Biography

Giulia Tresca received the B.Sc. and M.Sc. degrees in electrical engineering from the University of L’Aquila and the University of Pavia in 2014 and 2017, respectively. From 2017 to 2019, she worked as a Test Engineer at Infineon, Villach. In 2019, she joined PELAB at the University of Pavia, where she completed her Ph.D. in 2023 and is now an Assistant Professor. She is also an Associate Researcher at the University of Nottingham. Her research interests include grid-connected converters, multilevel and reconfigurable power converters, and AI-based fault detection.

CV

Projects

Innovative methods for impedance estimation using artificial intelligence

Develop an LSTM-based model to estimate grid impedance dynamically. Learned nonlinear grid behavior from operational data without explicit equations. Improves converter control stability and fault resilience.

Multilevel Converters for High Power Applications and Medium Voltage Drives

Multilevel Converters for High Power Applications and Medium Voltage Drives

Modeling, Control and Stability of Power Electronics Based Power Systems

Each power converter in modern power grids has local intelligence, control and filters: the complex interactions between them require advanced stability assessment methods and global control design methods.

Fault Detection and Location in Power Converters with a High Number of Switches using Deep Learning

This research topic explores novel approaches in fault diagnostic techniques using DL for power converters with a large number of switches, like multilevel converters, to develop efficient and effective approaches for improved overall system reliability.

Double-Stage 5 kW Charger for Vanadium Redox Flow Batteries

Study, design, and development of a power converter optimized for grid integration of Vanadium Redox flow batteries

Advanced Model Predictive Control for Electrical Drives and Power Electronics Converters

Advanced Model Predictive Control for Electrical Drives and Power Electronics Converters

Isolated DC/DC LLC resonant GaN converter for motor sport applications

The study investigates an ISOP LLC resonant converter using GaN transistors for high-efficiency, high–power-density dc-dc conversion. It also analyzes module mismatches and employs a genetic algorithm to optimize losses, transformer volume, and efficiency

Grid Active Node for DC Electrical Systems (GRAND)

Development of innovative multi-port power conversion systems for DC microgrids that enable seamless integration of Distributed Energy Resources and Storage into the Internet of Energy: focus on advanced control, optimized design, and remote operability.

Intelligent, Modular and Adaptive Power Conversion Technology for Battery Energy Storage Systems

Developing of Intelligent Battery Modules (IBMs) to replace traditional battery packs and converters, forming a DC/AC multilevel converter to optimize energy delivery and system integration across various battery chemistries.

Digital Twin of Power Electronics Converters using Artificial Neural Networks

Build a virtual–physical loop for CHB and DAB converters to enable predictive maintenance. Integrated sensor data, neural models, and feedback control in real time. Supports AI-driven reliability enhancement and fault prevention

Solid State Transformers for next generation AI server stations

Development of a multi-port Solid-State Transformer (SST) system working as a key power interface between the medium-voltage (MV) grid and low voltage (LV) critical server infrastructure, with multiple DC output voltage levels.

Design control and implementation of an LLC resonant converter using GaN technology

Design and implementation of a ISOP LLC resonant converter to test, at a real application system level, the use of GaN devices versus traditional technology and to investigate novel solutions for drivers, overcurrent protections and control

Contacts