Publikationen - Netze für Erneuerbare Energien
Markus de Koster,
This thesis explores the suitability of physics-aware neural
networks for power quality state estimation in distribution grids. For
that, power quality data was generated in a simulated environment and used for training and evaluation of dierent
neural network models. Comprehensive data analyses were carried out,
focusing on optimal representations and processing of power quality
data for neural network applications. Comparative assessments with
traditional fully connected architectures demonstrated the superior
capabilities of physics-aware models, which utilize the physical grid structure as a
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Voltage predictions for a single randomly chosen iteration of the test set at 50 Hz.
Physics-aware neural networks (PANN) and dense neural networks (DNN) predict values for all nodes based on inputs at three nodes with added measurement noise.