Interval prediction of dissolved-gas concentration in transformer oil

被引：0
|
作者：
Lin X. [1 ,2 ]
Huang J. [1 ]
Xiong W. [3 ]
Weng H. [1 ]
Zhu L. [2 ]
Zhang Z. [4 ]
Xie Z. [2 ]
机构：
[1] College of Electrical & New Energy, China Three Gorges University, Yichang
[2] School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan
[3] Central China Grid Company Limited of State Grid Corporation of China, Wuhan
[4] Huaneng Beijing Thermal Power Co., Ltd., Beijing
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2016年 / 36卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Combination prediction; Dissolved gas; Electric transformers; Entropy; Models; Normal distribution; Prediction; Prediction interval; Relevance;
D O I：
10.16081/j.issn.1006-6047.2016.04.012
中图分类号：
学科分类号：
摘要：
The factors highly related to the variables to be predicted are confirmed by the grey relational analysis and a combination prediction model with objective weight is built based on the entropy theory. Since uncertain factors may influence the dissolved-gas concentration in transformer oil and the prediction interval can effectively quantify its fluctuation, the proportionality coefficient method and particle swarm algorithm are adopted to build an interval prediction model of dissolved gas in transformer oil at a certain confidence level, which, different from the traditional interval prediction method, does not have to obey the normal distribution limitation. The calculative results for an example show the effectiveness of the proposed model. © 2016, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码：73 / 77
页数：4
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