DocumentCode
2500413
Title
The study of variant DGA feature neural network multilayer diagnostic model
Author
Hu, Qing ; Chen, Weigen ; Du, Lin ; Li, Nan ; Sun, Caixin
Author_Institution
Key Lab. of High Voltage Eng. & Electr. New Technol. of MOE, Chongqing Univ., Chongqing
fYear
2008
fDate
25-27 June 2008
Firstpage
8526
Lastpage
8530
Abstract
Selecting appropriate features has vital effect on the effectiveness of fault diagnosis, and DGA is widely used in transformer fault diagnosis, so this paper, using ANN as method and 5 gas concentrations as available features, studies the the key feature gases according to fault types, and their roles in fault diagnosis. Based on this, this paper provides the variant feature neural network multilayer diagnosis model.
Keywords
chemical analysis; fault diagnosis; neural nets; power engineering computing; power transformers; dissolved gas analysis; gas concentration; transformer fault diagnosis; variant DGA feature neural network multilayer diagnostic model; Appropriate technology; Artificial neural networks; Automation; Dissolved gas analysis; Fault diagnosis; Intelligent control; Multi-layer neural network; Neural networks; Power transformers; Sun; DGA; Fault Diagnosis; Neural Network; Transformer;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location
Chongqing
Print_ISBN
978-1-4244-2113-8
Electronic_ISBN
978-1-4244-2114-5
Type
conf
DOI
10.1109/WCICA.2008.4594268
Filename
4594268
Link To Document