On-line diagnosis of incipient faults and cellulose degradation based on artificial intelligence methods

被引：0
|
作者：
Izzularab, MA [1 ]
Aly, GEM [1 ]
Mansour, DA [1 ]
机构：
[1] Menoufia Univ, Fac Engn, Dept Elect Engn, Shibin Al Kawm, Egypt
来源：
PROCEEDINGS OF THE 2004 IEEE INTERNATIONAL CONFERENCE ON SOLID DIELECTRICS, VOLS 1 AND 2 | 2004年
关键词：
transformer fault diagnosis; cellulose degradation; dissolved gas analysis; artificial neural network; fuzzy logic;
D O I：
暂无
中图分类号：
O469 [凝聚态物理学];
学科分类号：
070205 ;
摘要：
In this paper, a new artificial intelligence technique is proposed to detect incipient faults and cellulose degradation in power transformers using dissolved gas analysis. The proposed technique is based on a combination between neural networks and fuzzy logic theory. Incipient faults diagnosis is based on hydrocarbon gases as an input while cellulose degradation detection is based on carbon monoxide and carbon dioxide. The capabilities of the proposed diagnostic system have been verified through practical test data collected from the Egyptian Electricity network. A comparison between the proposed technique and reported methods are carried out.
引用
收藏
页码：767 / 770
页数：4
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