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

页码：73 / 77

页数：4

共 20 条

[11] Liu W.H., Forecasting the semiconductor industry cycles by boot-strap prediction intervals, Appl Econ, 39, 13, pp. 1731-1742, (2007)
[12] Luo Y., Li Y., Comprehensive decision-making of transmission network planning based on entropy weight and grey relational analysis, Power System Technology, 37, 1, pp. 77-81, (2013)
[13] Qi M., Fu Z., Jing Y., Et al., A comprehensive evaluation method of power plant units based on information entropy and principal component analysis, Proceedings of the CSEE, 33, 2, pp. 58-64, (2013)
[14] Wang J., Decision method of enterprise strategy based on information entropy and grey situation set, Statistics & Decision, 4, pp. 173-175, (2014)
[15] Dybowski R., Gant V., Clinical Applications of Artificial Neural Networks, pp. 298-326, (2001)
[16] Khosravi A., Nahavandi S., Creighton D., Et al., A lower-upper bound estimation method for construction of neural network based prediction intervals, IEEE Trans on Neural Network, 22, 3, pp. 337-346, (2011)
[17] Khosravi A., Nahavandi S., Creighton D., Et al., Interval type-2 fuzzy logic systems for load forecasting: A comparative study, IEEE Trans on Power Systems, 27, 3, pp. 1274-1282, (2012)
[18] Hu W., Li Z., A simpler and more effective particle swarm optimization algorithm, Journal of Software, 18, 4, pp. 861-868, (2007)
[19] Liang S., Study on antioxidation transformer insulation oil and its effects on oil-immersed paper thermal aging, (2009)
[20] Gu X., Zhao B., Liu W., Load restoration based on multi-objective optimization and grey incidence decision-making, Electric Power Automation Equipment, 35, 9, pp. 6-13, (2015)

← 1 2 →