摘要
为提高脱硫系统动态矩阵算法(DMC)的控制精度,使控制器参数能够自动寻优,提出采用自适应混合粒子群算法优化DMC中的参数。首先以粒子群算法为基础,加入自适应权重和局部因子构建自适应混合粒子群,并通过Griewank函数验证自适应混合粒子群的寻优性能;接着搭建DMC模型,使用自适应混合粒子群算法对DMC的控制时域、优化时域等参数进行迭代寻优,最后以浆液密度和机组负荷作为干扰因素对脱硫系统进行控制仿真及抗干扰测试。以某电厂600 MW机组配置脱硫塔浆液pH值为研究对象,将电厂实际运行数据作为输入检验控制系统特性。仿真结果表明:与传统PID控制以及Smith预估控制相比,自适应混合粒子群优化DMC控制下浆液pH值上升时间更短,控制更集中,波动范围小,在设定值±0.02范围内覆盖率达到99.41%。
In order to improve the control accuracy of the dynamic matrix algorithm(DMC)for desulfurization systems and facilitate automatic optimization of controller parameters,we propose an adaptive hybrid particle swarm optimization algorithm to optimize the parameters in DMC.Firstly,we construct an adaptive hybrid particle swarm by incorporating adaptive weights and local factors into the particle swarm algorithm.Then verify the optimization performance of the adaptive hybrid particle swarm through the Griewank function.Subsequently,we build a DMC model and use the adaptive hybrid particle swarm optimization algorithm to iteratively optimize control time domain,optimization time domain,and other parameters of the DMC.Finally,we conduct control simulation and anti-interference testing on the desulfurization system using slurry density and unit load as interference factors.Taking the pH value of desulfurization tower slurry configured for a 600 MW unit in a certain power plant as the research object,we use the actual operating data of the power plant as input to verify the characteristics of the control system.The simulation results show that compared to traditional PID control and Smith predictive control,the adaptive hybrid particle swarm optimization DMC control offers a shorter rise time for the slurry pH value,more centralized control,and a smaller fluctuation range.The coverage rate reaches 99.41%within the set value±0.02 range.
作者
王惠杰
李绍鑫
许小刚
秦志明
WANG Huijie;LI Shaoxin;XU Xiaogang;QIN Zhiming(School of Energy Power and Mechanical Engineering,North China Electric Power University,Baoding 071003,China)
出处
《华北电力大学学报(自然科学版)》
北大核心
2025年第4期125-133,142,共10页
Journal of North China Electric Power University(Natural Science Edition)
基金
中央高校基本科研业务费专项资金资助项目(2019MS094).
关键词
自适应混合粒子群算法
动态矩阵
PH值
控制优化
adaptive hybrid particle swarm optimization algorithm
dynamic matrix
pH value
control optimization
