Distribution Network Situational Awareness Prediction Based on Spatio-Temporal Attention Dynamic Graph Neural Network

Xixi Qiu, Yuteng Huang, Guojin Liu (), Jiaxiang Yan and Shan Chen
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Xixi Qiu: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China
Yuteng Huang: State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311000, China
Guojin Liu: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China
Jiaxiang Yan: State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311000, China
Shan Chen: State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311000, China

Energies, 2025, vol. 18, issue 16, 1-20

Abstract: Distribution network situational awareness prediction is a key technology for ensuring the safe and stable operation of distribution networks. However, most existing methods suffer from spatio-temporal dynamic correlation and dynamic topology, resulting in unsatisfactory performance. To address these issues, we propose a distribution network situational awareness prediction method based on a spatio-temporal attention dynamic graph neural network model that realizes the decoupling of spatio-temporal features of the distribution network data by adopting the alternating stacking of the multi-head self-attention mechanism with temporal dynamic perception and the spatial dynamic graph convolution module. Furthermore, the dynamic correlation matrix is introduced to adaptively adjust the node interaction weights to effectively handle the network dynamic topology information. Through extensive experiments, the proposed method outperforms eight baseline models.

Keywords: distribution network; situational awareness prediction; graph neural network; self-attention mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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