Professor Maurizio Cirrincione
My research focuses on AI-enabled modelling, estimation and control for power and energy systems, spanning microgrids, power electronics and electrical drives. I develop machine-learning and signal-processing methods for condition monitoring, fault diagnosis and remaining useful life estimation of machines, converters and distributed energy resources. I also work on advanced control of grid-interfaced converters and multi-source systems (grid-connected and islanded), including weak-grid operation, renewable integration, and data-driven energy management for PV–battery–hydrogen systems and fuel-cell/electrolyser technologies. My work has been recognised through multiple international best-paper awards and the Caianello Prize for the best Italian PhD thesis on neural networks, and I have been listed in the Stanford/Elsevier World’s Top 2% Scientists (2020–2025). I have led and contributed to international projects across Australia, Europe and the Pacific, publishing widely in IEEE venues. I offer HDR projects with hands-on experimental and digital-twin work through CDU’s Energy and Resources Institute facilities (including microgrid and hardware-in-the-loop platforms), supported by strong industry and international collaborations.
Research Interests:
- AI for power/energy systems (machine learning, deep learning)
- Microgrids (grid-connected & islanded), weak grids, grid-forming converters
- Renewable integration (PV/wind), battery systems
- PV–battery–hydrogen systems
- Energy management and optimisation (including AI/DRL-based control)
- Power electronics and converter control
- Electrical drives and sensorless control
- Condition monitoring, fault diagnosis, remaining useful life (RUL) estimation
- Digital twins and hardware-in-the-loop for energy systems