DocumentCode :
3392991
Title :
Transformer Fault Diagnosis Based on Rough Sets and Support Vector Machine
Author :
Li Zhi-bin ; Xie Zhi-hui
Author_Institution :
Coll. of Power & Autom., Shanghai Univ. of Electr. Power, Shanghai, China
fYear :
2012
fDate :
27-29 March 2012
Firstpage :
1
Lastpage :
4
Abstract :
For the problem of little sample size and incomplete sample information which leads to the fact that fault diagnosis results are not ideal in the transformer fault diagnosis process, we combine the simplifying of rough sets with support vector machine classification .Then, we build the model of transformer fault diagnosis which is based on the rough sets and support vector machine .Proved by the simulation of true sample, this model can diagnosis the transformer fault effectively and has very high accuracy rate.
Keywords :
fault diagnosis; pattern classification; power engineering computing; power transformers; rough set theory; support vector machines; rough set theory; support vector machine classification; transformer fault diagnosis process; Accuracy; Fault diagnosis; Kernel; Power transformers; Support vector machines; Testing; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific
Conference_Location :
Shanghai
ISSN :
2157-4839
Print_ISBN :
978-1-4577-0545-8
Type :
conf
DOI :
10.1109/APPEEC.2012.6307355
Filename :
6307355
Link To Document :
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