Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Andreas Venzke"'
Publikováno v:
IEEE Power and Energy Magazine. 20:32-41
Publikováno v:
Venzke, A, Chatzivasileiadis, S & Molzahn, D K 2020, ' Inexact convex relaxations for AC optimal power flow : Towards AC feasibility ', Electric Power Systems Research, vol. 187, 106480 . https://doi.org/10.1016/j.epsr.2020.106480
Convex relaxations of AC optimal power flow (AC-OPF) problems have attracted significant interest as in several instances they provably yield the global optimum to the original non-convex problem. If, however, the relaxation is inexact, the obtained
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dba079343ea7b6e55fe6ae135d8b9eb3
https://orbit.dtu.dk/en/publications/bd918bac-bca8-4e69-87ad-352cfb5930e7
https://orbit.dtu.dk/en/publications/bd918bac-bca8-4e69-87ad-352cfb5930e7
Publikováno v:
SmartGridComm
Deep Neural Networks (DNNs) approaches for the Optimal Power Flow (OPF) problem received considerable attention recently. A key challenge of these approaches lies in ensuring the feasibility of the predicted solutions to physical system constraints.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d25501358593ee5a386ebfb0f99a3492
http://arxiv.org/abs/2009.03147
http://arxiv.org/abs/2009.03147
Publikováno v:
SmartGridComm
This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems as a guiding example. Neural networks have the potential to substantial
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::141ebe504e1976da005f0f17bdee7fe8
Advances in data-driven methods have sparked renewed interest for applications in power systems. Creating datasets for successful application of these methods has proven to be very challenging, especially when considering power system security. This
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6324b670b22d27e8f91d7eef072df95d
http://arxiv.org/abs/1910.01794
http://arxiv.org/abs/1910.01794
Publikováno v:
International Journal of Electrical Power & Energy Systems. 127:106625
Convex relaxations of the AC Optimal Power Flow (OPF) problem are essential not only for identifying the globally optimal solution but also for enabling the use of OPF formulations in Bilevel Programming and Mathematical Programs with Equilibrium Con
Publikováno v:
Venzke, A & Chatzivasileiadis, S 2021, ' Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications ', IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 383-397 . https://doi.org/10.1109/TSG.2020.3009401
This paper presents for the first time, to our knowledge, a framework for verifying neural network behavior in power system applications. Up to this moment, neural networks have been applied in power systems as a black-box; this has presented a major
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::247069aaf97fc778c304c40c741683fc
Publikováno v:
IEEE Transactions on Power Systems. 35:4143-4143
Publikováno v:
IEEE Transactions on Power Systems. 35:4142-4142
Publikováno v:
Thams, F, Venzke, A, Eriksson, R & Chatzivasileiadis, S 2020, ' Efficient Database Generation for Data-driven Security Assessment of Power Systems ', IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 30-41 . https://doi.org/10.1109/TPWRS.2018.2890769
Power system security assessment methods require large datasets of operating points to train or test their performance. As historical data often contain limited number of abnormal situations, simulation data are necessary to accurately determine the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1dfd5eda8b3b4b36eb08415e66f25d46
http://arxiv.org/abs/1806.01074
http://arxiv.org/abs/1806.01074