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Probabilistic Physics-Informed Graph Convolutional Network for Active Distribution System Voltage Prediction

Journal Article · · IEEE Transactions on Power Systems
 [1];  [1];  [1];  [2]
  1. University of Connecticut, Storrs, CT (United States)
  2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
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Here this letter proposes a novel data-driven probabilistic physics-informed graph convolutional network (GCN) for active distribution system voltage prediction with PVs and EVs. It leverages both measurements and network topology to accurately and efficiently predict node voltages without the need for an accurate distribution system power flow model. The dropout-enabled Bayesian inference is developed to achieve uncertainty quantification of the voltage prediction. Thanks to the network model embedding, it also has robustness against topology changes, a key difference with existing machine learning-based approaches. Comparison results with other state-of-the-art machine learning methods on a realistic 759-node distribution system demonstrate that the proposed method can achieve better accuracy and robustness under different scenarios.
Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
2283428
Report Number(s):
NREL/JA--5D00-87747; MainId:88522; UUID:85450061-992f-44a1-91dd-3e022239984d; MainAdminId:71694
Journal Information:
IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 6 Vol. 38; ISSN 0885-8950
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

References (8)

Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation conference August 2020
Optimal Steady-State Voltage Control Using Gaussian Process Learning journal October 2021
An Intelligent Data-Driven Learning Approach to Enhance Online Probabilistic Voltage Stability Margin Prediction journal July 2021
A Data-Driven Method for Prediction of Post-Fault Voltage Stability in Hybrid AC/DC Microgrids journal September 2022
Deep Sigma Point Processes-Assisted Chance-Constrained Power System Transient Stability Preventive Control journal January 2024
Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities journal January 2023
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms journal September 2023
Voltage Estimation in Low-Voltage Distribution Grids With Distributed Energy Resources journal July 2021

Figures / Tables (6)

Fig. 1(p. 2)figure Fig. 1
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Fig. 4(p. 3)figure Fig. 4
TABLE I(p. 3)table TABLE I
Fig. 5(p. 4)figure Fig. 5

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Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
distribution system
physics-informed graph convolutional network (GCN)
probabilistic voltage prediction