Ricerc@Sapienza

In this paper we propose a novel evolutive agent-based clustering algorithm where agents act as individuals of an evolving population, each one performing a random walk on a different subset of patterns drawn from the entire dataset. Such agents are orchestrated by means of a customised genetic algorithm and are able to perform simultaneously clustering and feature selection.

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.

An effective management of Electrochemical Energy Storage Systems (ESSs) is nowadays of utmost importance for the technological evolution in both automotive and sustainable power networks applications. In particular, Battery Managements Systems (BMSs) are the electronic devices devolved to this management. One of the most important task of any BMS is cells balancing, aiming at leveling the operating points of the cells composing the ESS. Therefore, a novel online balancing algorithm is proposed in this work.