摘要
针对热工对象传递函数模型辨识精度不高的问题,提出一种新的建模方法。首先对1000MW超超临界机组锅炉主汽压控制系统进行了动态特性分析,将传统的相关函数法与智能粒子群算法相结合,挖掘和筛选电厂的现场运行历史数据,对该系统进行了传递函数建模。最后,得到了主汽压控制系统的传递函数,并用其他时段的现场数据进行了模型的验证。辨识得到的模型能够代表同类机组一段时间内的热工特性,为大型超超临界锅炉的控制器设计与优化奠定了基础。
Aiming at the problem that identification accuracy of thermal transfer function model is not high, a new modeling method was presented in the paper. Based on the analysis of the dynamic characteristics of 1000MW Ultra Supercritical （USC） boiler＇ s main steam pressure control system, the traditional correlation function and intelligence particle swarm optimization algorithm were combined. Exploiting useful data from the historical operating data of the power plant, the main steam pressure control system＇ s transfer function model was built. Meanwhile, the model vali- dations have been given. The results can represent the thermal characteristics of the similar units under certain condi- tion, which will lay the foundation for the controller design and optimization of large USC boiler.
出处
《计算机仿真》
CSCD
北大核心
2014年第3期151-154,260,共5页
Computer Simulation
关键词
超超临界锅炉
互相关函数
粒子群算法
建模
USC boiler
Cross -correlation function
Particle swarm algorithm （PSO）
Modeling
