Neural network based transformer incipient fault detection

被引:0
|
作者
Nagpal, Tapsi [1 ]
Brar, Yadwinder Singh [2 ]
机构
[1] Thapar Univ, Dept Elect & Instrumentat Engn, Patiala 147004, Punjab, India
[2] Guru Nanak Dev Engn Coll, Dept Elect Engn, Ludhiana 141006, Punjab, India
关键词
Artificial intelligence; Fault diagnosis; Power transformer; Neural network; IN-OIL ANALYSIS; DIAGNOSIS; SYSTEM;
D O I
暂无
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
The most common diagnosis method for power transformer faults is the dissolved gas analysis (DGA) of transformer oil. Various methods have been developed to interpret DGA results such as key gas method, and roger's ratio method. The present approach utilizes IEC 60599 ratio method to discriminate fault in transformers, which is having the advantage of usage of three gas ratios instead of four gas ratios used in other ratio methods. In some cases, the DGA results cannot be matched by the existing codes, making the diagnosis unsuccessful in multiple faults. To overcome this, the authors have proposed the use of neural networks to highlight their ability to detect the incipient faults in transformer.
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页数:5
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