001049120 001__ 1049120
001049120 005__ 20251211202157.0
001049120 0247_ $$2doi$$a10.1109/AMPS66841.2025.11219956
001049120 037__ $$aFZJ-2025-05212
001049120 1001_ $$0P:(DE-HGF)0$$aPasella, Manuela$$b0$$eCorresponding author
001049120 1112_ $$a2025 IEEE 15th International Workshop on Applied Measurements for Power Systems (AMPS)$$cBucharest$$d2025-09-24 - 2025-09-26$$wRomania
001049120 245__ $$aImpact of Pseudo-Measurements Generation on Distribution System State Estimation
001049120 260__ $$bIEEE$$c2025
001049120 300__ $$a1-6
001049120 3367_ $$2ORCID$$aCONFERENCE_PAPER
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001049120 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1765435959_28636
001049120 520__ $$aThe 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.
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001049120 536__ $$0G:(DE-HGF)POF4-1122$$a1122 - Design, Operation and Digitalization of the Future Energy Grids (POF4-112)$$cPOF4-112$$fPOF IV$$x1
001049120 536__ $$0G:(DE-HGF)POF4-1123$$a1123 - Smart Areas and Research Platforms (POF4-112)$$cPOF4-112$$fPOF IV$$x2
001049120 588__ $$aDataset connected to CrossRef Conference
001049120 7001_ $$0P:(DE-Juel1)179029$$aBenigni, Andrea$$b1$$ufzj
001049120 7001_ $$0P:(DE-HGF)0$$aCannas, Barbara$$b2
001049120 7001_ $$0P:(DE-Juel1)186779$$aCarta, Daniele$$b3$$ufzj
001049120 7001_ $$0P:(DE-HGF)0$$aMuscas, Carlo$$b4
001049120 7001_ $$0P:(DE-HGF)0$$aPegoraro, Paolo Attilio$$b5
001049120 7001_ $$0P:(DE-HGF)0$$aPisano, Fabio$$b6
001049120 7001_ $$0P:(DE-HGF)0$$aSitzia, Carlo$$b7
001049120 773__ $$a10.1109/AMPS66841.2025.11219956
001049120 8564_ $$uhttps://ieeexplore-dev.ieee.org/document/11219956
001049120 909CO $$ooai:juser.fz-juelich.de:1049120$$pVDB
001049120 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Cagliari$$b0
001049120 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179029$$aForschungszentrum Jülich$$b1$$kFZJ
001049120 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Cagliari$$b2
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001049120 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Cagliari$$b4
001049120 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Cagliari$$b5
001049120 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Cagliari$$b6
001049120 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Cagliari$$b7
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001049120 9131_ $$0G:(DE-HGF)POF4-112$$1G:(DE-HGF)POF4-110$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1123$$aDE-HGF$$bForschungsbereich Energie$$lEnergiesystemdesign (ESD)$$vDigitalisierung und Systemtechnik$$x2
001049120 9141_ $$y2025
001049120 920__ $$lno
001049120 9201_ $$0I:(DE-Juel1)ICE-1-20170217$$kICE-1$$lModellierung von Energiesystemen$$x0
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001049120 980__ $$aI:(DE-Juel1)ICE-1-20170217
001049120 980__ $$aUNRESTRICTED