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@INPROCEEDINGS{Pasella:1049120,
      author       = {Pasella, Manuela and Benigni, Andrea and Cannas, Barbara
                      and Carta, Daniele and Muscas, Carlo and Pegoraro, Paolo
                      Attilio and Pisano, Fabio and Sitzia, Carlo},
      title        = {{I}mpact of {P}seudo-{M}easurements {G}eneration on
                      {D}istribution {S}ystem {S}tate {E}stimation},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-05212},
      pages        = {1-6},
      year         = {2025},
      abstract     = {The evolving structural changes in power networks have a
                      significant impact on the management of monitoring and
                      control applications. Among them, Distribution System State
                      Estimation (DSSE) faces inherent limitations due to
                      uncertainties arising from these transformations, which
                      often lead to a degradation in the quality of measurements
                      and pseudo-measurements used in state estimation routines.
                      To mitigate these challenges, Machine Learning techniques
                      are increasingly recognized as effective solutions to
                      improve the performance of monitoring applications. In this
                      context, this paper aims to assess how the prediction of
                      active and reactive powers obtained through a Multi-Layer
                      Perceptron (MLP) neural network and compared with simple
                      benchmark models, affects DSSE performance. Firstly,
                      starting from real data collected from the Forschungszentrum
                      Jülich campus, the MLP model has been characterized and,
                      finally, DSSE has been evaluated by means of several
                      numerical simulations. The preliminary exploratory results
                      have suggested that the proposed model shows promising
                      potential in improving the accuracy of DSSE. These initial
                      results suggest that it may be worth investigating more
                      complex neural models in the future, with the aim of further
                      enhancing DSSE performance and providing system operators
                      with increasingly reliable monitoring and control tools.},
      month         = {Sep},
      date          = {2025-09-24},
      organization  = {2025 IEEE 15th International Workshop
                       on Applied Measurements for Power
                       Systems (AMPS), Bucharest (Romania), 24
                       Sep 2025 - 26 Sep 2025},
      cin          = {ICE-1},
      cid          = {I:(DE-Juel1)ICE-1-20170217},
      pnm          = {1121 - Digitalization and Systems Technology for
                      Flexibility Solutions (POF4-112) / 1122 - Design, Operation
                      and Digitalization of the Future Energy Grids (POF4-112) /
                      1123 - Smart Areas and Research Platforms (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1121 / G:(DE-HGF)POF4-1122 /
                      G:(DE-HGF)POF4-1123},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.1109/AMPS66841.2025.11219956},
      url          = {https://juser.fz-juelich.de/record/1049120},
}