基于樽海鞘群算法的床温多变量系统辨识研究  

Research on Bed Temperature Identification of CFB Boiler Based on Salp Swarm Algoritm

作　　者：赵威 钱进[1] 王一桂 黄凤启 ZHAO Wei;QIAN Jin;WANG Yi-gui;HUANG Feng-qi(The Electrical Engineering College,Guizhou University,Guiyang 550025 China;Power China Guizhou Engineering Co.,Ltd.,Guiyang 550025 China;Guotou Panjiang Power Generation Co.,Ltd.,Panzhou 553000 China)

机构地区：[1]贵州大学电气工程学院,贵州贵阳550025 [2]中国电建集团贵州工程有限公司,贵州贵阳550025 [3]国投盘江发电有限公司,贵州盘州553000

出　　处：《自动化技术与应用》2024年第2期1-5,共5页Techniques of Automation and Applications

基　　金：贵州省科技支撑计划项目([2020]2Y040)。

摘　　要：针对热工系统具有多输入多输出的特点,介绍将多输入多输出系统简化为多个多输入单输出系统的具体过程。利用机组的实际运行数据,建立在50%负荷运行工况下,以给煤量、煤泥量和一次风量为输入,以床温和主蒸汽压力为输出的多变量系统模型。为提高模型参数辨识的精度,采用樽海鞘群算法(SSA)对多变量系统的模型参数进行寻优。该算法采用一种新的群体更新模型,算法流程简单。仿真结果表明,相比于传统粒子群算法(PSO),樽海鞘群算法运算速度明显提高,可以获到更优的辨识模型。Aiming at the multi-input and multioutput characteristic of the thermal system,the specific process of simplifying the multiple in-put and multiple output systems into multiple multiple input and single output systems is introduced.A multivariable system mod-el is established under 50%load operation condition,which is based on the actual operating data.The field operating data is used for the identification of model parameter.The coal-feed quantity,slime quantity and primary air volume are selected as input vari-ables,and the bed temperature and the main steam pressure are selected as the outputs.In order to improve the accuracy of model parameter identification,salp swarm algorithm(SSA)is used to optimize the model parameters of multivariable system.This algo-rithm adopts a new group updating model with simple algorithm flow.The simulation results show that compared with the tradi-tional particle swarm optimization(PSO),salp swarm algorithm is faster and can obtain a better identification model.

关 键 词：多变量系统 床温 樽海鞘群算法 参数辨识 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]