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001049196 005__ 20251211202159.0
001049196 0247_ $$2doi$$a10.1109/AMPS66841.2025.11219969
001049196 037__ $$aFZJ-2025-05278
001049196 041__ $$aEnglish
001049196 1001_ $$0P:(DE-HGF)0$$aBuechel, Maximilian$$b0$$eCorresponding author
001049196 1112_ $$a2025 IEEE 15th International Workshop on Applied Measurements for Power Systems (AMPS)$$cBucharest$$d2025-09-24 - 2025-09-26$$wRomania
001049196 245__ $$aDistribution Grid Voltage Estimation by Correlated Gaussian Processes
001049196 260__ $$bIEEE$$c2025
001049196 300__ $$a1-6
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001049196 520__ $$aModern power distribution grids are facing significant monitoring challenges from renewable energy volatility and persistent measurement scarcity. In this paper, we propose a data-driven approach for voltage magnitude estimation in distribution systems using correlated Gaussian processes. We demonstrate that the proposed approach can be applied to a variety of monitoring configurations. Comparable accuracies are achieved even when the number of monitoring units is significantly reduced. Moreover, the results are obtained with the help of small training sets, allowing for short observation periods to generate training data. The proposed approach offers flexibility in terms of monitoring configurations and provides reliable voltage estimation in poorly monitored distribution networks.
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001049196 536__ $$0G:(DE-HGF)POF4-1122$$a1122 - Design, Operation and Digitalization of the Future Energy Grids (POF4-112)$$cPOF4-112$$fPOF IV$$x1
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001049196 7001_ $$0P:(DE-Juel1)185033$$aZimmer, Marcel$$b1$$ufzj
001049196 7001_ $$0P:(DE-Juel1)186779$$aCarta, Daniele$$b2$$ufzj
001049196 7001_ $$0P:(DE-Juel1)179029$$aBenigni, Andrea$$b3$$ufzj
001049196 773__ $$a10.1109/AMPS66841.2025.11219969
001049196 8564_ $$uhttps://ieeexplore.ieee.org/document/11219969
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001049196 9141_ $$y2025
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