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
Hauptverfasser: Mishra, Chetan, Vanfretti, Luigi, Delaree, Jaime, Jones, Kevin D.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2025
Elsevier
Schlagworte:
Spectral analysis
Synchrophasor
Topological data analysis
Synchrophasor
Topological data analysis
Spectral analysis
ISSN:0142-0615
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Zusammenfassung:•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.
ISSN:0142-0615
DOI:10.1016/j.ijepes.2025.110916