Predictive control of nonlinear model for heat collecting system with terminal limit in solar thermal power generation system
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Predictive control of nonlinear model for heat collecting system with terminal limit in solar thermal power generation system
Thermal Power GenerationVol. 44, Issue 10, Pages: 63-67(2015)
作者机构:
兰州交通大学自动化与电气工程学院
作者简介:
基金信息:
DOI:
CLC:TM615
Published:2015
稿件说明:
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LU Xiaojuan, DONG Haiying. Predictive control of nonlinear model for heat collecting system with terminal limit in solar thermal power generation system[J]. Thermal power generation, 2015, 44(10): 63-67.
DOI:
LU Xiaojuan, DONG Haiying. Predictive control of nonlinear model for heat collecting system with terminal limit in solar thermal power generation system[J]. Thermal power generation, 2015, 44(10): 63-67.DOI:
Predictive control of nonlinear model for heat collecting system with terminal limit in solar thermal power generation system
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 prediction model of controller design.The terminal restricted conditions were converted to control quantity constraint and hybrid optimization model formed by particle swarm optimization(PSO)algorithm and the gradient descent method was added to model prediction rolling optimization process
which can obtain the optimal 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 results show that
the tracking error of the PSO-TLNMPC control algorithm is small and the control precision is high
which has better control effect than the basic TLNMPC algorithm.