摘要
针对300MW发电机组的历史运行数据,对以给煤量、一次风量、排渣量为输入量,床温和主汽压为输出量的锅炉燃烧系统进行建模研究。基于发电厂采集的运行数据,首先对其分析和预处理,然后根据给煤量、一次风量、排渣量引起的床温和主汽压的变化特性,利用粒子群算法辨识出其在不同工况下的燃烧系统模型矩阵,并分析系统的动态特性。误差范围内的拟合结果表明,所建立模型可完全表征对应工况下的系统特性,模型的输出能够较好地跟踪机组实际生产过程曲线,既体现出所建模型的高精度性,也表明了粒子群算法的有效性和强泛化性,同时可为后续控制器的优化设计提供部分依据。
Aiming at the historical operation data of 300 MW generating units, the we modeled and studied a boiler combustion system which takes coal feed quantity, primary air flow and slag discharge quantity as inputs and bed temperature and main steam pressure as output. Based on the operation data collected by power plant, the combustion system model matrix under different conditions was identified by using particle swarm optimization(PSO) according to the characteristics of bed temperature and main steam pressure caused by coal feed quantity, primary air flow quantity and slag discharge quantity, and the dynamic characteristics of the system were analyzed. The fitting results within the error range show that the model can fully characterize the system characteristics under the corresponding operating conditions. The output of the model can better track the actual production process curve of the unit, which not only reflects the high accuracy of the model, but also shows the effectiveness and generalization of the particle swarm optimization algorithm. At the same time, it can provide part of the basis for the optimization design of the subsequent controller.
作者
晏恒
胡林静
仝傲宇
YAN Heng;HU Lin-jing;TONG Ao-yu(Electric Power College,Inner Mongolia University of Technology,Hohhot Inner Mongolia 010080,China)
出处
《计算机仿真》
北大核心
2021年第6期61-66,423,共7页
Computer Simulation
关键词
循环流化床
燃烧系统
粒子群优化算法
建模
仿真
Circulating fluidized bed
Combustion system
Particle swarm optimization algorithm
Modeling
Simulation
