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Off-line measuring sampling data identification parameters for digital twins mirroring load modelling and stability analysis
Currently, the methods used to represent loads do not differ between the characteristics that compose them or the nature of these. Therefore, the purpose of this research is to develop digital twins mirroring load models that can be used for more precise studies on power-flows and stability within t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10030858/ https://www.ncbi.nlm.nih.gov/pubmed/36944720 http://dx.doi.org/10.1038/s41598-023-31451-9 |
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author | Urquizo, Javier Ramirez, Nathalie Sanchez, Dietmar Plazarte, Juan |
author_facet | Urquizo, Javier Ramirez, Nathalie Sanchez, Dietmar Plazarte, Juan |
author_sort | Urquizo, Javier |
collection | PubMed |
description | Currently, the methods used to represent loads do not differ between the characteristics that compose them or the nature of these. Therefore, the purpose of this research is to develop digital twins mirroring load models that can be used for more precise studies on power-flows and stability within the National Transmission Grid (NTG). Off-line sampling data of different electric measurements have been used in six substations of the Guayaquil (Ecuador) area. These values were organized by statistical methods and by time periods, to determine the parameters that make up the static load model. Dynamic models are also constructed for the same six substations using the analysis of current and voltage signals obtained from the substations. All data is organized to show a digital twin mirroring visual representation of the disturbances that may occur in the substation buses. A more accurate description of the static and dynamic responses can be obtained by replacing the general model that is currently used by engineers and planners with off-line sampling data. Digital twins help the electric utility businesses gather, visualise, and contextualise data from different sources, and enable to act on data, and to understand what-if modelling stability scenarios. |
format | Online Article Text |
id | pubmed-10030858 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100308582023-03-23 Off-line measuring sampling data identification parameters for digital twins mirroring load modelling and stability analysis Urquizo, Javier Ramirez, Nathalie Sanchez, Dietmar Plazarte, Juan Sci Rep Article Currently, the methods used to represent loads do not differ between the characteristics that compose them or the nature of these. Therefore, the purpose of this research is to develop digital twins mirroring load models that can be used for more precise studies on power-flows and stability within the National Transmission Grid (NTG). Off-line sampling data of different electric measurements have been used in six substations of the Guayaquil (Ecuador) area. These values were organized by statistical methods and by time periods, to determine the parameters that make up the static load model. Dynamic models are also constructed for the same six substations using the analysis of current and voltage signals obtained from the substations. All data is organized to show a digital twin mirroring visual representation of the disturbances that may occur in the substation buses. A more accurate description of the static and dynamic responses can be obtained by replacing the general model that is currently used by engineers and planners with off-line sampling data. Digital twins help the electric utility businesses gather, visualise, and contextualise data from different sources, and enable to act on data, and to understand what-if modelling stability scenarios. Nature Publishing Group UK 2023-03-21 /pmc/articles/PMC10030858/ /pubmed/36944720 http://dx.doi.org/10.1038/s41598-023-31451-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Urquizo, Javier Ramirez, Nathalie Sanchez, Dietmar Plazarte, Juan Off-line measuring sampling data identification parameters for digital twins mirroring load modelling and stability analysis |
title | Off-line measuring sampling data identification parameters for digital twins mirroring load modelling and stability analysis |
title_full | Off-line measuring sampling data identification parameters for digital twins mirroring load modelling and stability analysis |
title_fullStr | Off-line measuring sampling data identification parameters for digital twins mirroring load modelling and stability analysis |
title_full_unstemmed | Off-line measuring sampling data identification parameters for digital twins mirroring load modelling and stability analysis |
title_short | Off-line measuring sampling data identification parameters for digital twins mirroring load modelling and stability analysis |
title_sort | off-line measuring sampling data identification parameters for digital twins mirroring load modelling and stability analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10030858/ https://www.ncbi.nlm.nih.gov/pubmed/36944720 http://dx.doi.org/10.1038/s41598-023-31451-9 |
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