Data Generation for Stability Studies of Power Systems with High Penetration of Inverter-Based Resources
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918300887285760 |
|---|---|
| author | Rossi, Francesca Lorenzo, Mauro Garcia de Acevedo, Eduardo Iraola Barriendos, Elia Mateu Lacerda, Vinicius Albernaz Lordan-Gomis, Francesc Badia, Rosa Prieto-Araujo, Eduardo |
| author_facet | Rossi, Francesca Lorenzo, Mauro Garcia de Acevedo, Eduardo Iraola Barriendos, Elia Mateu Lacerda, Vinicius Albernaz Lordan-Gomis, Francesc Badia, Rosa Prieto-Araujo, Eduardo |
| contents | The increasing penetration of inverter-based resources (IBRs) is fundamentally reshaping power system dynamics and creating new challenges for stability assessment. Data-driven approaches, and in particular machine learning models, require large and representative datasets that capture how system stability varies across a wide range of operating conditions and control settings. This paper presents an open-source, high-performance computing framework for the systematic generation of such datasets. The proposed tool defines a scalable operating space for large-scale power systems, explores it through an adaptive sampling strategy guided by sensitivity analysis, and performs small-signal stability assessments to populate a high-information-content dataset. The framework efficiently targets regions near the stability margin while maintaining broad coverage of feasible operating conditions. The workflow is fully implemented in Python and designed for parallel execution. The resulting tool enables the creation of high-quality datasets that support data-driven stability studies in modern power systems with high IBR penetration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Generation for Stability Studies of Power Systems with High Penetration of Inverter-Based Resources Rossi, Francesca Lorenzo, Mauro Garcia de Acevedo, Eduardo Iraola Barriendos, Elia Mateu Lacerda, Vinicius Albernaz Lordan-Gomis, Francesc Badia, Rosa Prieto-Araujo, Eduardo Systems and Control The increasing penetration of inverter-based resources (IBRs) is fundamentally reshaping power system dynamics and creating new challenges for stability assessment. Data-driven approaches, and in particular machine learning models, require large and representative datasets that capture how system stability varies across a wide range of operating conditions and control settings. This paper presents an open-source, high-performance computing framework for the systematic generation of such datasets. The proposed tool defines a scalable operating space for large-scale power systems, explores it through an adaptive sampling strategy guided by sensitivity analysis, and performs small-signal stability assessments to populate a high-information-content dataset. The framework efficiently targets regions near the stability margin while maintaining broad coverage of feasible operating conditions. The workflow is fully implemented in Python and designed for parallel execution. The resulting tool enables the creation of high-quality datasets that support data-driven stability studies in modern power systems with high IBR penetration. |
| title | Data Generation for Stability Studies of Power Systems with High Penetration of Inverter-Based Resources |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.06369 |