Web-based wide-area monitoring platform for ringdown and clustering analytics in power systems

This paper introduces an open-source research platform for monitoring the Mexican interconnected power grid, allowing real-time processing and information extraction of the grid’s dynamic condition. Moreover, the platform is a Python-based development that embeds different ringdown and clustering analytics tools. In the case of ringdown analysis, the modal information can be extracted using some of the most known algorithms, i.e., Prony analysis, eigensystem realization algorithm (ERA), and matrix pencil (MP). For clustering analysis, the coherent behaviour of generator and non-generator buses is provided by applying recent state-of-the-art techniques such as affinity propagation, K-means, hierarchical agglomerative clustering, and typicality data analysis. The results of up to 93 PMUs show that this open-source platform suits researchers’ and engineers’ power system dynamic analysis requirements. • This platform is a web-based project with open access to PMUs and WAMS analytics. • The platform executes ringdown oscillations monitoring and analytics (ROMA). • The platform executes clustering analytics for power system dynamics (CAPS-D). • The time-synchronized Mexican WAMS is based on FDRs, PMUs, and OpenPDC©. • The back-end API works on Django and the front-end uses JavaScript, CSS and HTML.

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Publication Details

Journal
International Journal of Electrical Power & Energy Systems
Published
2025-06-09
DOI
https://doi.org/10.1016/j.ijepes.2025.110714
Citations
1
Primary Topic
Smart Grid and Power Systems
Type
article
Field-Weighted Citation Impact
0.73
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article

Web-based wide-area monitoring platform for ringdown and clustering analytics in power systems

Lucas L. Fernandes, Joe H. Chow, Alejandro Zamora‐Mendez, Daniel Dotta et al.
1 citations
International Journal of Electrical Power & Energy Systems
Smart Grid and Power Systems
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article

Web-based wide-area monitoring platform for ringdown and clustering analytics in power systems

Lucas L. Fernandes, Joe H. Chow, Alejandro Zamora‐Mendez, Daniel Dotta, José Zarate, Jose Ortiz‐Bejar, Mario R. Arrieta Paternina, José Antonio de la O Serna, José Manuel Ramos Guerrero, Garibaldi Pineda García, Francisco Zelaya-Arrazabal, Juan M. Ramı́rez, Yilu Liu, Carlos Miguel Tobar Toledo, R. Luna, Antonino López-Rios, Luis Mendieta-Mejia, Félix Reyes-Maldonado
article en
1 citations

Abstract

This paper introduces an open-source research platform for monitoring the Mexican interconnected power grid, allowing real-time processing and information extraction of the grid’s dynamic condition. Moreover, the platform is a Python-based development that embeds different ringdown and clustering analytics tools. In the case of ringdown analysis, the modal information can be extracted using some of the most known algorithms, i.e., Prony analysis, eigensystem realization algorithm (ERA), and matrix pencil (MP). For clustering analysis, the coherent behaviour of generator and non-generator buses is provided by applying recent state-of-the-art techniques such as affinity propagation, K-means, hierarchical agglomerative clustering, and typicality data analysis. The results of up to 93 PMUs show that this open-source platform suits researchers’ and engineers’ power system dynamic analysis requirements. • This platform is a web-based project with open access to PMUs and WAMS analytics. • The platform executes ringdown oscillations monitoring and analytics (ROMA). • The platform executes clustering analytics for power system dynamics (CAPS-D). • The time-synchronized Mexican WAMS is based on FDRs, PMUs, and OpenPDC©. • The back-end API works on Django and the front-end uses JavaScript, CSS and HTML.

International Journal of Electrical Power & Energy SystemsVol. 169
Universidad Industrial de Santander (CO), University of Sussex (GB), Rensselaer Polytechnic Institute (US), Universidad Autónoma de Nuevo León (MX), Universidad Michoacana de San Nicolás de Hidalgo (MX), Universidade Estadual de Campinas (UNICAMP) (BR), Universidad de Guadalajara (MX), University of Tennessee at Knoxville (US), Universidad Nacional Autónoma de México (MX)
Openalex Percentile: Top 27%
Smart Grid and Power Systems
0.73
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