DocumentCode
553946
Title
Fault predictive diagnosis of wind turbine based on LM arithmetic of Artificial Neural Network theory
Author
Lincang Ju ; Dekuan Song ; Beibei Shi ; Qiang Zhao
Author_Institution
Sch. of Energy & Power Eng., Xi´an Jiaotong Univ., Xi´an, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
575
Lastpage
579
Abstract
This paper analyses the main fault factors on wind turbine, and presents three general faults: gear box fault, leeway system fault and generator fault. After the analysis and research of the basic principle of Back-Propagation Neural Network based on LM arithmetic, a three-layer Back-Propagation Network faults predictive diagnosis model is built. Data from two wind turbines are used to test the effectiveness of this method.
Keywords
backpropagation; curve fitting; fault diagnosis; neural nets; power engineering computing; wind power plants; wind turbines; LM arithmetic; artificial neural network theory; backpropagation neural network; fault predictive diagnosis; gear box fault; generator fault; leeway system fault; wind turbine; Gears; Generators; Shafts; Temperature; Vibrations; Wind speed; Wind turbines; Back-Propagation Neural Network; Fault Prediction; LM Arithmetic; Wind Turbine;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6021921
Filename
6021921
Link To Document