Title:Concentration Prediction of Dissolved Gases in Transformer Oil by Relevance Vector Regression Algorithm
VOLUME: 6 ISSUE: 1
Author(s):Sheng-wei Fei and Yong He
Affiliation:School of Mechanical Engineering, Donghua University, Shanghai 201620, China.
Keywords:Concentration prediction, chromatographic analysis transformer, relevance vector regression, time series prediction
Abstract:Concentration prediction of dissolved gases in power transformer oil is very significant to detect incipient failures
of transformer early. Concentration prediction of dissolved gases in power transformer oil is complicated problem
due to its nonlinearity and little training data. Relevance vector regression algorithm (RVR) is applied to concentration
prediction of dissolved gases in transformer oil in this paper. Compared with traditional support vector machine, relevance
vector regression algorithm has the higher prediction accuracy because it has less support vectors than support vector regression
algorithm. The concentration prediction model of dissolved gases in power transformer oil is established based
on regression algorithm of RVR. The experimental results indicate that the RVR method can achieve greater prediction
accuracy than support vector regression algorithm. Consequently, the RVR model is a proper alternative for predicting the
concentration of dissolved gases in power transformer oil. The article presents some promising patents on concentration
prediction of dissolved gases in transformer oil by relevance vector regression algorithm.
