摘要
为满足火电机组快速深度变负荷工况下主蒸汽温度控制器参数整定需求,将选择算子、自适应学习因子、自适应惯性权重系数引入标准粒子群算法中,提出多策略分区勘探粒子群算法。该方法根据适应度评价指标,将种群划分为标准粒子群操作区、选择算子操作区、自适应学习因子操作区、自适应惯性权重系数操作区4个分区,以充分发挥各寻优策略的优势,保证算法具有较高收敛精度,同时具有较快的收敛速度。与其他5种改进粒子群算法进行对比实验,结果表明:本文所提算法寻优精度高,收敛时间短。将本文算法与衰减曲线法、3种改进粒子群算法分别应用于主蒸汽温度串级PID控制器参数优化,仿真实验结果验证了本文算法的有效性。
In order to meet the parameter tuning requirements of main steam temperature controller under fast and deep variable load conditions of thermal power units,the selection operator,adaptive learning factor and adaptive inertia weight coefficient are introduced into the standard particle swarm optimization algorithm,and the multi strategy zonal exploration particle swarm optimization algorithm is proposed.According to the fitness evaluation index,the population is divided into four regions:standard particle swarm operation region,selection operator operation region,adaptive learning factor operation region and adaptive inertia weight coefficient operation region,so as to give full play to the advantages of each optimization strategy,and ensure that the algorithm has higher convergence accuracy and faster convergence speed.Compared with four other improved particle swarm optimization algorithms,the results show that the proposed algorithm has high optimization accuracy and short convergence time.Moreover,the proposed algorithm,attenuation curve method and three improved particle swarm optimization algorithms are applied to the parameter optimization of main steam temperature cascade PID controller respectively.The simulation results verify the effectiveness of the proposed algorithm.
作者
刘萌
王印松
牟文彪
杨敏
陆陆
LIU Meng;WANG Yinsong;MOU Wenbiao;YANG Min;LU Lu(School of Control and Computer Engineering,North China Electric Power University,Baoding 071000,China;Zhejiang Provincial Energy Group Co.,Ltd.,Hangzhou 310007,China;Zhejiang Provincial Key Laboratory of Energy Conservation&Pollutant Control Technology for Thermal Power,Hangzhou 310000,China)
出处
《热力发电》
CAS
CSCD
北大核心
2021年第7期23-30,共8页
Thermal Power Generation
基金
国家自然基金联合基金项目(U1709211)。
关键词
粒子群优化算法
主蒸汽温度
参数优化
串级控制
分区勘探
PID
收敛速度
particle swarm optimization algorithm
main steam temperature
parameter optimization
cascade control
zonal exploration
PID
convergence speed
