LI Qizhou;LI Liang;ZHAO Jian;GAO Yuan;SUN Zhou;CHEN Feng;

• 1:College of Electrical Engineering
• 2: Shanghai University of Electric Power
• 3:State Grid Shengzhou Power Supply Company
• 4:State Grid Zhejiang Electric Power Co.

Abstract：

Incomplete deployment of measurement devices and data transmission losses can result in sparse measurements in distribution networks. To address this issue, this paper proposes an inference method for sparse measurements 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 designed 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 encoder 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 features. 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.

Key Words： distribution network;sparse measurement;GINN;data completion

Abstract:

Keywords:

Foundation: 国家自然科学基金(51907114)

Authors: LI Qizhou;LI Liang;ZHAO Jian;GAO Yuan;SUN Zhou;CHEN Feng;

DOI: 10.19585/j.zjdl.202603009

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