浙江电力2026，Vol.45Issue(3)：96-105,10.DOI:10.19585/j.zjdl.202603009
基于图填补神经网络的配电网稀疏量测数据推演方法
An inference method for sparse measurements in distribution networks based on a graph imputation neural network
摘要
Abstract
Incomplete deployment of measurement devices and data transmission losses can result in sparse mea-surements in distribution networks.To address this issue,this paper proposes an inference method for sparse mea-surements based on a graph imputation neural network(GINN).The proposed method aims to improve the accuracy and reduce the sparsity of existing measurements.First,a GINN-based measurement feature encoder module is de-signed to extract power flow features such as power and voltage from nodal measurements.A transformer network is employed to model cross-feature correlations among different power flow features.Second,a GINN-based graph en-coder module explicitly encodes topological connectivity between distribution network nodes.By incorporating a graph convolutional network(GCN),this module enables the propagation and updating of nodal power flow fea-tures.Subsequently,by leveraging two modules to capture the correlations between different power flow features of node measurements and the topological correlations across nodes,the missing data is inferred and completed using the sparse measurements.Finally,simulation tests are conducted on IEEE 14-,30-,57-,and 118-bus systems to validate the effectiveness of the proposed method.关键词
配电网/稀疏量测/图填补神经网络/数据补齐Key words
distribution network/sparse measurement/GINN/data completion引用本文复制引用
李企洲,李梁,赵健,高源,孙洲,陈峰..基于图填补神经网络的配电网稀疏量测数据推演方法[J].浙江电力,2026,45(3):96-105,10.基金项目
国家自然科学基金(51907114) （51907114）
