Discerning dynamics in synchrophasor data using topological data analysis
•Formulated discovering dynamics in ambient data as detecting prominent topological features in spectrum data.•Derived a statistical threshold for sub-level set persistence diagrams.•The threshold accounts for estimation noise and is compared to state of the art.•Applied method to real-world synchro...
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| Veröffentlicht in: | International journal of electrical power & energy systems Jg. 170; S. 110916 |
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| Sprache: | Englisch |
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01.09.2025
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| ISSN: | 0142-0615 |
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| Abstract | •Formulated discovering dynamics in ambient data as detecting prominent topological features in spectrum data.•Derived a statistical threshold for sub-level set persistence diagrams.•The threshold accounts for estimation noise and is compared to state of the art.•Applied method to real-world synchrophasor data from Dominion Energy’s power grid.
This paper explores the application of topological data analysis (TDA) for capturing relevant dynamic behavior (modes) in ambient synchrophasor data. In frequency domain, dominant dynamics correspond to prominent spectral peaks, which persist under a specific choice of continuous deformation to the spectral content and therefore, can be treated as topological features. Owing to the stochastic nature of the ambient data, there is still a need to threshold the said features to capture the most prominent system dynamics, which is the subject explored in this work. |
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| AbstractList | This paper explores the application of topological data analysis (TDA) for capturing relevant dynamic behavior (modes) in ambient synchrophasor data. In frequency domain, dominant dynamics correspond to prominent spectral peaks, which persist under a specific choice of continuous deformation to the spectral content and therefore, can be treated as topological features. Owing to the stochastic nature of the ambient data, there is still a need to threshold the said features to capture the most prominent system dynamics, which is the subject explored in this work. •Formulated discovering dynamics in ambient data as detecting prominent topological features in spectrum data.•Derived a statistical threshold for sub-level set persistence diagrams.•The threshold accounts for estimation noise and is compared to state of the art.•Applied method to real-world synchrophasor data from Dominion Energy’s power grid. This paper explores the application of topological data analysis (TDA) for capturing relevant dynamic behavior (modes) in ambient synchrophasor data. In frequency domain, dominant dynamics correspond to prominent spectral peaks, which persist under a specific choice of continuous deformation to the spectral content and therefore, can be treated as topological features. Owing to the stochastic nature of the ambient data, there is still a need to threshold the said features to capture the most prominent system dynamics, which is the subject explored in this work. |
| ArticleNumber | 110916 |
| Author | Vanfretti, Luigi Mishra, Chetan Jones, Kevin D. Delaree, Jaime |
| Author_xml | – sequence: 1 givenname: Chetan orcidid: 0000-0002-8667-6778 surname: Mishra fullname: Mishra, Chetan email: chetan31@vt.edu organization: Engineering Analytics and Modeling, Dominion Energy, Glen Allen, VA 23060, USA – sequence: 2 givenname: Luigi surname: Vanfretti fullname: Vanfretti, Luigi organization: Dept. of ECSE, Rensselaer Polytechnic Institute, Troy, NY 12180, USA – sequence: 3 givenname: Jaime surname: Delaree fullname: Delaree, Jaime organization: Engineering Analytics and Modeling, Dominion Energy, Glen Allen, VA 23060, USA – sequence: 4 givenname: Kevin D. surname: Jones fullname: Jones, Kevin D. organization: Engineering Analytics and Modeling, Dominion Energy, Glen Allen, VA 23060, USA |
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| Cites_doi | 10.1109/TPWRS.2012.2227840 10.1109/MSCPES55116.2022.9770146 10.1109/PESGM.2015.7286192 10.1109/TPWRS.2015.2456919 10.1109/TPWRS.2024.3350377 10.1109/ISGT50606.2022.9882712 10.1109/TSG.2016.2608965 10.1109/59.780909 10.1109/ISCAS.2008.4542044 10.1002/etep.1847 10.1109/PESGM52003.2023.10252802 10.1080/07474939608800344 10.1016/j.segan.2025.101735 10.1109/EEEIC.2015.7165462 10.1109/59.630467 10.1109/GridEdge54130.2023.10102706 10.1109/TPWRS.2016.2521319 10.1080/15325008.2015.1101727 10.1109/BlackSeaCom.2013.6623413 10.1016/j.ijepes.2020.106685 10.1109/59.49089 10.1214/14-AOS1252 10.1088/0964-1726/10/3/303 10.1109/PESGM48719.2022.9917070 10.1109/TPWRS.2018.2870838 10.1109/AMPS.2012.6344015 |
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| Keywords | Synchrophasor Topological data analysis Spectral analysis |
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| Title | Discerning dynamics in synchrophasor data using topological data analysis |
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