A hybrid framework for assessing regional inertia estimation in bulk power systems using COI-driven spectral clustering
Modern power systems are facing growing challenges in frequency stability due to the increasing integration of renewable energy sources. Accurate regional inertia estimation is essential to address this issue. This paper proposes a novel grid partitioning method that enhances spectral clustering to...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525004776 |
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| author | Alexander Sanchez-Ocampo Mario R. Arrieta Paternina Juan M. Ramirez Lucas L. Fernandes Alejandro Zamora-Mendez Petr Korba Miguel Ramirez-Gonzalez |
| author_facet | Alexander Sanchez-Ocampo Mario R. Arrieta Paternina Juan M. Ramirez Lucas L. Fernandes Alejandro Zamora-Mendez Petr Korba Miguel Ramirez-Gonzalez |
| author_sort | Alexander Sanchez-Ocampo |
| collection | DOAJ |
| description | Modern power systems are facing growing challenges in frequency stability due to the increasing integration of renewable energy sources. Accurate regional inertia estimation is essential to address this issue. This paper proposes a novel grid partitioning method that enhances spectral clustering to identify regional centres of inertia (RCOIs) using complex network analysis. These RCOIs enable precise inertia estimation through an auto-regressive moving average (ARMAX) model, which combines data-driven and model-based approaches to achieve this. Validated on the NETS-NYPS test system under conventional and renewable generation scenarios and on the NPCC test system under traditional generation, the method achieves estimation errors below 3%, with renewable-specific accuracy nearing 10%. The framework offers a robust solution for monitoring regional inertia, thereby enhancing grid stability, operational efficiency, and resilience in dynamic power systems. |
| format | Article |
| id | doaj-art-2513d357e90c499cbca64ddfea0d3590 |
| institution | DOAJ |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-2513d357e90c499cbca64ddfea0d35902025-08-20T03:03:46ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-09-0117011092910.1016/j.ijepes.2025.110929A hybrid framework for assessing regional inertia estimation in bulk power systems using COI-driven spectral clusteringAlexander Sanchez-Ocampo0Mario R. Arrieta Paternina1Juan M. Ramirez2Lucas L. Fernandes3Alejandro Zamora-Mendez4Petr Korba5Miguel Ramirez-Gonzalez6Centre for Research and Advanced Studies, Guadalajara 45019 Jal, MexicoNational Autonomous University of Mexico, Mexico City 04510 Mex, MexicoCentre for Research and Advanced Studies, Guadalajara 45019 Jal, MexicoUniversity of Campinas, Campinas, SP 13083-852, BrazilMichoacan State University, Morelia 58030 Mich, MexicoZurich University of Applied Sciences, 8401 Winterthur, SwitzerlandZurich University of Applied Sciences, 8401 Winterthur, SwitzerlandModern power systems are facing growing challenges in frequency stability due to the increasing integration of renewable energy sources. Accurate regional inertia estimation is essential to address this issue. This paper proposes a novel grid partitioning method that enhances spectral clustering to identify regional centres of inertia (RCOIs) using complex network analysis. These RCOIs enable precise inertia estimation through an auto-regressive moving average (ARMAX) model, which combines data-driven and model-based approaches to achieve this. Validated on the NETS-NYPS test system under conventional and renewable generation scenarios and on the NPCC test system under traditional generation, the method achieves estimation errors below 3%, with renewable-specific accuracy nearing 10%. The framework offers a robust solution for monitoring regional inertia, thereby enhancing grid stability, operational efficiency, and resilience in dynamic power systems.http://www.sciencedirect.com/science/article/pii/S0142061525004776Regional inertia estimationClusteringCentre of inertiaWind turbineComplex networks |
| spellingShingle | Alexander Sanchez-Ocampo Mario R. Arrieta Paternina Juan M. Ramirez Lucas L. Fernandes Alejandro Zamora-Mendez Petr Korba Miguel Ramirez-Gonzalez A hybrid framework for assessing regional inertia estimation in bulk power systems using COI-driven spectral clustering International Journal of Electrical Power & Energy Systems Regional inertia estimation Clustering Centre of inertia Wind turbine Complex networks |
| title | A hybrid framework for assessing regional inertia estimation in bulk power systems using COI-driven spectral clustering |
| title_full | A hybrid framework for assessing regional inertia estimation in bulk power systems using COI-driven spectral clustering |
| title_fullStr | A hybrid framework for assessing regional inertia estimation in bulk power systems using COI-driven spectral clustering |
| title_full_unstemmed | A hybrid framework for assessing regional inertia estimation in bulk power systems using COI-driven spectral clustering |
| title_short | A hybrid framework for assessing regional inertia estimation in bulk power systems using COI-driven spectral clustering |
| title_sort | hybrid framework for assessing regional inertia estimation in bulk power systems using coi driven spectral clustering |
| topic | Regional inertia estimation Clustering Centre of inertia Wind turbine Complex networks |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525004776 |
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