Biography

Over 7 years of industrial engineering field, ranging from telecommunications, transportation, industrial control automation, and electrification engineering, his research areas are in AI application for fault diagnostics and condition monitoring in power electronics, Digital Twin Modelling for power converters, grid impedance parameter estimation using AI techniques, and parameter estimation using AI for advanced control or diagnostics. He is an active reviewer for the Journal of Emerging and Selected Topics in Power Electronics.

CV

Projects

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.

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

Contacts