Forecasting of Dissolved Gases in Power Transformer Oil Based on DOG - LSSVM Regression and Artificial Bee Colony

被引：0

作者：

Zhang, Yiyi ^{[1
]}

Zhao, Liuliang ^{[1
]}

Fang, Jiake ^{[2
]}

Jiao, Jian ^{[1
]}

Liao, Changyi ^{[1
]}

Li, Xin ^{[1
]}

机构：

[1] Guangxi Key Lab Power Syst Optimizat & Energy Tec, Nanning 530004, Peoples R China

[2] Guangxi Univ, Nanning 530004, Peoples R China

来源：

2018 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON) | 2018年

关键词：

Artificial bee colony algorithm; the least squares support vector machine; the DOG wavelet kernel function; transformer; SUPPORT VECTOR MACHINE; MODEL; 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 accurately forecast the development trend of transformers and the trend in the early stage of faults, this paper provides a method to predict the dissolved gas content in transformer oil with DOG wavelet kernel function and artificial bee colony algorithm (ABC) based on least squares vector machine regression (LSSVR). This paper first optimizes the parameters of LSSVR by ABC, then constructs the LSSVR model with the DOG, finally evaluates the prediction performance based on the measurement of the average absolute error percentage (MAPE) and the square correlation coefficient (r(2)) to prove the accuracy and effectiveness of this method.

引用

页码：3620 / 3625

页数：6

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