基于改进混合蛙跳算法的氧量对象模型辨识  

Identification of Oxygen Object Model based on Improved Shuffled Frog Leaping Algorithm

作　　者：孙宇贞 黄晓筱 郭皓文 SUN Yu-zhen;HUANG Xiao-xiao;GUO Hao-wen(Shanghai Engineering Research Center of Intelligent Management and Control for Power Process,College of Automation Engineering,Shanghai University of Electric Power,Shanghai,China,Post Code:200090)

机构地区：[1]上海电力大学自动化工程学院上海发电过程智能管控工程技术研究中心,上海200090

出　　处：《热能动力工程》2020年第1期185-190,共6页Journal of Engineering for Thermal Energy and Power

基　　金：上海市科技创新行动计划高新技术领域项目(1751109402)。

摘　　要：针对混合蛙跳算法易早熟、寻优精度低的缺陷,将混沌初始化和局部变异高斯因子引入混合蛙跳算法,进行种群初始化和局部搜索的改进。将改进后的混合蛙跳算法用于火电厂过程模型辨识,以某1000 MW火电机组双输入单输出的尾部烟气含氧量模型为辨识对象进行系统辨识。结果表明:与混合蛙跳算法相比,改进的混合蛙跳算法种群遍历性更广,局部搜索更快,能够避免陷入局部最优解,全局寻优能力更强,寻优精度更高,收敛速度更快,稳定性更好。To overcome the shortcomings of the shuffled frog leaping algorithm,such as premature aging and low precision,the chaotic initialization and local variation Gaussian factor are introduced into the shuffled frog leaping algorithm to improve the population initialization and local search.And the improved shuffled frog leaping algorithm is applied to the identification of thermal power plant process models.With a 1000 MW thermal power unit,the system identification is performed for the oxygen content model of the tail flue gas with two inputs and one output.The identification results show that,compared with the shuffled frog leaping algorithm,the improved shuffled frog leaping algorithm has wider ergodicity and faster local search,which can avoid falling into local optimal solution,and has better global optimization ability,higher precision,faster convergence and better stability.

关 键 词：混沌初始化 高斯因子 锅炉燃烧系统 模型辨识 

分 类 号：TM621[电气工程—电力系统及自动化]