SVM-based decision for power transformers fault diagnosis using Rogers and Doernenburg ratios DGA

Abstract

Dissolved gas analysis (DGA) is a widely-used method to detect the power transformer faults, because of its high sensitivity to small amount of electrical faults. The DGA is exploited for fault classification tools implementation using the artificial intelligence techniques. In this study, we use the Rogers ratios and the Doernenburg ratios DGA methods as gas signature. The Support vector machine (SVM) is powerful for the problem with small sampling (small amounts of training data), nonlinear and high dimension (large amounts of input data). The paper presents a comparative study on one hand for the choice the most appropriate DGA method between the Rogers and Doernenburg ratios methods. On the other hand, it compares the various SVM architectures by comparing the kernel functions types with the aim to establish the most appropriate SVM model. Before testing, the proposed structures are trained and tested by the experimental data from Tunisian Company of Electricity and Gas (STEG). The test results suggest that SVM Rogers model can generalize better than SVM Doernenburg model. The approach has the advantages of high accuracy. The other advantage is that the model is practically applicable and may be utilized for an automated power transformer diagnosis. The classification accuracies of the SVM classifier are compared with fuzzy logic (FL), radial basis function (RBF), K-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification.