Probabilistic Physics-Informed Graph Convolutional Network for Active Distribution System Voltage Prediction
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA
2. National Renewable Energy Laboratory, Golden, CO, USA
Funder
National Renewable Energy Laboratory
Alliance for Sustainable Energy
LLC
U.S. Department of Energy
U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology
Link
https://ieeexplore.ieee.org/ielam/59/10288290/10238825-aam.pdf
Reference10 articles.
1. Simple and deep graph convolutional networks;chen;Proc 37th Int Conf Mach Learn,0
2. Optimal Steady-State Voltage Control Using Gaussian Process Learning
3. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning;gal;Proc 33rd Int Conf Mach Learn,0
4. Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms
5. A Data-Driven Method for Prediction of Post-Fault Voltage Stability in Hybrid AC/DC Microgrids
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