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.
Go to projectEnhanced STATCOM wiith Supercap: Design, Control and Real Time Simulator for Hardware In the Loop Test
Enhanced STATCOM wiith Supercap: Design, Control and Real Time Simulator for Hardware In the Loop Test
Go to projectDigital 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
Go to projectIntelligent, 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.
Go to projectGrid 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.
Go to projectIsolated 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
Go to projectAdvanced Model Predictive Control for Electrical Drives and Power Electronics Converters
Advanced Model Predictive Control for Electrical Drives and Power Electronics Converters
Go to projectFault 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.
Go to projectSiC MOSFET-Based Power Supply for high-current applications
Proper design of electrical converters is essential for applications requiring precise current control, especially in high-current (tens of kiloamperes) systems used for generating strong magnetic fields in plasma confinement or particle acceleration.
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