基于改进粒子群算法的主蒸汽温度系统辨识

doi:10.16182/j.issn1004731x.joss.20-0609

摘要/Abstract

摘要： 建立主蒸汽温度精确的数学模型是提高控制系统性能的基础。针对传统的粒子群优化(Particle Swarm Optimization,PSO)算法在模型辨识中存在易早熟,收敛速度慢的问题,提出一种引入收缩因子的改进PSO算法。该算法通过调整收缩因子的方法来提高算法的全局寻优能力和收敛速度。将山西某电厂350 MW循环流化床(Circulating Fluidized Bed,CFB)锅炉现场运行的数据用于主蒸汽模型参数辨识中,利用改进PSO算法寻优得到主蒸汽温度系统最佳的模型参数,通过现场实际数据验证了模型的有效性,为CFB锅炉主蒸汽温度控制优化奠定了基础。

关键词: 主蒸汽温度, 改进粒子群算法, 收缩因子, 现场数据

Abstract: Establishing an accurate mathematical model of main steam temperature is the basis of improving the performance of control system. Aiming at the problems of early maturity and slow convergence in traditional particle swarm optimization (PSO) algorithm in model identification, an improved PSO algorithm with shrinkage factor is proposed. The algorithm improves the global optimization capability and convergence speed of the algorithm by adjusting the shrinkage factor. The on-site operating data of a 350 MW circulating fluidized bed (CFB) boiler in a power plant in Shanxi province are used in the identification of the main steam model parameters, and the improved PSO algorithm is used to optimize the model parameters of the main steam temperature system. The validity of the model is verified by actual data on-site, which lays the foundation for the optimization of main steam temperature control of CFB boilers.

Key words: main steam temperature, improved particle swarm algorithm, shrinkage factor, field data

中图分类号:

TP391.9

曹振乾, 印江, 张津华. 基于改进粒子群算法的主蒸汽温度系统辨识[J]. 系统仿真学报, 2021, 33(10): 2411-2419.

Cao Zhenqian, Yin Jiang, Zhang Jinhua. Identification of Main Steam Temperature System Based on Improved Particle Swarm Optimization[J]. Journal of System Simulation, 2021, 33(10): 2411-2419.

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