Data-driven Optimal Power Flow: A Physics-Informed Machine Learning
Approach

Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach

31 May 2020

Junbo Zhao

Papers citing "Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach"

7 / 7 papers shown

Title
Time and Frequency Domain-based Anomaly Detection in Smart Meter Data for Distribution Network Studies Petar Labura Tomislav Antic Tomislav Capuder 35 0 0 25 Apr 2025
Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids Deepak Tiwari Mehdi Jabbari Zideh Veeru Talreja Vishal Verma S. K. Solanki J. Solanki 41 9 0 15 Jan 2024
Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks Chen Li Alexander Kies Kai Zhou Markus Schlott O. Sayed Mariia Bilousova Horst Stoecker 21 4 0 23 Nov 2023
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study Robert Ferrando Laurent Pagnier R. Mieth Zhirui Liang Y. Dvorkin D. Bienstock Michael Chertkov 26 7 0 31 Mar 2023
Compact Optimization Learning for AC Optimal Power Flow Seonho Park Wenbo Chen Terrence W.K. Mak Pascal Van Hentenryck 35 17 0 21 Jan 2023
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch Wenbo Chen Seonho Park Mathieu Tanneau Pascal Van Hentenryck 34 42 0 27 Dec 2021
Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems Tianyu Zhao Xiang Pan Minghua Chen S. Low 32 10 0 15 Dec 2021