高效运行的重要保障。模型预测控制（MPC）具有内在的约束处理能力，是PWR功率控制的有效方法，采用PWR非线性模型，设计了基于字典序优化的非线性MPC（NMVC）。首先，将PWR功率控制问题转化为具有不同目标优先级的字典序优化问题。其次，考虑控制棒执行机构精度，采用粒子群优化（Ps0）高效求解NMPC。最后，将该控制器应用于PWR的负荷跟踪仿真，结果表明了算法的有效性。
The pressurized water reactor （PWR） power system is a constrained essential nonlinear system, and its load tracking ability is an important guarantee for the safe and efficient operation of PWR. Due to the inherent constraint handling ability, model predictive control （MPC） has been a quite effective way of controlling the power level of PWR. Based on the nonlinear model of PWR, a nonlinear MPC （NMPC） is presented using lexicographic optimization method. Firstly, the PWR power control problem is reformulated as the lexicographic one that can deal with the different prioritization of objectives. Then, considering the accuracy of the control rod actuator, particle swarm optimization （PSO） algorithm is used to solve NMPC online efficiently. Finally, this controller is applied to the load following power-level regulation of PWR. The numerical simulation results show the effectiveness of the proposed controller.
作者
姜頔
刘向杰
JIANG Di;LIU Xiang-jie(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)
出处
《控制工程》
CSCD
北大核心
2018年第4期577-586,共10页
Control Engineering of China
基金
国家自然科学基金(61673171,61533013)
作者简介
姜頔（1989-），男，安徽阜阳人，博士研究生，主要研究方向为非线性模型预测控制在核反应堆控制上的应用等.;刘向杰（1966-），男，北京人，博士，教授，博士生导师，主要从事先进控制策略在电力过程控制等方面的教学与科研工作。
