Research Area

  • Title:
    Computational Intelligence for Electrical Machines and Smart Grids

  • Keywords:

    Smart grid, Electrical machines, Renewable energy sources, Electric transportation, Deep learning

  • Description:

    The ability to handle huge amount of data coming from the sensing activity in smart grid contexts is crucial for reliable user services, requiring proper Digital Signal Processing and Computational Intelligence algorithms. These algorithms efficiently extract necessary information from various abstraction levels of acquired energy data, allowing intelligent decision-making and action implementation onto the grid. Ongoing research topics in this area include:

    • Energy management: optimization of grid operations in settings with distributed energy sources such as microgrids, using advanced computational techniques
    • Non-Intrusive Load Monitoring is a technique to automatically identify the consumption patterns of different loads by using only aggregate electric measurements
    • Electric vehicle integration investigates bidirectional communication and energy exchange between EVs and the power grid to improve operation and prevent overloading, instability, and voltage drop
    • Power quality enhancement addresses concerns about power quality due to the increased use of renewables

    In the Electrical Machines and Drives field, machine learning and deep learning techniques enhance performance, efficiency, and reliability by detecting faults and malfunctions. Topics under investigation include automated temperature monitoring to prevent malfunctions, fault detection, prediction, and diagnosis of electrical machines, and design optimization using artificial intelligence techniques.

  • Laboratory:

    iLabs-SmartEnvironments laboratory, A3lab laboratory

  • Contact Person:

    Emanuele Principi

  • Collaborations:

    Universities and research institutions: University of Strathclyde, Glasgow (UK), University of Rhode Island, Kingston (USA), University of Lincoln, Lincoln, (UK), Clemson University, Clemson, (USA), Honda Research Institute, Offenbach/Main (Germany), Griffith University, Queensland (Australia), University of Salerno (Italy) for the National PhD in “Photovoltaics” companies: Loccioni, Angeli di Rosora (Italy), MAC Srl, Recanati (Italy), Dowsee Srl, Fabriano (Italy), Honda Research Institute, Offenbach/Main (Germany).

  • Projects:
    • MOST (Centro Nazionale di Ricerca per la mobilità sostenibile)
    • Nilm clOud seRvices for ResIdential userS (NORRIS)
    • ENERGIS++
    • ComESto – Community Energy Storage: Gestione Aggregata di Sistemi d’Accumulo dell’Energia in Power Cloud
    • Piattaforma Regionale MIRACLE – Marche Innovation and Research fAcilities for Connected and sustainable Living Environments