Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Venzke, Andreas"'
Autor:
Nellikkath, Rahul, Venzke, Andreas, Bakhshizadeh, Mohammad Kazem, Murzakhanov, Ilgiz, Chatzivasileiadis, Spyros
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 Loops (PLLs), to keep grid-tied renewable resources in synchronism wit
Externí odkaz:
http://arxiv.org/abs/2303.12116
Autor:
Nellikkath, Rahul, Murzakhanov, Ilgiz, Chatzivasileiadis, Spyros, Venzke, Andreas, Bakhshizadeh, Mohammad Kazem
Publikováno v:
In Electric Power Systems Research November 2024 236
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:
http://arxiv.org/abs/2009.03147
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:
http://arxiv.org/abs/2006.11029
This paper introduces a framework to capture previously intractable optimization constraints and transform them to a mixed-integer linear program, through the use of neural networks. We encode the feasible space of optimization problems characterized
Externí odkaz:
http://arxiv.org/abs/2003.07939
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
Externí odkaz:
http://arxiv.org/abs/2001.00898
This paper introduces for the first time, to our knowledge, a framework for physics-informed neural networks in power system applications. Exploiting the underlying physical laws governing power systems, and inspired by recent developments in the fie
Externí odkaz:
http://arxiv.org/abs/1911.03737
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:
http://arxiv.org/abs/1910.01794
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:
http://arxiv.org/abs/1910.01624
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:
http://arxiv.org/abs/1902.04815