Trung-Son Nguyen

I am a lecturer and researcher at the School of Electrical and Electronic Engineering, Hanoi University of Science and Technology (HUST), working in the Smart Power Systems Research Group. My work sits on the boundary between numerical field computation and power system engineering.

I earned my MSc (2009) and PhD (2012) at Grenoble Institute of Technology, in the G2Elab laboratory, and stayed on there for a further year of postdoctoral research. My thesis developed model order reduction for inductive PEEC circuits — making the electromagnetic modelling of large power-electronic interconnections cheap enough to be usable inside a design loop. That line of work produced adaptive multipoint reduction schemes, independent-loop formulations, and equivalent-circuit synthesis methods for reduced models, alongside fast multipole methods applied to ship magnetic anomaly computation and 3-D eddy current problems.

The instinct behind all of it — replace an expensive numerical model with a cheap, faithful surrogate — is what drives my current work. I am extending it into power systems through physics-informed neural networks (PINN) coupled with EMTP-ATP simulation: transformer parameter estimation, breakdown-voltage prediction for insulation systems, and surrogate models that stand in for full transient studies.

Closer to the grid itself, I work on electromechanical and electromagnetic transients: small-signal models for resonance analysis of DFIG wind farms connected to the network, and unbalanced power flow under a high penetration of distributed generation.

In parallel I work on the protection of high-voltage networks at 220 kV and 500 kV — relay coordination, SF₆/GIS equipment, and the pressure that inverter-based resources and HVDC links put on conventional protection philosophy, following IEC and IEEE practice.

I also design and teach Technical Writing and Presentation, a 15-session course that helps electrical engineers structure, write, and defend their technical work for an international audience.

Open to collaboration on physics-informed modelling for power systems, and to supervising students working at the intersection of numerical methods and machine learning.