A Supervised Classification System based on Evolutive Multi-Agent Clustering for Smart Grids Faults Prediction

Due to the increasing amount of sensors and data streams that can be collected in order to monitor electric distribution networks, developing predictive diagnostic systems over Smart Grids demands powerful and scalable algorithms in order to search for regularities in Big Data. In this regards, Evolutive Agent Based Clustering (E-ABC) is a promising framing reference, as it is conceived to orchestrate a swarm of intelligent agents acting as individuals of an evolving population, each performing a random walk on a different subset of patterns. Each agent is in charge of discovering well-formed (compact and populated) clusters and, at the same time, a suitable subset of features corresponding to the subspace where such clusters lie, following a local metric learning approach, where each cluster is characterized by its own subset of relevant features. E-ABC is able to process data belonging to structured and possibly non metric spaces, relying on custom parametric dissimilarity measures. In this paper, a supervised version of E-ABC is proposed. This novel classification system has been employed for recognizing and predicting localized faults on the electric distribution network of Rome, managed by the Italian utility company ACEA. Tests results show that E-ABC is able to synthesize classification models characterized by a remarkable generalization capability, with adequate performances to be employed in Smart Grids condition based management systems. Moreover, the feature subsets where most of the meaningful clusters have been discovered can be used to better understand sub-classes of failures, each identified by a set of related causes.