Application of Neural Networks in the Classification of Incipient Faults in Power Transformers: A Study of Case

被引：0

作者：

Castanheira, Luciana G. ^{[1
]}

de Vasconcelos, Joao Antonio ^{[1
]}

Rocha Reis, Agnaldo J. ^{[1
]}

Magalhaes, Paulo H. V. ^{[1
]}

Lopes da Silva, Savio A. ^{[1
]}

机构：

[1] Univ Fed Ouro Preto, Sch Mines, Dept Control Engn & Automat, Campus Morro Cruzeiro, BR-35400000 Ouro Preto, MG, Brazil

来源：

2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2011年

关键词：

IN-OIL ANALYSIS; DIAGNOSIS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The power transformer is one of the most important equipment in an electric power system. If this equipment is out of order in an unplanned way, the damage for both society and electric utilities are very significant. In this work, multi-layer perceptrons have been trained via Rprop algorithm to classify incipient faults in power transformers. The proposed procedure has been applied to real databases derived from chromatographic tests of power transformers. The results obtained here show that the proposed technique generates concordance rates between 75 and 90% most of the time. Neural classifiers can be seen as a key component in power transformer predictive maintenance.

引用

页码：3099 / 3104

页数：6

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