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

Author

Liu, Chang-liang ; Liu, Ji-zhen ; Niu, W-guang ; Zeng, De-liang

Author_Institution

North China Electr. Power Univ., Baoding, China

Volume

1

fYear

2003

fDate

2-5 Nov. 2003

Firstpage

314

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.

Keywords

boilers; genetic algorithms; nonlinear systems; parameter estimation; power engineering computing; 300 MW; MATLAB program; elitist reservation; floating-point coding; genetic algorithm; grouping method; model identification; multivariables nonlinear boiler; operating data log files; premature convergence; rank-based selection; Algorithm design and analysis; Boilers; Convergence; Genetic algorithms; Genetic mutations; MATLAB; Mathematical model; Power system modeling; Signal processing algorithms; Water heating;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2003 International Conference on

Print_ISBN

0-7803-8131-9

Type

conf

DOI

10.1109/ICMLC.2003.1264493

Filename

1264493