基于神经网络的超临界机组数学模型  

Mathematical Model Based on Neural Network for Supercritical Unit

作　　者：马良玉[1] 高志元[1] 

机构地区：[1]华北电力大学控制与计算机工程学院,保定071003

出　　处：《动力工程学报》2013年第7期517-522,共6页Journal of Chinese Society of Power Engineering

基　　金：国家自然科学基金资助项目(61174111);中央高校基本科研业务费专项资金资助项目(09MG21)

摘　　要：以某600MW超临界机组为例,采用神经网络方法建立了机组负荷、汽压特性的数学模型,并运用大量的宽范围变工况数据完成了对模型的训练.仿真结果表明:模型可以很好地拟合机组负荷、主蒸汽压力与燃料量、给水量和汽轮机调门开度间复杂的非线性动态特性,且精度高、泛化能力强,可作为预测模型用于超临界机组智能控制器的设计.Taking a 600 MW supercritical unit as an object of study, a mathematical model was set up for the unit load and steam pressure characteristics based on neural network, which was subsequently trained with large amount of data obtained under wide-range varying load conditions. Simulation results show that the model can well fit the complex non-linear dynamic characteristics between unit load, main steam pressure and the fuel supply, feedwater flow and turbine governing valve opening with high precision and strong generalization ability, which therefore may serve as a prediction model for design of intelligent con- troller in supercritical units.

关 键 词：超临界机组 数学模型 神经网络 协调控制系统 

分 类 号：TK229.2[动力工程及工程热物理—动力机械及工程]