Transformer fault prediction based on support vector machine

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.