注意力机制驱动的多源数据融合配网估计OA

Aiming at the challenges of data heterogeneity and multi-source in distribution networks,a self-supervised multi-source measurement data fusion method based on coding-decoding attention mechanism was proposed.This method automatically captured the correlation between data through self-supervised learning,and extracted weighted fusion features by encoding and decoding attention mechanisms to enhance the relevance,integrity and availability of data.This method can adapt to different types of input data,thus ensuring the realization of high-precision distribution network state estimation in multi-source data scenarios.Experimental results on a 57-node simulation system show that the proposed method outperforms mainstream algorithms such as GraphMDN,RetNode,AdaAtt and DR-GCN in terms of accuracy,AUC and Macro_F value.Among them,the accuracy reached 88%,the AUC increased to 76.05%,the Macro_F value reached 93.02%,and the overall performance was significantly improved.Compared with the optimal comparison algorithm,the average error is reduced by 47%,and the maximum error is controlled within 0.017.The results verify the effectiveness and generalization ability of the proposed method in multi-source fusion,power grid data modeling and state estimation.