DocumentCode :
2172053
Title :
Fault diagnosis of power transformer based on association rules gained by rough set
Author :
Zhou Ming ; Wang Taiyong
Author_Institution :
Sch. of Mech. Eng., Tianjin Univ., Tianjin, China
Volume :
3
fYear :
2010
fDate :
26-28 Feb. 2010
Firstpage :
123
Lastpage :
126
Abstract :
Dissolved gas analysis (DGA) is one of the most useful techniques, which are used to detect the incipient faults of power transformer. In the past decade, various fault diagnosis techniques have been proposed that include the conventional ratio method to detect the incipient faults of power transformer. In the paper, rough set is presented to generate association rules which are used to fault diagnosis of power transformer. Rough set can mine the deep relation, association rule of power transformer is gained by rough set. By reduction of rough set, redundant feature attribute which affects the classification performance will be deleted. Then, association rule of power transformer is gained. The experimental results indicate that the method has very good results.
Keywords :
chemical analysis; data mining; fault diagnosis; power transformers; rough set theory; association rules; dissolved gas analysis; fault diagnosis; power transformer; rough set; Association rules; Data mining; Dissolved gas analysis; Fault detection; Fault diagnosis; Gases; Mechanical engineering; Power generation; Power transformers; Temperature; association rules; dissolved gas analysis; incipient faults; power transformer;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-5585-0
Electronic_ISBN :
978-1-4244-5586-7
Type :
conf
DOI :
10.1109/ICCAE.2010.5452070
Filename :
5452070
Link To Document :
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