no code implementations • 21 Mar 2023 • Rahul Nellikkath, Andreas Venzke, Mohammad Kazem Bakhshizadeh, Ilgiz Murzakhanov, Spyros Chatzivasileiadis
However, using EMT simulations or Reduced-order models (ROMs) to accurately determine the ROA is computationally expensive.
no code implementations • 19 Jun 2020 • Andreas Venzke, Guannan Qu, Steven Low, Spyros Chatzivasileiadis
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.
no code implementations • 17 Mar 2020 • Ilgiz Murzakhanov, Andreas Venzke, George S. Misyris, Spyros Chatzivasileiadis
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.
2 code implementations • 9 Nov 2019 • George S. Misyris, Andreas Venzke, Spyros Chatzivasileiadis
This work unlocks a range of opportunities in power systems, being able to determine dynamic states, such as rotor angles and frequency, and uncertain parameters such as inertia and damping at a fraction of the computational time required by conventional methods.
no code implementations • 3 Oct 2019 • Andreas Venzke, Spyros Chatzivasileiadis
This paper presents for the first time, to our knowledge, a framework for verifying neural network behavior in power system applications.
no code implementations • 4 Jun 2018 • Florian Thams, Andreas Venzke, Robert Eriksson, Spyros Chatzivasileiadis
This paper proposes a modular and highly scalable algorithm for computationally efficient database generation.
Systems and Control
