A hybrid tool for detection of incipient faults in transformers based on the dissolved gas analysis of insulating oil

被引：113
|
作者：
Morais, DR [1 ]
Rolim, JG [1 ]
机构：
[1] Univ Fed Santa Catarina, Dept Elect Engn, Power Syst Grp, BR-88040900 Florianopolis, SC, Brazil
来源：
IEEE TRANSACTIONS ON POWER DELIVERY | 2006年 / 21卷 / 02期
关键词：
dissolved gas analysis (DGA); fault diagnosis; fuzzy logic; neural networks; standards; transformers;
D O I：
10.1109/TPWRD.2005.864044
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper describes the development and implementation of a tool for the diagnosis of faults in power transformers through the analysis of dissolved gases in oil. The computational system approach is based on the combined use of some traditional criteria of the dissolved gas analysis published in standards, an artificial neural network, and a fuzzy logic system. The objective of the tool is to provide the user with an answer obtained from analysis not only of the traditional methods already consolidated in the technical literature, but also via artificial-intelligence techniques, reaching a higher degree of reliability with respect to each technique individually. The results obtained with this too; are promising in the diagnosis of incipient faults in transformers, reaching success levels of more than 80%.
引用
收藏
页码：673 / 680
页数：8
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