Neural Network for Estimating the Technical Age of Power Transformers

被引：0
|
作者：
Schnittker, Henning [1 ]
Werle, Peter [1 ]
Muenster, Tobias [2 ]
Lottner, Matthias [2 ]
机构：
[1] Leibniz Univ Hannover, Inst Elect Power Syst, Div High Voltage & Asset Management, Schering Inst, Hannover, Germany
[2] TenneT TSO GmbH, Bayreuth, Germany
来源：
2022 9TH INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS (CMD) | 2022年
关键词：
Asset management; power transformer; health index; artificial neural network; standard oil test; dissolved gas analysis;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
An artificial neural network is created to estimate the technical age of power transformers based on the measurement results of standard oil tests and dissolved gas analysis. The prediction accuracy for +/- 5 years is 75 percent indicates the overlapping between the technical and calendrical age of the power transformers.
引用
收藏
页码：289 / 292
页数：4
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