A data-driven approach for topology correction in low voltage distribution networks with photovoltaics

Abstract

To correct outdated and incomplete topologies in low-voltage distribution networks (LVDNs) using only voltage magnitude measurements, a data-driven approach is developed by integrating machine learning algorithms with correlation analysis. Similar to existing data-driven topology identification and correction methods, the proposed approach exploits smart meter data to infer topology information. However, unlike many conventional approaches that require repeated preprocessing, multiple data sources, or separate procedures for different topology elements, it provides a unified framework that consistently uses the same up-to-date voltage magnitude dataset across all processing stages. Specifically, switch states are identified via supervised learning, while user–feeder connections and customer phase labels are refined using a modified hierarchical clustering algorithm. To address the similarity among smart meter data induced by distributed photovoltaic (PV) systems, a time-based data selection strategy is incorporated into the correlation analysis. The feasibility and robustness of the proposed approach are validated using modified real-world LVDNs and multiple incomplete smart meter datasets collected from customers in the Netherlands. The results demonstrate that the proposed approach can effectively mitigate the impact of PV-induced similarity on phase identification and improve topology correction performance. Although the approach is designed for topology correction rather than full topology reconstruction, the corrected topology improves network observability and supports distribution system operators in load balancing and PV integration.