8 citations · 8 across the 1 of their papers we have counts for
7 papers
DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility
Tianyu Zhao, Xiang Pan, Minghua Chen +2
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 f…
Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
Andreas Venzke, Guannan Qu, Steven Low +1
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 a…
Second-Order Cone Relaxations of the Optimal Power Flow for Active Distribution Grids
Lucien Bobo, Andreas Venzke, Spyros Chatzivasileiadis
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…
Physics-Informed Neural Networks for Power Systems
George S. Misyris, Andreas Venzke, Spyros Chatzivasileiadis
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 g…
Efficient Creation of Datasets for Data-Driven Power System Applications
Andreas Venzke, Daniel K. Molzahn, Spyros Chatzivasileiadis
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…
Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications
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. Up to this moment, neural networks have be…