Publikationen - Netze für Erneuerbare Energien

Patrick Mack, Markus de Koster, Patrick Lehnen, Eberhard Waffenschmidt, Ingo Stadler,
"Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks - Harmonic State Estimation",
Energies 2024, 1, 0. https://doi.org/, 31 Oct 2024.

State estimation methods provide knowledge about unmeasured locations in a power grid by learning a physical system’s non-linear relationships. This article examines a new flexible, close-to-real-time concept of harmonic state estimation using synchronized measurements processed in a neural network. A physics-aware approach enhances a data-driven model, taking into account the structure of the electrical network. An OpenDSS simulation generates data for model training and validation. Different load profiles for both training and testing were utilized to increase the variance in the data. The results of the presented concept demonstrate high accuracy compared to other methods for harmonic orders 1to 20.

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