Multi-model predictive function control based on neural network and its application to the coordinated control system of power plants

Author

Hou, Guolian ; Liu, Haitao ; Sun, Yi ; Zhang, Jianhua

Author_Institution

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

fYear

2010

fDate

26-28 May 2010

Firstpage

3950

Lastpage

3954

Abstract

The coordinated control system of boiler-turbine unit in power plants is a complicated multivariable system with nonlinear, uncertainty and strong coupling. In this paper the algorithm of multi-model predictive function based on neural network is proposed and it is applied in a 500 MW unit. Firstly, several linearized models of the unit on different working conditions are obtained with small deviation linearized method and the global predictive model is gained by the method of neural network weights. Then, the control variables are calculated by predictive function controller. Finally, the simulation results testify the validity of this control algorithm.

Keywords

multivariable control systems; neurocontrollers; nonlinear control systems; power station control; predictive control; steam power stations; boiler-turbine unit; control variables; coordinated control system; multimodel predictive function control; multivariable system; neural network; nonlinear control; power 500 MW; power plants; Control systems; Couplings; MIMO; Neural networks; Nonlinear control systems; Power generation; Power system modeling; Prediction algorithms; Predictive models; Uncertainty; linearized models; multi-model predictive function; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498453

Filename

5498453