Fault Diagnosis of Power Transformer Based on Association Rules Gained by Rough Set

被引:7
|
作者
Zhou Ming [1 ]
Wang Taiyong [1 ]
机构
[1] Tianjin Univ, Sch Mech Engn, Tianjin 300072, Peoples R China
基金
中国国家自然科学基金; 高等学校博士学科点专项科研基金;
关键词
dissolved gas analysis; association rules; power transformer; incipient faults;
D O I
10.1109/ICCAE.2010.5452070
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:123 / 126
页数:4
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