Diagnosing Faults in Power Transformers With Autoassociative Neural Networks and Mean Shift

被引：79

作者：

Miranda, Vladimiro ^{[1
]}

Garcez Castro, Adriana R. ^{[2
]}

Lima, Shigeaki ^{[3
]}

机构：

[1] Univ Porto, Fac Engn, P-4200465 Oporto, Portugal

[2] UFPA Fed Univ Para, BR-66075110 Belem, Para, Brazil

[3] UFMA Fed Univ Maranhao, BR-65085580 Sao Luis, Brazil

来源：

IEEE TRANSACTIONS ON POWER DELIVERY | 2012年 / 27卷 / 03期

关键词：

Autoassociative neural networks; dissolved gas analysis (DGA); information theoretic learning; mean shift; transformer fault diagnosis; DISSOLVED-GAS ANALYSIS; SYSTEM;

D O I：

10.1109/TPWRD.2012.2188143

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 presents a new approach to incipient fault diagnosis in power transformers, based on the results of dissolved gas analysis. A set of autoassociative neural networks or autoencoders is trained, so that each becomes tuned with a particular fault mode or no fault condition. The scarce data available forms clusters that are densified using an Information Theoretic Mean Shift algorithm, allowing all real data to be used in the validation process. Then, a parallel model is built where the autoencoders compete with one another when a new input vector is entered and the closest recognition is taken as the diagnosis sought. A remarkable accuracy of 100% is achieved with this architecture, in a validation data set using all real information available.

引用

页码：1350 / 1357

页数：8

共 38 条

[1] Power transformer fault diagnosis based on dissolved gas analysis by support vector machine [J].