Evolutionary optimization of a one-class classification system for faults recognition in smart grids

The Computational Intelligence paradigm has proven to be a useful approach when facing problems related to Smart Grids (SG). The modern SG systems are equipped with Smart Sensors scattered in the real-world power distribution lines that are able to take a fine-grained picture of the actual power grid state gathering a huge amount of heterogeneous data. Modeling and predicting general faults instances by means of processing structured patterns of faults data coming from Smart Sensors is a very challenging task. This paper deals with the problem of faults modeling and recognition on MV feeders in the real-world Smart Grid system that feeds the city of Rome, Italy. The faults recognition problem is faced by means of a One-Class classifier based on a modified k-means algorithm trained through an evolutive approach. Due to the nature of the specific data-driven problem at hand, a custom weighted dissimilarity measure designed to cope with mixed data type like numerical data, Time Series and