DocumentCode :
1327696
Title :
Artificial neural networks controlled fast valving in a power generation plant
Author :
Han, Yingduo ; Xiu, Lincheng ; Wang, Zhonghong ; Chen, Qi ; Tan, Shaohua
Author_Institution :
Dept. of Electr. Eng., Tsinghua Univ., Beijing, China
Volume :
8
Issue :
2
fYear :
1997
fDate :
3/1/1997 12:00:00 AM
Firstpage :
373
Lastpage :
389
Abstract :
This paper presents an artificial neural-network-based controller to realize the fast valving in a power generation plant. The backpropagation algorithm is used to train the feedforward neural networks controller. The hardware implementation and the test results of the controller on a physical pilot-scale power plant setup are described in detail. Compared with the conventional fast valving methods applied to the same system, test results both with the computer simulation and on a physical pilot-scale power plant setup demonstrate that the artificial neural network controller has satisfactory generalization capability, reliability, and accuracy to be feasible for this critical control operation
Keywords :
backpropagation; feedforward neural nets; neurocontrollers; nonlinear control systems; power system control; artificial neural-network-based controller; backpropagation algorithm; computer simulation; fast valving; feedforward neural networks controller; generalization capability; hardware implementation; pilot-scale power plant; power generation plant; reliability; Artificial neural networks; Backpropagation algorithms; Computer network reliability; Computer simulation; Feedforward neural networks; Hardware; Neural networks; Power generation; Power system reliability; System testing;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.557689
Filename :
557689
Link To Document :
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