Hydro-generator units operating condition forecasting and fault diagnosis based on BP neural network

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

27-29 June 2011

Firstpage

1315

Lastpage

1317

Abstract

In this paper, from the Angle to predict , take hydro generating operation condition parameters (head, power) as input sample, take vibration, shaft waggling and pulse pressure, bearings temperature and so on parameter as output sample, create neural network prediction model. Train the established models, through comparing a different designs scheme, chose one smaller error model. Predict through the trained neural network modes ,and compare with the measurement values.

Keywords

backpropagation; fault diagnosis; hydroelectric generators; neural nets; power engineering computing; BP neural network; bearings temperature; fault diagnosis; hydrogenerator units operating condition forecasting; neural network prediction model; pulse pressure; shaft waggling; vibration; Artificial neural networks; Fault diagnosis; Forecasting; Mathematical model; Presses; Temperature measurement; Vibrations; condition forecasting; fault diagnosis; hydro-generating units; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Service System (CSSS), 2011 International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4244-9762-1

Type

conf

DOI

10.1109/CSSS.2011.5972027

Filename

5972027