DocumentCode
527834
Title
Fault diagnosis of wind turbine power electronic equipment based on SOM neural network
Author
Guo, Hongche ; Wu, Bo
Author_Institution
Sch. of Electr. Eng., Shenyang Univ. of Technol., Shenyang, China
Volume
4
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
1679
Lastpage
1681
Abstract
It has great significance for reducing the operation failure rate and operation maintenance cost of wind turbine through monitoring possible fault power electronic devices and identifying the faults of power electronic devices. The paper applies self-organizing feature map neural network (SOM) to the fault diagnosis of wind turbine power electronic equipment and verifies its engineering application effect. The experimental results show that the method has great effect in fault diagnosis of wind turbine power electronic equipment, and it has some engineering value.
Keywords
fault diagnosis; power engineering computing; power generation faults; self-organising feature maps; wind turbines; fault diagnosis; operation failure rate; operation maintenance cost; power electronic devices; self-organizing feature map neural network; wind turbine power electronic equipment; Artificial neural networks; Circuit faults; Fault diagnosis; Neurons; Power electronics; Training; Wind turbines; SOM neural network; power electronic device; wind turbine;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location
Yantai, Shandong
Print_ISBN
978-1-4244-5958-2
Type
conf
DOI
10.1109/ICNC.2010.5584567
Filename
5584567
Link To Document