NDLI: Transformer fault prediction based on support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Yan Zhang Bide Zhang Yuchun Yuan Zichun Pei
Copyright Year	2010
Description	Author affiliation: Institute of Electrical and Information, Xihua University, Chengdu , China (610039) (Yan Zhang; Bide Zhang; Yuchun Yuan; Zichun Pei)
Abstract	Forecasting of dissolved gases content in power transformer oil is very significant to detect incipient failures of transformer early and ensure normal operation of entire power system. Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, SVM has rarely been applied to forecast dissolved gases content in power transformer oil. In this study, support vector machine is proposed to forecast dissolved gases content in power transformer oil, among which cross-validation used to determine free parameters of support vector machine. The experimental data from the electric power company in Chengdu are used to illustrate the performance of proposed SVM model. The experimental results indicate that the proposed SVM model can achieve greater forecasting accuracy than grey model (GM) under the circumstances of small sample. Consequently, the SVM model is a proper alternative for forecasting dissolved gases content in power transformer oil.
File Size	258437
File Format	PDF
ISBN	9781424463473
e-ISBN	9781424463497
DOI	10.1109/ICCET.2010.5485828
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear regression Predictive models Oil insulation Dissolved gas analysis cross-validation Power transformers Petroleum Support vector machines Gases support vector machine Training data regression algorithm free parameters Risk management fault prediction
Content Type	Text
Resource Type	Article

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