@inproceedings{TischbeinKeanVertgewalletal.2023,
  author    = {Tischbein, Franziska and Kean, Kilian and Vertgewall, Chris Martin and Ulbig, Andreas and Altherr, Lena},
  title     = {Determination of the topology of low-voltage distribution grids using cluster methods},
  series = {27th International Conference on Electricity Distribution (CIRED 2023)},
  booktitle = {27th International Conference on Electricity Distribution (CIRED 2023)},
  publisher = {IEEE},
  isbn      = {978-1-83953-855-1},
  doi       = {10.1049/icp.2023.0478},
  pages     = {1 -- 5},
  year      = {2023},
  abstract  = {Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis.},
  language  = {en}
}