摘要
针对线性菲涅尔太阳能热发电集热系统的平稳性问题,提出了基于终端受限非线性模型预测控制(TLNMPC)策略。建立了系统预测模型;将终端约束条件转换为对控制量的约束条件,在模型预测滚动优化过程中加入粒子群(PSO)算法和梯度下降法形成混合寻优(PSOTLNMPC)算法;采用反馈校正修正模型预测值;按照不同的天气设定集热系统出口导热油温度目标曲线;采用MATLAB软件,通过TLNMPC算法和PSO-TLNMPC算法跟踪目标曲线,对比2种算法的控制效果。结果表明:在不同太阳辐射强度下,PSO-TLNMPC算法精度均较TLNMPC算法高,且滞后时间短,均方差显著小于TLNMPC。因此,PSO-TLNMPC算法跟踪误差小且控制精度高,控制效果优于TLNMPC算法。
Aiming at solving the stability problem of linear Fresnel solar thermal power generation system, the terminal restricted nonlinear model predictive control （TLNMPC）strategy was put forward.The heat loss of heat transfer process was ignored and the system mathematical model was established as the predic-tion model of controller design.The terminal restricted conditions were converted to control quantity con-straint and hybrid optimization model formed by particle swarm optimization （PSO）algorithm and the gra-dient descent method was added to model prediction rolling optimization process,which can obtain the opti-mal control of system quickly and accurately.Feedback correction was applied to modify the predicted value timely.According to different weather conditions,the target curve of outlet temperature was set.Through the MATLAB software,the TLNMPC and hybrid optimization TLNMPC （PSO-TLNMPC）were applied to track the target curve,and the predictive control results of two control strategies were compared.The re-sults show that,the tracking error of the PSO-TLNMPC control algorithm is small and the control preci-sion is high,which has better control effect than the basic TLNMPC algorithm.
出处
《热力发电》
CAS
北大核心
2015年第10期63-67,共5页
Thermal Power Generation
基金
863高新技术项目(2013AA050401)
甘肃省自然科学基金(145RJZA128)
关键词
线性菲涅尔
太阳能热发电
集热系统
预测控制
混合寻优
终端受限非线性模型
粒子群寻优算法
linear Fresnel
solar thermal power generation
heating system
predictive control
hybrid optimi-zation
terminal limit nonlinear model
particle swarm optimization
