14 papers
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…
CONDUCTOR: An LLM-Orchestrated Digital Twin for Uncertainty-Aware Distribution Grid Operations
Antonio Alcántara, Aysegül Kahraman, Anosh Arshad Sundhu +1
Large language models (LLMs) are proposed as natural-language interfaces to power system analysis, yet existing frameworks are validated almost exclusively on synthetic benchmarks…
Trustworthiness Layer for Foundation Models in Power Systems: Application to N-k Contingency Screening
Antonio Alcántara, Spyros Chatzivasileiadis
We propose a model-agnostic trustworthiness layer that equips any foundation model (FM) for power systems with statistically valid prediction intervals. The layer offers two calibr…
Digital Twin for Real-Time Security Assessment and Flexibility Activation in the Bornholm Distribution System
Anosh Arshad Sundhu, Aysegül Kahraman, Spyros Chatzivasileiadis
The increasing penetration of distributed energy resources (DERs) is transforming distribution networks into actively managed systems, introducing challenges related to voltage reg…
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…
Physics-Informed Neural Network Models for EMT Simulators
Ignasi Ventura Nadal, Mohammad Kazem Bakhshizadeh, Petros Aristidou +4
This is the first paper, to the best of our knowledge, to propose a framework that integrates Physics-Informed Neural Network (PINN) models in Electromagnetic Transient (EMT) simul…