| Autor: |
Andrea Bragantini, Andreas Sumper |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 27180-27198 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3366337 |
| Popis: |
Learning-based state estimators can represent a cost-effective opportunity for distribution system operators to perform grid monitoring and control in low-voltage grids where the measuring infrastructure is minimal, if not absent. This study lays the foundation for designing and evaluating neural network-based state estimators for low-voltage radial distribution grids. A simulation-based methodology is proposed for generating synthetic training data-sets relying only on minimal grid data. Additionally, a novel framework for performance analysis of low voltage learning-based state estimators is considered, which relies on a bi-dimensional evaluation of the absolute error and the parallel observation of relative metrics. The applicability and potential of these estimators have been tested and validated through various low-voltage radial case studies, showing promising results especially for large distribution grids. Finally, a propagation error study has been conducted to observe how these estimators handle errors in input measurements. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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