164 citations · 213 across the 13 of their papers we have counts for
13 papers
Physics-Informed Neural Network Models for EMT Simulators
Ignasi Ventura Nadal, Nicolae Darii, 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…
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…
Physics-Informed Neural Networks in Power System Dynamics: Improving Simulation Accuracy
Ignasi Ventura Nadal, Rahul Nellikkath, Spyros Chatzivasileiadis
The importance and cost of time-domain simulations when studying power systems have exponentially increased in the last decades. With the growing share of renewable energy sources,…
Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow
Rahul Nellikkath, Mathieu Tanneau, Pascal Van Hentenryck +1
Optimal Power Flow (OPF) is a valuable tool for power system operators, but it is a difficult problem to solve for large systems. Machine Learning (ML) algorithms, especially Neura…
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…
Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees
Rahul Nellikkath, Spyros Chatzivasileiadis
Machine learning algorithms, especially Neural Networks (NNs), are a valuable tool used to approximate non-linear relationships, like the AC-Optimal Power Flow (AC-OPF), with consi…
Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessment
Rahul Nellikkath, Andreas Venzke, Mohammad Kazem Bakhshizadeh +2
A significant increase in renewable energy production is necessary to achieve the UN's net-zero emission targets for 2050. Using power-electronic controllers, such as Phase Locked…
Minimizing Worst-Case Violations of Neural Networks
Rahul Nellikkath, Spyros Chatzivasileiadis
Machine learning (ML) algorithms are remarkably good at approximating complex non-linear relationships. Most ML training processes, however, are designed to deliver ML tools with g…
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations
Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad +5
Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This letter first seeks to…
Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath +2
Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represen…
Physics-Informed Neural Networks for AC Optimal Power Flow
Rahul Nellikkath, Spyros Chatzivasileiadis
This paper introduces, for the first time to our knowledge, physics-informed neural networks to accurately estimate the AC-OPF result and delivers rigorous guarantees about their p…
Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets
Eléa Prat, Irena Dukovska, Lars Herre +3
This paper proposes a method to design network-aware flexibility requests for local flexibility markets. These markets are becoming increasingly important for distribution system o…
Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow
Rahul Nellikkath, Spyros Chatzivasileiadis
Physics-informed neural networks exploit the existing models of the underlying physical systems to generate higher accuracy results with fewer data. Such approaches can help drasti…