摘要
为保证超(超)临界机组过热蒸汽温度控制品质良好,提出了基于改进粒子群算法的阶梯式广义预测控制方法。首先利用模拟退火算法避免了粒子群算法(PSO)易于陷入局部最优问题,然后将改进后的PSO算法引入广义预测控制(GPC)的滚动优化环节中。建立了锅炉过热汽温的阶梯式广义预测串级控制系统。仿真结果表明,在不同负荷以及变工况下,相比于串级PID和传统广义预测控制,所提出的控制策略使过热汽温控制系统表现出更好的给定值跟随性能、良好的抗干扰性及负荷适应性。
In order to ensure the good quality of superheated steam temperature of the supercritical(super)critical unit,a superheated steam temperature control method is proposed,which is based on the stepped generalized predictive control under the improved particle swarm algorithm.First,the simulated annealing algorithm(SA)is used to prevent the particle swarm algorithm(PSO)from falling into the local optimal problem.Then the improved PSO algorithm is introduced into the rolling optimization link of the generalized predictive control(GPC).Finally,a stepped generalized predictive cascade control system for boiler superheated steam temperature is established.The simulation results show that compared to cascade PID and traditional generalized predictive controls in the superheated steam temperture control system under different loads and variable conditions,the proposed control strategy shows better setpoint following performanceand has good anti-interference and load adaptability.
作者
杨硕
张悦
YANG Shuo;ZHANG Yue(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China)
出处
《电力科学与工程》
2021年第12期47-56,共10页
Electric Power Science and Engineering
关键词
过热汽温控制系统
粒子群算法
模拟退火算法
广义预测控制
superheated temperature control system
particle swarm algorithm
simulated annealing algorithm
generalized predictive control
