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001031814 0247_ $$2doi$$a10.1109/AMPS62611.2024.10706662
001031814 0247_ $$2WOS$$aWOS:001344552300003
001031814 037__ $$aFZJ-2024-05833
001031814 1001_ $$0P:(DE-Juel1)200465$$aPasella, Manuela$$b0$$eCorresponding author$$ufzj
001031814 1112_ $$a2024 IEEE 14th International Workshop on Applied Measurements for Power Systems (AMPS)$$cCaserta$$d2024-09-18 - 2024-09-20$$wItaly
001031814 245__ $$aOn the Quality of Pseudo-Measurements for Distribution System State Estimation
001031814 260__ $$bIEEE$$c2024
001031814 300__ $$a1-6
001031814 3367_ $$2ORCID$$aCONFERENCE_PAPER
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001031814 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1728895315_31821
001031814 520__ $$aWith the increasing penetration of distributed en-ergy resources, Smart Grids control applications are always more dependent on monitoring data. Pseudo-measurements are used in Distribution System State Estimation to allow estimating the operating conditions of the system also when the number of field measurements is limited. Since the accuracy of the estimation depends on the quality of the pseudo-measurements, in this paper the factors that affect this quality are investigated and the performance of a machine learning-based approach for pseudo-measurements generation is evaluated. Starting from real data collected from the Forschungszentrum Jülich campus, a dataset is engineered, and the considered data coding approach is presented. Finally, different neural models based on multilayer perceptron are presented, and their performances are compared with those of trivial alternatives.
001031814 536__ $$0G:(DE-HGF)POF4-1122$$a1122 - Design, Operation and Digitalization of the Future Energy Grids (POF4-112)$$cPOF4-112$$fPOF IV$$x0
001031814 536__ $$0G:(DE-HGF)POF4-1123$$a1123 - Smart Areas and Research Platforms (POF4-112)$$cPOF4-112$$fPOF IV$$x1
001031814 588__ $$aDataset connected to CrossRef Conference
001031814 7001_ $$0P:(DE-Juel1)179029$$aBenigni, Andrea$$b1$$ufzj
001031814 7001_ $$0P:(DE-HGF)0$$aCannas, Barbara$$b2
001031814 7001_ $$0P:(DE-Juel1)186779$$aCarta, Daniele$$b3$$ufzj
001031814 7001_ $$0P:(DE-HGF)0$$aMuscas, Carlo$$b4
001031814 7001_ $$0P:(DE-HGF)0$$aPisano, Fabio$$b5
001031814 773__ $$a10.1109/AMPS62611.2024.10706662
001031814 8564_ $$uhttps://ieeexplore.ieee.org/abstract/document/10706662
001031814 909CO $$ooai:juser.fz-juelich.de:1031814$$pVDB
001031814 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)200465$$aForschungszentrum Jülich$$b0$$kFZJ
001031814 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)200465$$a University of Cagliari$$b0
001031814 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179029$$aForschungszentrum Jülich$$b1$$kFZJ
001031814 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Cagliari$$b2
001031814 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186779$$aForschungszentrum Jülich$$b3$$kFZJ
001031814 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Cagliari$$b4
001031814 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a University of Cagliari$$b5
001031814 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)POF4-1122$$aDE-HGF$$bForschungsbereich Energie$$lEnergiesystemdesign (ESD)$$vDigitalisierung und Systemtechnik$$x0
001031814 9131_ $$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)$$vDigitalisierung und Systemtechnik$$x1
001031814 9141_ $$y2024
001031814 920__ $$lno
001031814 9201_ $$0I:(DE-Juel1)ICE-1-20170217$$kICE-1$$lModellierung von Energiesystemen$$x0
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