DocumentCode
3160540
Title
Notice of Retraction
Hydro-generator units operating condition forecasting and fault diagnosis based on GANN
Author
Ge Xinfeng ; Pan Luoping ; Gao Zhongxin ; Tang Shu ; Chu Dongdong
Author_Institution
China Inst. of Water Resources & Hydropower Res., Beijing, China
fYear
2011
fDate
16-18 April 2011
Firstpage
5007
Lastpage
5009
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, from the Angle to predict, take hydro-generating operation condition parameters (head, power) as input sample, take unit head cover vibration as output sample, create BP and GANN neural network prediction model. Train the established models, through comparing the two models. GANN model Has better precision.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, from the Angle to predict, take hydro-generating operation condition parameters (head, power) as input sample, take unit head cover vibration as output sample, create BP and GANN neural network prediction model. Train the established models, through comparing the two models. GANN model Has better precision.
Keywords
backpropagation; condition monitoring; fault diagnosis; hydroelectric generators; neural nets; power engineering computing; BP neural network prediction model; GANN neural network prediction model; condition forecasting; fault diagnosis; hydrogenerating operation condition parameters; hydrogenerator units; Artificial neural networks; Fault diagnosis; Forecasting; Genetic algorithms; Mathematical model; Presses; Vibrations; BP neural network; GANN; condition forecasting; fault diagnosis; hydro-generating units;
fLanguage
English
Publisher
ieee
Conference_Titel
Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on
Conference_Location
XianNing
Print_ISBN
978-1-61284-458-9
Type
conf
DOI
10.1109/CECNET.2011.5768865
Filename
5768865
Link To Document