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

Informacje na temat projektu

VeriPhIED

Identyfikator umowy o grant: 949899

DOI

10.3030/949899

Data podpisania przez KE 25 Sierpnia 2020

Data rozpoczęcia 1 Maja 2022

Data zakończenia 30 Kwietnia 2027

Finansowanie w ramach

• EXCELLENT SCIENCE - European Research Council (ERC)

Koszt całkowity

€ 1 499 183,00

Wkład UE

€ 1 499 183,00

Koordynowany przez

DANMARKS TEKNISKE UNIVERSITET
Denmark

CORDIS oferuje możliwość skorzystania z odnośników do publicznie dostępnych publikacji i rezultatów projektów realizowanych w ramach programów ramowych HORYZONT.

Odnośniki do rezultatów i publikacji związanych z poszczególnymi projektami 7PR, a także odnośniki do niektórych konkretnych kategorii wyników, takich jak zbiory danych i oprogramowanie, są dynamicznie pobierane z systemu OpenAIRE .

Rezultaty

Open Research Data Pilot (1)

Data Management Plan (DMP)

Participation in the Open Research Data Pilot

Publikacje

Peer reviewed articles (10)

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

Autorzy: Simon Stock, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis
Opublikowane w: Electric Power Systems Research, Numer 235, 2024, Strona(/y) 110860, ISSN 0378-7796
Wydawca: 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

Autorzy: Eléa Prat, Spyros Chatzivasileiadis
Opublikowane w: IEEE Transactions on Power Systems, Numer 38, 2024, Strona(/y) 2376-2387, ISSN 0885-8950
Wydawca: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpwrs.2022.3188432

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

Autorzy: Spyros Chatzivasileiadis, Andreas Venzke, Jochen Stiasny, Georgios Misyris
Opublikowane w: IEEE Power and Energy Magazine, Numer 20, 2022, Strona(/y) 32-41, ISSN 1540-7977
Wydawca: 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

Autorzy: Alyssa Kody, Samuel Chevalier, Spyros Chatzivasileiadis, Daniel Molzahn
Opublikowane w: Electric Power Systems Research, Numer 213, 2024, Strona(/y) 108282, ISSN 0378-7796
Wydawca: Elsevier BV
DOI: 10.1016/j.epsr.2022.108282

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

Autorzy: Jochen Stiasny, Baosen Zhang, Spyros Chatzivasileiadis
Opublikowane w: Electric Power Systems Research, Numer 235, 2024, Strona(/y) 110796, ISSN 0378-7796
Wydawca: 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

Autorzy: Panagiotis N. Papadopoulos, Spyros Chatzivasileiadis, Antoine Marot
Opublikowane w: IEEE Power and Energy Magazine, Numer 22, 2024, Strona(/y) 100-111, ISSN 1540-7977
Wydawca: Institute of Electrical and Electronics Engineers
DOI: 10.1109/mpe.2024.3421388

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

Autorzy: Jiawei Wang, Pierre Pinson, Spyros Chatzivasileiadis, Mathaios Panteli, Goran Strbac, Vladimir Terzija
Opublikowane w: IEEE Transactions on Sustainable Energy, Numer 14, 2024, Strona(/y) 1230-1243, ISSN 1949-3029
Wydawca: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tste.2022.3194728

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

Autorzy: Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Andreas Venzke, Mohammad Kazem Bakhshizadeh
Opublikowane w: Electric Power Systems Research, Numer 236, 2024, Strona(/y) 110790, ISSN 0378-7796
Wydawca: Elsevier BV
DOI: 10.1016/j.epsr.2024.110790

Physics-Informed Neural Networks for AC Optimal Power Flow

Autorzy: Rahul Nellikkath, Spyros Chatzivasileiadis
Opublikowane w: Electric Power Systems Research, Numer 212, 2024, Strona(/y) 108412, ISSN 0378-7796
Wydawca: Elsevier BV
DOI: 10.1016/j.epsr.2022.108412

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

Autorzy: Jochen Stiasny, Spyros Chatzivasileiadis
Opublikowane w: Electric Power Systems Research, Numer 224, 2024, Strona(/y) 109748, ISSN 0378-7796
Wydawca: 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

Autorzy: Bastien Giraud, Lola Charles, Agnes Marjorie Nakiganda, Johanna Vorwerk, Spyros Chatzivasileiadis
Opublikowane w: 2025
Wydawca: Working Paper
DOI: 10.48550/arxiv.2501.09513

Minimizing Worst-Case Violations of Neural Networks

Autorzy: Nellikkath, Rahul; Chatzivasileiadis, Spyros
Opublikowane w: Numer 2, 2022
Wydawca: Working Paper
DOI: 10.48550/arxiv.2212.10930

Correctness Verification of Neural Networks Approximating Differential Equations

Autorzy: Petros Ellinas, Rahul Nellikath, Ignasi Ventura, Jochen Stiasny, Spyros Chatzivasileiadis
Opublikowane w: Working Paper, 2024
Wydawca: ArXiV
DOI: 10.48550/arxiv.2402.07621

Conference proceedings (8)

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

Autorzy: Rahul Nellikkath, Spyros Chatzivasileiadis
Opublikowane w: 2023 IEEE Belgrade PowerTech, 2023, Strona(/y) 1-6
Wydawca: IEEE
DOI: 10.1109/powertech55446.2023.10202770

GPU-Accelerated Verification of Machine Learning Models for Power Systems

Autorzy: Samuel Chevalier, Ilgiz Murzakhanov, Spyros Chatzivasileiadis
Opublikowane w: Proceedings of the 57th Hawaii International Conference on System Sciences, 2024
Wydawca: IEEE

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

Autorzy: Simon Stock, Jochen Stiasny, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis
Opublikowane w: 2023 IEEE Belgrade PowerTech, 2023, Strona(/y) 1-6
Wydawca: IEEE
DOI: 10.1109/powertech55446.2023.10202692

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

Autorzy: Vladimir Dvorkin, Samuel Chevalier, Spyros Chatzivasileiadis
Opublikowane w: 2023 62nd IEEE Conference on Decision and Control (CDC), 2024, Strona(/y) 1575-1579
Wydawca: IEEE
DOI: 10.1109/cdc49753.2023.10383948

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

Autorzy: Stiasny, Jochen; Chevalier, Samuel; Nellikkath, Rahul; Sævarsson, Brynjar; Chatzivasileiadis, Spyros
Opublikowane w: 2022 iREP Symposium - Bulk Power System Dynamics and Control - XI (iREP), Numer 4, 2022
Wydawca: 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

Autorzy: Murzakhanov, Ilgiz; Venzke, Andreas; Misyris, George S.; Chatzivasileiadis, Spyros
Opublikowane w: Proceedings of 11th Bulk Power Systems Dynamics and Control Symposium, Numer 5, 2022
Wydawca: IEEE
DOI: 10.48550/arxiv.2003.07939

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

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

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

Autorzy: Samuel Chevalier, Jochen Stiasny, Spyros Chatzivasileiadis
Opublikowane w: Proceedings of Machine Learning Research, Numer vol 168, 2022, Strona(/y) 1-14
Wydawca: 4th Annual Conference on Learning for Dynamics and Control