Fault diagnosis model of DGA for power transformer based on FCM and SVM
2008
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.
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