A learning intelligent system for classification and characterization of localized faults in Smart Grids

The worldwide power grid can be thought as a System of Systems deeply embedded in a time-varying, non-deterministic and stochastic environment. The availability of ubiquitous and pervasive technology about heterogeneous data gathering and information processing in the Smart Grids allows new methodologies to face the challenging task of fault detection and modeling. In this study, a fault recognition system for Medium Voltage feeders operational in the power grid in Rome, Italy, is presented. The recognition task is performed synthesizing a data-driven model of fault phenomenons based on a hybridization of Evolutionary learning and Clustering techniques. The model is synthesized starting from a set of clusters obtained by partitioning the fault patterns, tuning at the same time the core dissimilarity measure. In this paper we show as clusters can be successively analyzed for mining useful information about the fault phenomenon and to build up an ad-hoc decision system to support business strategies such as Condition Based Maintenance tasks.