1 citations · 1 across the 8 of their papers we have counts for
9 papers
RAPTOR: RAndom-projection Physics-informed Transient sOlveR
Petros Ellinas, Benjamin Vilmann, Spyros Chatzivasileiadis +1
The complexity of time-domain simulation of modern power systems has increased significantly because converter-based resources introduce control dynamics that must be simulated alo…
Safety Screening for Voltage Control in Active Distribution Grids via Distributionally Robust Conformal Screening
Sarra Bouchkati, Petros Ellinas, Adriana Geisler +4
Deploying a new control policy for voltage control in active distribution grids requires evidence that physical limits will be satisfied before the policy is tested on the physical…
Tools to Explain Neural Networks for Power System Dynamics
Petros Ellinas, Johanna Vorwerk, Spyros Chatzivasileiadis
This paper presents, for the first time in power systems literature to our knowledge, analytical tools to explain the training performance of machine learning surrogate models for…
Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation
Petros Ellinas, Indrajit Chaudhuri, Johanna Vorwerk +1
Physics-informed machine learning surrogates are increasingly explored to accelerate dynamic simulation of generators, converters, and other power grid components. The key question…
Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels
Khai Nguyen, Petros Ellinas, Anvita Bhagavathula +1
To scale optimization and simulation, prior work has explored training machine-learning surrogates that map problem parameters to solutions inexpensively at inference time. Unfortu…
Neural Operators for Power Systems: A Physics-Informed Framework for Modeling Power System Components
Ioannis Karampinis, Petros Ellinas, Johanna Vorwerk +1
Modern power systems require fast and accurate dynamic simulations for stability assessment, digital twins, and real-time control, but classical ODE solvers are often too slow for…
Toolbox for Developing Physics Informed Neural Networks for Power Systems Components
Ioannis Karampinis, Petros Ellinas, Ignasi Ventura Nadal +2
This paper puts forward the vision of creating a library of neural-network-based models for power system simulations. Traditional numerical solvers struggle with the growing comple…
A hybrid Quantum-Classical Algorithm for Mixed-Integer Optimization in Power Systems
Petros Ellinas, Samuel Chevalier, Spyros Chatzivasileiadis
Mixed Integer Linear Programming (MILP) can be considered the backbone of the modern power system optimization process, with a large application spectrum, from Unit Commitment and…
Correctness Verification of Neural Networks Approximating Differential Equations
Petros Ellinas, Rahul Nellikath, Ignasi Ventura +2
Verification of Neural Networks (NNs) that approximate the solution of Partial Differential Equations (PDEs) is a major milestone towards enhancing their trustworthiness and accele…