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
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