Data-Driven Topology Generation with Physics-Guidance in LV Distribution Networks

Low-voltage distribution networks (LVDNs) topology is significant for distributed energy resources (DERs) integration, and network operation management, among others. However, topology identification is a difficult task due to the outdated recordings of networks, the uncertainty of DERs and data privacy. To address this issue, a data-driven topology generation approach is proposed based on open GIS and voltage magnitude data. The proposed approach aims to generate a topology with an accurate number of main feeders and sub-branches for adjacent substations. The boundaries between adjacent substations are first identified by using hierarchical clustering (HC) to cluster normalized voltage magnitude. Given the boundaries and the location of LV transformers, a hierarchical minimum spanning tree algorithm (HMST) is adopted to generate graph topologies using GIS data, which simultaneously verifies the number of cables under the streets. Finally, the endpoints of each feeder are estimated by clustering the transformed Pearson correlation coefficient of voltage magnitude. The feasibility of the proposed approach is evaluated on two real LVDNs in the Netherlands.