Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks—Harmonic State Estimation

Patrick Mack (), Markus de Koster, Patrick Lehnen, Eberhard Waffenschmidt () and Ingo Stadler
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Patrick Mack: Cologne Institute for Renewable Energies (CIRE) & Institute for Electrical Power Engineering, TH Köln, 50679 Cologne, Germany
Markus de Koster: Cologne Institute for Renewable Energies (CIRE) & Institute for Electrical Power Engineering, TH Köln, 50679 Cologne, Germany
Patrick Lehnen: Cologne Institute for Renewable Energies (CIRE) & Institute for Electrical Power Engineering, TH Köln, 50679 Cologne, Germany
Eberhard Waffenschmidt: Cologne Institute for Renewable Energies (CIRE) & Institute for Electrical Power Engineering, TH Köln, 50679 Cologne, Germany
Ingo Stadler: Cologne Institute for Renewable Energies (CIRE) & Institute for Electrical Power Engineering, TH Köln, 50679 Cologne, Germany

Energies, 2024, vol. 17, issue 21, 1-19

Abstract: In the transition from traditional electrical energy generation with mainly linear sources to increasing inverter-based distributed generation, electrical power systems’ power quality requires new monitoring methods. Integrating a high penetration of distributed generation, which is typically located in medium- or low-voltage grids, shifts the monitoring tasks from the transmission to distribution layers. Compared to high-voltage grids, distribution grids feature a higher level of complexity. Monitoring all relevant nodes is operationally infeasible and costly. State estimation methods provide knowledge about unmeasured locations 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 1 to 20.

Keywords: harmonic state estimation; physics-aware neural networks; pruned artificial neural network; power quality state estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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