Albert Poulose, Ph.D.

Albert Poulose, Ph.D.

Power-System Stability · Scientific AI · Grid Digital Twins

Postdoctoral Researcher | Kyungpook National University, Daegu, South Korea
Microgrid Research Center & Dept. of Electrical Engineering

I work on dynamic security of converter-dominated power systems: whether the grid stays stable after a fault, and whether margins such as critical clearing time (CCT) remain adequate.

I pursue this through stability theory for grid-forming (GFM) and grid-following (GFL) converters, physics-informed surrogates for transient stability assessment (TSA) and CCT screening, and grid digital twins.

Research interests

  • Dynamic security and transient stability of converter-dominated grids
  • Stability theory for GFM and GFL converters
  • Physics-informed AI for TSA and CCT screening
  • Grid digital twins for online security assessment
Albert Poulose, Ph.D.
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Featured research

Research programme →

IEEE Access, 2026

Physics-Informed AI for Transient Stability

A unified PINN trajectory surrogate for transient stability assessment and critical clearing time estimation.

Paper Code Project GitHub
One trajectory model, two assessment tasksTime-domainsimulationANDES / EMT referenceAccurate, expensive to repeatPhysics-informedneural networkPredicts rotor-angle δ(t)Respects swing physicsTSA screenNominal clearingCCT searchBinary search on t_clSame trained checkpoint. Better trajectories help screening; they do not automatically give protection-grade CCT.
Scientific AI here is a surrogate for post-fault trajectories, not a classifier that only says stable or unstable. Time-domain simulation remains the teacher. The physics-informed neural network (PINN) is queried many times for planning-oriented TSA and CCT studies when repeating full simulation would be too slow.

Selected publications

All publications →

PINN / TSA / CCT (IEEE Access, 2026)

A. Poulose and S. Kim

IEEE Access, vol. 14, pp. 122177–122196

DOI Code

GFM/GFL transient energy functions (IEEE Access, 2024)

A. Poulose and S. Kim

IEEE Access, vol. 12, pp. 133545–133556

DOI

Renewable-rich transient-stability review (Energies, 2023)

A. Poulose and S. Kim

Energies, vol. 16, no. 5, p. 2495

DOI

Research software

Software →
Available

PINN-TSA

Physics-informed surrogate modeling for transient stability.

Python · PyTorch · ANDES

In progress

CCT Screening Toolkit

Automated CCT analysis and contingency screening.

MATLAB · Python

Planned

Grid Digital Twin Framework

Parameter estimation, dynamic model, and security assessment in one loop.

PMU analytics · surrogates

News

Full chronology →

Completed Lecturer appointment, Department of Electrical Engineering, Kyungpook National University (Logic Circuits; Numerical Analysis).

Joined Kyungpook National University as Postdoctoral Researcher, Microgrid Research Center, and as Lecturer, Department of Electrical Engineering.

Completed Ph.D. in Electronic and Electrical Engineering at Kyungpook National University (Smart Power System Lab).