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
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;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6021921
