DocumentCode
295872
Title
Fast valving control using radial-basis function neural network
Author
Chen, Qi ; Tan, Shaohua ; Han, Yingduo ; Wang, Zonghong
Author_Institution
Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore
Volume
5
fYear
1995
fDate
Nov/Dec 1995
Firstpage
2247
Abstract
Fast valving has long been seen as an effective and economic method to perform transient control in a power generation plant. Due to the inherent nonlinearities that exist in this operation, the fast valving controller designed in the conventional way cannot deliver a satisfactory control. This paper introduces a new approach to control fast valving by using a RBF(radial-basis function) neural network. A controller construction scheme is proposed, in which a stable learning algorithm is embedded. Then the implementation issue is discussed. From the outcome of on-line tests, it is seen that the controller constructed is effective and robust in many different fault situations
Keywords
control system synthesis; feedforward neural nets; neurocontrollers; power system control; power system transients; robust control; controller construction scheme; fast valving control; fault situations; implementation; nonlinearities; power generation plant; radial-basis function neural network; stable learning algorithm; transient control; Control systems; Neural networks; Power engineering and energy; Power generation; Power generation economics; Power system economics; Power system stability; Power system transients; Testing; Valves;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-2768-3
Type
conf
DOI
10.1109/ICNN.1995.487711
Filename
487711
Link To Document