Study of an algorithm of GA-RBF neural network generalized predictive control for Generating Unit

Author

Li, Ning ; Ling, Hujun

Author_Institution

Coll. of Electr. Power, Inner Mongolia Univ. of Technol., Huhhot, China

fYear

2011

fDate

15-17 April 2011

Firstpage

1723

Lastpage

1726

Abstract

With the development of power industry, the proportion of Large-scale Generating Unit in power grid is getting bigger and bigger. The control object of the generating unit is a complicated manufacturing process which is strong-coupling, time-variable, nonlinear and big-lag. It is difficult to establish accurate model when the parameters of control object is uncertainty because of all disturbances, and it is a complex rambunctious large-scale production process. The efficient way to solve the problem is coordinated control system which is developed based on conventional local control system. GA-RBF network is used to identify the coordinated control system by establishing a predictive model in generalized predictive control strategy, and achieve predictive control with online rolling optimization and real time feedback revision. The results of the simulation show the availability of it.

Keywords

feedback; genetic algorithms; large-scale systems; neurocontrollers; nonlinear control systems; power generation control; predictive control; radial basis function networks; time-varying systems; GA-RBF neural network generalized predictive control; big-lag process; coordinated control system; genetic algorithm; large-scale generating unit; large-scale production process; manufacturing process; nonlinear process; online rolling optimization; power grid; power industry; real time feedback revision; strong-coupling process; time-variable process; uncertain control object parameters; Artificial neural networks; Genetic algorithms; Load modeling; Mathematical model; Optimization; Predictive control; Predictive models; Genetic algorithm; RBF neural network; generalized predictive control; generating unit;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Information and Control Engineering (ICEICE), 2011 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-8036-4

Type

conf

DOI

10.1109/ICEICE.2011.5777819

Filename

5777819