A learning intelligent system for fault detection in Smart Grid by a One-Class Classification approach

The analysis and recognition of fault status in the Smart Grid field is a challenging problem. Computational Intelligence techniques have already been shown to be a successful framework to face complex problems related to a Smart Grid. The availability of huge amounts of data coming from smart sensors allows the system to take a fine grained picture of the power grid status. This data can be processed in order to offer an instrument in aiding humans operators to better understand the power grid status and to take decisions on grid operations. This paper addresses the problem of fault recognitions in a real-world power grid (i. e. the power grid that feds the city of Rome, Italy) with the One-Class Classification paradigm by a combined approach of dissimilarity measure learning by means of an evolution strategy and clustering techniques for modeling the decision regions between fault status and the standard functioning of the power system. In this paper we present an in-depth study of the performance of two clustering algorithms in building up the model of faults, as the core procedure of the proposed recognition system.