Support vector machine with genetic algorithm for forecasting of key-gas ratios in oil-immersed transformer

Authors:
Sheng-wei Fei;Cheng-liang Liu;Yu-bin Miao
Affiliations:
School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China;School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China;School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Failures of power transformer are related with key-gas ratios C"2H"2/C"2H"4, CH"4/H"2 and C"2H"4/C"2H"6 strongly. Forecasting of these ratios of key-gas in power transformer oil is very significant to detect and identify incipient failures of transformer early. Forecasting of the ratios of key-gas in power transformer oil is a complicated problem due to its non-linearity and the small quantity of training data. In this study, support vector machine with genetic algorithm (SVMG) is proposed to forecast the ratios of key-gas in power transformer oil, among which genetic algorithm (GA) is used to determine free parameters of support vector machine. The experimental results indicate that the SVMG method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. Consequently, the SVMG model is a proper alternative for forecasting of the ratios of key-gas in power transformer oil.