Version 1
: Received: 30 September 2024 / Approved: 1 October 2024 / Online: 1 October 2024 (09:40:34 CEST)
How to cite:
Mack, P.; de Koster, M.; Lehnen, P.; Waffenschmidt, E.; Stadler, I. Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks - Harmonic State Estimation. Preprints2024, 2024100033. https://doi.org/10.20944/preprints202410.0033.v1
Mack, P.; de Koster, M.; Lehnen, P.; Waffenschmidt, E.; Stadler, I. Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks - Harmonic State Estimation. Preprints 2024, 2024100033. https://doi.org/10.20944/preprints202410.0033.v1
Mack, P.; de Koster, M.; Lehnen, P.; Waffenschmidt, E.; Stadler, I. Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks - Harmonic State Estimation. Preprints2024, 2024100033. https://doi.org/10.20944/preprints202410.0033.v1
APA Style
Mack, P., de Koster, M., Lehnen, P., Waffenschmidt, E., & Stadler, I. (2024). Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks - Harmonic State Estimation. Preprints. https://doi.org/10.20944/preprints202410.0033.v1
Chicago/Turabian Style
Mack, P., Eberhard Waffenschmidt and Ingo Stadler. 2024 "Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks - Harmonic State Estimation" Preprints. https://doi.org/10.20944/preprints202410.0033.v1
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 buses 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
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
