Neuro-Fuzzy Generalized Predictive Control of Boiler Steam Temperature

Author

Liu, Xiang-jie ; Liu, Ji-zhen

Author_Institution

Dept. of Autom., North China Electr. Power Univ., Beijing

Volume

2

fYear

0

fDate

0-0 0

Firstpage

6531

Lastpage

6535

Abstract

Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. This is often difficult to achieve using conventional PI controllers, due to the nonlinearities and uncertainties. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPQ is proposed in this paper, which consists of local GPCs designed using the local linear models of the neuro-fuzzy network. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional cascade PI controller or the linear GPC is obtained

Keywords

PI control; boilers; control engineering computing; fuzzy control; fuzzy neural nets; nonlinear control systems; power plants; power system control; power system simulation; predictive control; 200 mW; PI controllers; boiler steam temperature; generalized predictive control; neuro-fuzzy network; nonlinear controller; plant simulation; power plant operation; Boilers; Fuzzy logic; Fuzzy neural networks; Neural networks; Power generation; Predictive control; Predictive models; Spraying; Temperature control; Uncertainty; generalized predictive control; neuro-fuzzy networks; superheated steam temperature;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714344

Filename

1714344