Application of LS-SVM by GA for Dissolved Gas Concentration Forecasting in Power Transformer Oil
LS-SVM (least square support vector machines) is widely used in the regression analysis, but the predition accuracy greatly depends on the parameters selection, in this paper, genetic algorithm is applied to optimize the LS-SVM parameters, correspondingly, the prediction accuracy is improved. First, this paper introduced the principle of LS-SVM and genetic algorithm, and gave the optimization parameter flow chart with genetic algorithm. Then this algorithm is used to forecast dissolved gas concentration in power transformer oil. Through comparing the forecasting result with the other results, which are forecasted by traditional SVM and LS-SVM, it proved that the method had the higher forecasting precision. Field application showed that the method is effectiveness.