Graph Attention Enabled Convolutional Network for Distribution System Probabilistic Power Flow

Huayi Wu, Minghao Wang, Zhao Xu, Youwei Jia

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


Probabilistic power flow (PPF) is pivotal to quantifying the state uncertainties of distribution power systems. However, it is very challenging due to underlying complex correlations among renewable outputs. To address this problem, a graph attention enabled convolutional network (GAECN) is proposed to approximate PPF in this article. Specifically, the graph convolutional layer of GAECN is used to aggregate the correlations among the nodal power injections during the training process. Within this layer, a full self-adaptive graph convolutional operation is proposed to automatically capture and learn the implicit correlation for achieving significantly enhanced accuracy of PPF. This layer is then followed by the convolutional neural network to capture the uncertain generation of renewable energy to achieve the robust computation of system state variable distributions. The simulation results demonstrate the accuracy and efficiency of the proposed method in IEEE 33, PG&E 69-node, 118-node, and practical 76-node distribution systems.

Original languageEnglish
Pages (from-to)7068-7078
Number of pages11
JournalIEEE Transactions on Industry Applications
Issue number6
Publication statusPublished - 26 Aug 2022


  • Correlation
  • graph
  • node embedding
  • probabilistic power flow (PPF)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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