Support vector machine and tree models for oil and Kraft degradation in power transformers

Abstract

The power transformer analysis focused internal fault identification is important for the energy efficiency in all the countries, the partial discharge is one of the main failure modes, it affects the remaining life until the failure. In this research article, the new approach by using soft techniques as support vector machine (SVM) and tree models (TM) allow to detect partial discharge with high accuracy (97.55%); dissolved gas analysis (DGA) for the quality of the oil-Kraft, degradation and evaluate the remaining life. An important achieve is the highest accuracy for the thermal influence in oil degradation (100%). Finally, this research article has provided an algorithm for the highest accuracy obtained from the DGA for oil and Kraft degradation with a crosscheck process by using the cluster analysis.