The study of fault diagnosis model of DGA for oil-immersed transformer based on fuzzy means Kernel clustering and SVM multi-class object simplified structure

A model based on Fuzzy Kernel C-Means Clustering (FKCM) and Support Vector Machine (SVM) multi-class object simplified structure is proposed for oil-immersed transformer fault diagnosis. The basic idea is, firstly, the training samples are clustered by Fuzzy Kernel C-Means Clustering algorithm so as to cancel the isolated data that have no compactness characteristics, then the right ones clustered by Fuzzy Kernel C-Means Clustering are put into the classifier of SVM multi-class object simplified structure and trained by this structure. Finally, the fault of the transformer can be detected through the SVM structure. The result shows that the precision is better than the traditional one, and the reliability and effectiveness using above method is satisfied in fault diagnosis.