粒子群参数辨识的超(超)临界锅炉负荷响应数学建模  

Load response mathematical model for(ultra)supercritical boilers based on parameter identification with particle swarm optimization algorithm

作　　者：柯尊光 高海东[2] 高林[2] 王田[2] 吕永涛[2] 

机构地区：[1]神华神东电力重庆万州港电有限责任公司,重庆404027 [2]西安热工研究院有限公司,陕西西安710032

出　　处：《热力发电》2015年第10期102-106,共5页Thermal Power Generation

摘　　要：为了适应电网系统中长期稳定性分析的需要,构建了具有线性传递函数形式的超(超)临界锅炉非线性数学模型,同时考虑了模型变量之间在静态和动态过程中的非线性特性,以便通过扰动试验获取准确反映超(超)临界锅炉负荷响应特性的模型参数。针对某超临界600 MW机组,采用基于粒子群(PS0)算法的超(超)临界锅炉非线性数学模型参数辨识方法,进行不同机组负荷工况下的模型参数辨识。结果表明:只需在不同负荷点上进行模型参数辨识,即可获得超(超)临界锅炉非线性模型中的非线性函数;辨识负荷点数量越多,非线性函数曲线越平滑,非线性拟合效果也越好;模型输出曲线与实测输出曲线吻合较好,所建模型及其参数辨识方法可行。To meet the demands of power grid stability analysis in medium and long-term period,a new non-linear mathematical model with linear transfer function form for （ultra）supercritical boilers was built. Both steady and dynamic nonlinear characteristics were considered to make the model parameters that can accurately reflect the practical boiler＆#39;s load response characteristics be easy to be obtained through disturb-ance tests.Moreover,taking a supercritical 600 MW unit as an example,the particle swarm optimization （PSO）algorithm based parameter identification method was employed to identify parameters of the nonlin-ear mathematical model.The results show that,the nonlinear function in nonlinear model of the （ultra）su-percritical boilers can be obtained by model parameter identification at each load point.The large the load point number,the smoother the nonlinear function curve and the better the nonlinear fitting effect.The simulation results of the identified model were in accordance with the test output,indicating the proposed model and its parameter identification method are feasible.

关 键 词：超(超)临界 锅炉 负荷响应 数字模型 非线性 PSO算法 参数辨识 

分 类 号：O231[理学—运筹学与控制论]