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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 coupling of each parameter of superheater model, a method of parameter optimization of mechanism model based on genetic algorithm is proposed. The mathematical model of the superheater is established, the optimization parameters are determined, and the genetic algorithm is used to optimize until the accuracy of the model reaches the requirement. Simulation results show that the superheater model established by this method achieves the predetermined accuracy; the optimization process is carried out automatically and the modeling and optimization time are shortened. This method has the versatility, is simple and easy, and provides a new idea and method for the mathematical modeling and parameter optimization of thermal power plant simulator.