Abstract

The electrical grid is dealing with several new factors and agents, such as intermittent renewable energy and electrical vehicle, which compromise the good performance of the grid. In order to ensure a suitable electrical supply despite of these new factors, the need of an smart grid configuration evident. Moreover, the integration of new agents on the grid sometimes leads to the occurrence of electrical faults. The detection and classification of these electrical faults is of vital importance to guarantee the stability and good quality of the electrical supply. Nowadays data-driven methods based on machine learning techniques for detection and classification of electrical faults are gaining interest from the scientific community thanks to the good results they offer. In this paper, a solution for the detection and classification of electrical faults is presented based of machine learning algorithms and mainly driven in five main steps. The good results prove the good performance of the proposed solution.