NDLI: A GA-based grey prediction model for predicting the gas-in-oil concentrations in oil-filled transformer

Content Provider	IEEE Xplore Digital Library
Author	Youyuan Wang Ruijin Liao Caixin Sun Lin Du Jianlin Hu
Copyright Year	2004
Description	Author affiliation: Key Lab. of High Voltage Eng. & Electr. New Technol., Chongqing Univ., China (Youyuan Wang; Ruijin Liao; Caixin Sun; Lin Du; Jianlin Hu)
Abstract	Dissolved-gas-analysis (DGA) techniques are widely used to diagnose oil-filled transformer insulation, but the conventional procedure acquiring the gas-in-oil concentrations is not timely. To make up the disadvantage, a new method based on a genetic algorithm and the grey theory to predict the gas-in-oil concentrations is proposed in This work. The grey model (GM(1,1)) has been improved and a new optimized grey model (GM(1,1,/spl beta/)) has been constructed. The genetic algorithm has been applied to search the optimal parameters of the GM(1,1,/spl beta/) model. The validity of the GA-based GM(1,1,/spl beta/) model was verified with two prediction examples.
Starting Page	74
Ending Page	77
File Size	273716
Page Count	4
File Format	PDF
ISBN	0780384474
ISSN	1089084X
DOI	10.1109/ELINSL.2004.1380456
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Oil insulation Power transformer insulation Power transformers Power system modeling Genetic algorithms Dissolved gas analysis Differential equations Gas insulation Power system stability
Content Type	Text
Resource Type	Article

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