Missing Measurement Reconstruction for Active Distribution Networks Considering Topological Generalisation

ABSTRACT

The lack of measurement data can significantly impede situation awareness in active distribution networks (ADNs), making it difficult to accurately assess grid operation status and promptly identify potential risks. Moreover, frequent topological changes in ADNs introduce additional uncertainties into measurement data. To tackle the issue of missing measurements in ADNs with uncertain topologies, this paper proposes a novel graph attention network‐based topologically generalised missing measurement reconstruction (MMR) method. First, the MMR problem is formulated as a time‐series graph complementation model. A graph attention network‐based MMR framework is then constructed to capture the spatiotemporal correlations between node features. Specifically, a topology generalisation strategy is developed by expanding node adjacency relationships and constructing a reduced reconstruction topology set. Simulation tests and sensitivity analysis are conducted on a modified IEEE 33‐bus distribution network and the results verify the effectiveness of the proposed method.