Evaluierung relevanter Messstellen zur Erhöhung der Netztransparenz in Niederspannungsnetzen mittels künstlicher neuronaler Netze

As part of the ongoing energy transition, smart-metering technology will be installed at the low-voltage level. In addition to the deployment of smart meters, MV/LV transformers, feeders in local substations (SS), or feeders of cable distribution cabinets (CDC) will be equipped with measuring devices. However, more technical approaches are needed to evaluate the decision-making process for the placements of measurements. In this study, optimal locations for measurement devices at low-voltage grids are determined using artificial neural network (ANN) estimations. Time series simulations are computed using secondary data to provide training and test sets. The trained ANNs determine the quality of each measuring location based on estimation errors. The results of the analyses demonstrate that the methodology can support a focused deployment of measurement devices and thus contribute to an increase in grid transparency. Furthermore, measurements must be positioned individually for each LV grid, as the estimation results significantly depend on the underlying secondary data.