Neural network approach for fault diagnosis of transformers

被引：0

作者：

Salami, Abolfazl ^{[1
]}

Pahlevani, Parvaneh ^{[1
]}

机构：

[1] Iran Univ Sci & Technol, Arak Engn & Tech Dept, Arak, Iran

来源：

PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS | 2007年

关键词：

back propagation; condition monitoring; dissolved gas analysis; fault diagnostics; neural network; power transformers;

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：

Dissolved gas analysis (DGA) is one of the most useful techniques to detect the incipient faults of power transformer. This paper is a study of artificial neural networks (ANN) applications for the diagnosis of power transformer incipient fault. The fault diagnosis is based on dissolved gas-in-oil analysis (DGA). Using historical transformer failure data, a multi-layer perceptron (MLP) neural network is applied in this work. The proposed network can overcome the drawbacks of conventional methods. The proposed schemes are simulated and tested.

引用

页码：1346 / 1349

页数：4

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