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Probabilistic Physics-Informed Graph Convolutional Network for Active Distribution System Voltage Prediction
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DOI：10.1109/TPWRS.2023.3311638.png)
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摘要
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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.
Keyword:
Voltage
Probabilistic logic
Voltage control
Predictive models
Voltage measurement
Uncertainty
Topology
Distribution system
physics-informed graph convolutional network (GCN)
probabilistic voltage prediction
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
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