Verified physics-aware machine learning to transform non-linear power system stability and optimization

Información del proyecto

VeriPhIED

Identificador del acuerdo de subvención: 949899

DOI

10.3030/949899

Fecha de la firma de la CE 25 Agosto 2020

Fecha de inicio 1 Mayo 2022

Fecha de finalización 30 Abril 2027

Financiado con arreglo a

• EXCELLENT SCIENCE - European Research Council (ERC)

Coste total

€ 1 499 183,00

Aportación de la UE

€ 1 499 183,00

Coordinado por

DANMARKS TEKNISKE UNIVERSITET
Denmark

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

Open Research Data Pilot (1)

Data Management Plan (DMP)

Participation in the Open Research Data Pilot

Publicaciones

Peer reviewed articles (10)

Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems

Autores: Simon Stock, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis
Publicado en: Electric Power Systems Research, Edición 235, 2024, Página(s) 110860, ISSN 0378-7796
Editor: Elsevier BV
DOI: 10.1016/j.epsr.2024.110860

Learning Active Constraints to Efficiently Solve Linear Bilevel Problems: Application to the Generator Strategic Bidding Problem

Autores: Eléa Prat, Spyros Chatzivasileiadis
Publicado en: IEEE Transactions on Power Systems, Edición 38, 2024, Página(s) 2376-2387, ISSN 0885-8950
Editor: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpwrs.2022.3188432

Machine Learning in Power Systems: Is It Time to Trust It?

Autores: Spyros Chatzivasileiadis, Andreas Venzke, Jochen Stiasny, Georgios Misyris
Publicado en: IEEE Power and Energy Magazine, Edición 20, 2022, Página(s) 32-41, ISSN 1540-7977
Editor: Institute of Electrical and Electronics Engineers
DOI: 10.1109/mpe.2022.3150810

Modeling the AC power flow equations with optimally compact neural networks: Application to unit commitment

Autores: Alyssa Kody, Samuel Chevalier, Spyros Chatzivasileiadis, Daniel Molzahn
Publicado en: Electric Power Systems Research, Edición 213, 2024, Página(s) 108282, ISSN 0378-7796
Editor: Elsevier BV
DOI: 10.1016/j.epsr.2022.108282

PINNSim: A simulator for power system dynamics based on Physics-Informed Neural Networks

Autores: Jochen Stiasny, Baosen Zhang, Spyros Chatzivasileiadis
Publicado en: Electric Power Systems Research, Edición 235, 2024, Página(s) 110796, ISSN 0378-7796
Editor: Elsevier BV
DOI: 10.1016/j.epsr.2024.110796

Can Machine Learning Help Keep the System Secure?: Power Systems and Change Addressing the Increasing Complexity and Uncertainty During the Energy Transition

Autores: Panagiotis N. Papadopoulos, Spyros Chatzivasileiadis, Antoine Marot
Publicado en: IEEE Power and Energy Magazine, Edición 22, 2024, Página(s) 100-111, ISSN 1540-7977
Editor: Institute of Electrical and Electronics Engineers
DOI: 10.1109/mpe.2024.3421388

On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

Autores: Jiawei Wang, Pierre Pinson, Spyros Chatzivasileiadis, Mathaios Panteli, Goran Strbac, Vladimir Terzija
Publicado en: IEEE Transactions on Sustainable Energy, Edición 14, 2024, Página(s) 1230-1243, ISSN 1949-3029
Editor: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tste.2022.3194728

Physics-informed neural networks for phase locked loop transient stability assessment

Autores: Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Andreas Venzke, Mohammad Kazem Bakhshizadeh
Publicado en: Electric Power Systems Research, Edición 236, 2024, Página(s) 110790, ISSN 0378-7796
Editor: Elsevier BV
DOI: 10.1016/j.epsr.2024.110790

Physics-Informed Neural Networks for AC Optimal Power Flow

Autores: Rahul Nellikkath, Spyros Chatzivasileiadis
Publicado en: Electric Power Systems Research, Edición 212, 2024, Página(s) 108412, ISSN 0378-7796
Editor: Elsevier BV
DOI: 10.1016/j.epsr.2022.108412

Physics-informed neural networks for time-domain simulations: Accuracy, computational cost, and flexibility

Autores: Jochen Stiasny, Spyros Chatzivasileiadis
Publicado en: Electric Power Systems Research, Edición 224, 2024, Página(s) 109748, ISSN 0378-7796
Editor: Elsevier BV
DOI: 10.1016/j.epsr.2023.109748

Other (3)

A Dataset Generation Toolbox for Dynamic Security Assessment: On the Role of the Security Boundary

Autores: Bastien Giraud, Lola Charles, Agnes Marjorie Nakiganda, Johanna Vorwerk, Spyros Chatzivasileiadis
Publicado en: 2025
Editor: Working Paper
DOI: 10.48550/arxiv.2501.09513

Minimizing Worst-Case Violations of Neural Networks

Autores: Nellikkath, Rahul; Chatzivasileiadis, Spyros
Publicado en: Edición 2, 2022
Editor: Working Paper
DOI: 10.48550/arxiv.2212.10930

Correctness Verification of Neural Networks Approximating Differential Equations

Autores: Petros Ellinas, Rahul Nellikath, Ignasi Ventura, Jochen Stiasny, Spyros Chatzivasileiadis
Publicado en: Working Paper, 2024
Editor: ArXiV
DOI: 10.48550/arxiv.2402.07621

Conference proceedings (8)

Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees

Autores: Rahul Nellikkath, Spyros Chatzivasileiadis
Publicado en: 2023 IEEE Belgrade PowerTech, 2023, Página(s) 1-6
Editor: IEEE
DOI: 10.1109/powertech55446.2023.10202770

GPU-Accelerated Verification of Machine Learning Models for Power Systems

Autores: Samuel Chevalier, Ilgiz Murzakhanov, Spyros Chatzivasileiadis
Publicado en: Proceedings of the 57th Hawaii International Conference on System Sciences, 2024
Editor: IEEE

Bayesian Physics-Informed Neural Networks for Robust System Identification of Power Systems

Autores: Simon Stock, Jochen Stiasny, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis
Publicado en: 2023 IEEE Belgrade PowerTech, 2023, Página(s) 1-6
Editor: IEEE
DOI: 10.1109/powertech55446.2023.10202692

Emission-Constrained Optimization of Gas Networks: Input-Convex Neural Network Approach

Autores: Vladimir Dvorkin, Samuel Chevalier, Spyros Chatzivasileiadis
Publicado en: 2023 62nd IEEE Conference on Decision and Control (CDC), 2024, Página(s) 1575-1579
Editor: IEEE
DOI: 10.1109/cdc49753.2023.10383948

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems

Autores: Stiasny, Jochen; Chevalier, Samuel; Nellikkath, Rahul; Sævarsson, Brynjar; Chatzivasileiadis, Spyros
Publicado en: 2022 iREP Symposium - Bulk Power System Dynamics and Control - XI (iREP), Edición 4, 2022
Editor: 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022)
DOI: 10.48550/arxiv.2203.07505

Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow

Autores: Murzakhanov, Ilgiz; Venzke, Andreas; Misyris, George S.; Chatzivasileiadis, Spyros
Publicado en: Proceedings of 11th Bulk Power Systems Dynamics and Control Symposium, Edición 5, 2022
Editor: IEEE
DOI: 10.48550/arxiv.2003.07939

Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations

Autores: Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad, Samuel Chevalier, Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Panagiotis N. Papadopoulos
Publicado en: Climate Change Workshop of the International Conference on Learning Representations (ICLR), 2024
Editor: ICLR
DOI: 10.48550/arxiv.2209.05793

Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers

Autores: Samuel Chevalier, Jochen Stiasny, Spyros Chatzivasileiadis
Publicado en: Proceedings of Machine Learning Research, Edición vol 168, 2022, Página(s) 1-14
Editor: 4th Annual Conference on Learning for Dynamics and Control