Fault diagnosis model of DGA for power transformer based on FCM and SVM

Support vector machine (SVM) is a novel machine learning based on statistical learning theory, SVM is powerful for the problem with small sample, nonlinear and high dimension. A model of transformer diagnosis based on SVM is present in this paper in which it uses the grid search method based on cross-validation to determine model parameters. Taking into account the compactness characteristics of DGA data, the fuzzy C-means (FCM) clustering method is adopted to pre-select samples achieved. It solves the problem of long time expended on model parameters determined, and enhances a certain promotion of the model extension ability. Practical analysis shows that this model has a good classification results and extension ability.