TY  - CONF
AU  - Pasella, Manuela
AU  - Benigni, Andrea
AU  - Cannas, Barbara
AU  - Carta, Daniele
AU  - Muscas, Carlo
AU  - Pisano, Fabio
TI  - On the Quality of Pseudo-Measurements for Distribution System State Estimation
PB  - IEEE
M1  - FZJ-2024-05833
SP  - 1-6
PY  - 2024
AB  - With the increasing penetration of distributed en-ergy resources, Smart Grids control applications are always more dependent on monitoring data. Pseudo-measurements are used in Distribution System State Estimation to allow estimating the operating conditions of the system also when the number of field measurements is limited. Since the accuracy of the estimation depends on the quality of the pseudo-measurements, in this paper the factors that affect this quality are investigated and the performance of a machine learning-based approach for pseudo-measurements generation is evaluated. Starting from real data collected from the Forschungszentrum Jülich campus, a dataset is engineered, and the considered data coding approach is presented. Finally, different neural models based on multilayer perceptron are presented, and their performances are compared with those of trivial alternatives.
T2  - 2024 IEEE 14th International Workshop on Applied Measurements for Power Systems (AMPS)
CY  - 18 Sep 2024 - 20 Sep 2024, Caserta (Italy)
Y2  - 18 Sep 2024 - 20 Sep 2024
M2  - Caserta, Italy
LB  - PUB:(DE-HGF)8
UR  - <Go to ISI:>//WOS:001344552300003
DO  - DOI:10.1109/AMPS62611.2024.10706662
UR  - https://juser.fz-juelich.de/record/1031814
ER  -