To correct the outdated and incomplete topology of low voltage distribution networks (LVDNs) solely based on voltage magnitudes, a data driven approach is developed based on machine learning algorithms and correlation analysis. Meanwhile, to address the similarity among smart meter (SM) data induced by distributed photovoltaic (PV) systems, a time based SM data selection strategy is combined with the proposed correlation analysis. Unlike offline approaches, the proposed approach uses up to date voltage magnitudes to help distribution system operators determine switch states via supervised learning and refine user feeder connections and customer phase labels through a modified hierarchical clustering algorithm. The feasibility and robustness of the proposed approach are validated using modified real world LVDNs and multiple incomplete SM datasets collected from customers in the Netherlands. The results demonstrate that the time-based SM data selection strategy effectively mitigates its impact on phase identification, and the corrected topology not only improves network observability but also supports network operators in load balancing and PV consumption.
Categories: eess.SY cs.SY