116 citations · 283 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Inexact Convex Relaxations for AC Optimal Power Flow: Towards AC Feasibility
Andreas Venzke, Spyros Chatzivasileiadis, Daniel K. Molzahn
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…