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
fDate :
3/1/1997 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on