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

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.