Machine Learning Models for Bulk Electric System Well-Being Assessment

In this paper we compare two machine learning algorithms (Support Vector Machine (SVM) and Hamming Clustering (HC)) to perform a reliability assessment of an electric power system. Bulk electric system well-being analysis, which corresponds to the classification of the possible state of an electric power system as Healthy, Marginal or At Risk is properly emulated by training multi-class SVM and HC models, with a small amount of information. The experiments show that although both models produce reasonable predictions, HC accuracy is greater than the SVM one.