基于粒子群算法的CFB机组协调系统模型辨识  

Model Identification of CFB Unit Coordination System Based on Particle Swarm Optimization

作　　者：白建云[1] 邵茹 BAI Jian-yun;SHAO Ru(College of Automation and Software,Shanxi University,Taiyuan 030013,China)

机构地区：[1]山西大学自动化与软件学院,太原030013

出　　处：《自动化与仪表》2020年第9期84-89,共6页Automation & Instrumentation

基　　金：国家自然科学基金项目(U1610116);山西省科技重大专项项目(MD2016-02)。

摘　　要：为了消纳风光等新能源,火电机组均需要参与到调峰调频中,机炉协调控制系统是机组AGC模式下的核心。熟悉控制系统被控对象的特性并且建立系统的动态模型,是提高系统控制品质的基础。该文以山西某电厂超临界350 MW循环流化床机组协调系统为研究对象,首先对研究对象的动态特性进行分析,依据生产现场闭环运行数据,利用标准粒子群智能算法,寻优得到两输入两输出的协调系统模型。然后选取其他时间段的采样数据作为模型验证数据,证实利用标准PSO算法对多变量系统建模的可行性。In order to absorb new energy sources such as wind and light,thermal power units need to participate in peak and frequency modulation,and the boiler and boiler coordinated control system is the core of the unit’s AGC mode.Familiar with the characteristics of the controlled object of the control system and establishing the dynamic model of the system are the basis for improving the quality of the system control.This article takes the supercritical 350 MW circulating fluidized bed unit coordination system of a power plant in Shanxi as the research object.First,the dynamic characteristics of the research object are analyzed.Based on the closed-loop operating data of the production site,the standard particle swarm intelligence algorithm is used to obtain two inputs and two outputs for optimization.Model of the coordination system.Then select the sampling data from other time periods as model verification data to confirm the feasibility of using standard PSO algorithm to model multivariable system.

关 键 词：超临界 循环流化床机组 协调系统 两输入两输出 标准PSO 模型辨识 

分 类 号：TK39[动力工程及工程热物理—热能工程]