过热器机理模型的遗传算法参数优化方法研究

Study of Parameter Optimization Using Genetic Algorithm for Mechanism Model of Superheater

针对过热器模型各参数存在的强耦合性,提出了基于遗传算法的机理模型参数优化方法。建立过热器数学模型,确定优化参数,应用遗传算法进行优化,直到模型精度达到要求。仿真研究表明,运用该方法建立的过热器模型达到预定精度要求;优化过程自动进行,缩短了建模和优化时间。这种方法具有通用性,简单易行,为火电厂仿真机数学建模和参数优化提供一种新的思路和方法。

Aiming at the strong couple of parameter optimization for superheater mechanism model, the genetic algorithm was put forward to optimize parameters. The mechanism model was built and optimization parameters were selected. GA was applied until model errors were less than permitted error. Simulation research shows that superheater model reaches the accuracy in this method without manual adjustment and optimization time is shorten. It is general and simple, and provides a new way for parameter optimization for thermal device mechanism model in power plant.