Prediction of Dissolved Gas in Power Transformer Oil Based on Random Forests Algorithm

被引：0

作者：

Deng, Ke ^{[1
]}

Xiong, Weihong ^{[2
]}

Zhu, Liming ^{[3
]}

Zhang, Hongzhi ^{[3
]}

Li, Zhengtian ^{[3
]}

机构：

[1] State Grid Hubei Elect Power Co, Overhaul Branch, Wuhan 430050, Peoples R China

[2] Ctr China Grid Co Ltd, Wuhan 430077, Peoples R China

[3] HUST, Wuhan 430074, Peoples R China

来源：

2015 5TH INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES (DRPT 2015) | 2015年

关键词：

Power transformer; dissolved gas; condition based maintenance; prediction; random forests algorithm;

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：

In order to adopt reasonable measures to anticipate and avoid possible internal failures in power transformers, accurate prediction of power transformer oil dissolved gas trends is useful. It is necessary to realize the condition based maintenance of power transformers, and the prediction of dissolved gas in the oil is solid foundation. The gas concentration is affected by many factors, so prediction model built by reasonable selection of the larger correlation factors will help to improve prediction accuracy. Random forests algorithm is used to predict the gas trends and the prediction performance evaluation is realized by appropriate evaluation indexes. By comparison with support vector machine prediction, the advantage of random forests method for power transformer oil dissolved gas prediction is proved.

引用

页码：1531 / 1534

页数：4

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