Genetic algorithm-based multi-variables nonlinear boiler model identification for 300 MW power unit

Abstract

A kind of improve genetic algorithm for identifying multi-variables nonlinear boiler model of 300 MW power unit is introduced. In the algorithm, floating-point coding, rank-based selection, elitist reservation and grouping method are used, and the premature convergence is restrained, and the searching ability is improved. The genetic algorithm-based model identification MATLAB program is designed and the model parameters can be gotten with it according to the operating data log files. It is shown by simulation research that the multi-variables nonlinear model can be identified accurately no matter what kind of input signal is used.