Diagnosis of Power Transformer Oil Using PSO-SVM and KNN Classifiers

In this paper, a Support Vector Machine (SVM) and the k nearest neighbor (KNN) classifiers are employed to diagnose the power transformer oil insulation using dissolved gas analysis (DGA). Different vectors are used as inputs for classifiers to achieve a maximum accuracy rate. The five input vectors that are considered: DGA in ppm, DGA in percentage, Dornenberg ratios, Rogers ratios and Duval triangle reports. Concerning the classes of faults, five types are adopted as output of classifiers (PD, D1, D2, T1&T2 and T3). In order to perform a better fault classification, The SVM parameters have been optimized with a Particle Swarm Optimization (PSO). Using Duval triangle reports, the PSO-SVM algorithm provides the highest accuracy rate than the KNN algorithm one.