Research on Power Plant Superheated Steam Temperature Based on Least Squares Support Vector Machines

Author

Wang, Yong ; Liu, Jizhen ; Liu, Xiangjie ; Tan, Wen

Author_Institution

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

Volume

1

fYear

0

fDate

0-0 0

Firstpage

4752

Lastpage

4756

Abstract

Aiming at the strong nonlinearity and large time-varying characteristics in controlling of super-heater temperature in plant, the method of LS-SVMs based on radial basis function are used to model. Under the condition of modeling approximating to performance, the sparse modeling is gotten by the pruning algorithm. The merits of the algorithm are conforming to the least structural risk in training process and hardly leading to over-fitting. The simulation of a superheating system, in one super critical 600MW direct boiler in one power plant, is taken. The result shows that the controlling system can be adapt to the variation of the object characteristic well with strong nonlinearity and large time-varying characteristics rapidly

Keywords

boilers; control nonlinearities; heat transfer; steam plants; support vector machines; temperature control; time-varying systems; 600MW direct boiler; large time-varying control; least squares support vector machines; least structural risk; nonlinearity control; power plant superheated steam temperature; pruning algorithm; radial basis function; sparse modeling; superheating system; Boilers; Control systems; Least squares approximation; Least squares methods; Nonlinear control systems; Power generation; Power system modeling; Support vector machines; Temperature control; Time varying systems; RBF; Sparse; least squares; pruning; support vector machine;

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.2006.1713285

Filename

1713285