The neural network control application in a power plant boiler

Author

Li, Jianyong ; Ososanya, Esther T. ; Smoak, Robert A.

fYear

1996

fDate

11-14 Apr 1996

Firstpage

521

Lastpage

524

Abstract

Two neural networks are used in the control of power plant boiler throttle pressure and megawatt load, where one network acts as an emulator, and the other as a controller. The learning scheme is a two-phase procedure in which the first involves training the emulator in mapping the plant dynamics and the second to train a controller network to learn the desired performance using a backpropagation algorithm and minimize plant output error cost function. This example illustrates the potential application of neural network technique in the power plant control area

Keywords

backpropagation; boilers; controllers; load regulation; neurocontrollers; power control; power station control; power station load; pressure control; thermal power stations; backpropagation algorithm; controller; emulator; learning scheme; megawatt load control; neural network control; output error cost function minimisation; plant dynamics mapping; power plant boiler; throttle pressure control; training; two-phase procedure; Artificial neural networks; Biological neural networks; Boilers; Control systems; Multi-layer neural network; Neural networks; Power generation; Power system interconnection; Pressure control; Turbines;

fLanguage

English

Publisher

ieee

Conference_Titel

Southeastcon '96. Bringing Together Education, Science and Technology., Proceedings of the IEEE

Conference_Location

Tampa, FL

Print_ISBN

0-7803-3088-9

Type

conf

DOI

10.1109/SECON.1996.510126

Filename

510126