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Support vector machine (SVM) classifier has been successfully applied to power transformer fault diagnosis. However, there is no theoretical basis or effective method to select appropriate SVM classifier parameters which have a crucial influence on the classification accuracy. Currently, the main method is cut and try based on experience. In this study, genetic algorithm (GA) is employed to optimize...
LS-SVM (least square support vector machines) is applied to solve the practical problems of small samples and non-linear prediction better, and it is suitable for the DGA in power transformers. But in this model, the selecting of the parameters, and , impact on the result of the diagnosis greatly, so it is necessary to optimize these parameters. The IGA (improved genetic algorithm) is applied in this...
The least square support vector machines (LS-SVM) is applied to solve the practical problems of less samples and non-linear prediction better, and it is suitable for the forecasting dissolved gas in transformer oil. But in this model, the selecting values of the parameters impacts on the results of the diagnosis greatly, so it is necessary to optimize these parameters. A new method to optimize these...
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