Topological Data Analysis for fault classification on transmission lines
This paper proposes a novel method for fault classification on transmission lines through a hybrid model combining Topological Data Analysis and unsupervised Machine Learning. Through persistent homology, signal topological signatures are extracted from each current’s phase and residual current. The...
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| Vydané v: | Electric power systems research Ročník 248; s. 111915 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.11.2025
Elsevier |
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| ISSN: | 0378-7796, 1873-2046 |
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| Abstract | This paper proposes a novel method for fault classification on transmission lines through a hybrid model combining Topological Data Analysis and unsupervised Machine Learning. Through persistent homology, signal topological signatures are extracted from each current’s phase and residual current. The spatial properties of the signatures are then fed to a K-means clustering algorithm for fault classification. The method produces accurate and consistent results across a variety of fault records, even when tested under diverse parameterized faults and noise intensities. To investigate further, the model is applied to field records of the French transmission operator RTE (Réseau de Transport d’Electricité) without any parametrization or prior training. The accuracy reflects the generalization abilities of the approach.
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•Topological Data Analysis for Transmission Line Fault Classification.•A simple and interpretable model with low resource requirements•Robust, tuning-free model for accurate real-time results on raw current data |
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| AbstractList | This paper proposes a novel method for fault classification on transmission lines through a hybrid model combining Topological Data Analysis and unsupervised Machine Learning. Through persistent homology, signal topological signatures are extracted from each current’s phase and residual current. The spatial properties of the signatures are then fed to a K-means clustering algorithm for fault classification. The method produces accurate and consistent results across a variety of fault records, even when tested under diverse parameterized faults and noise intensities. To investigate further, the model is applied to field records of the French transmission operator RTE (Réseau de Transport d’Electricité) without any parametrization or prior training. The accuracy reflects the generalization abilities of the approach. This paper proposes a novel method for fault classification on transmission lines through a hybrid model combining Topological Data Analysis and unsupervised Machine Learning. Through persistent homology, signal topological signatures are extracted from each current’s phase and residual current. The spatial properties of the signatures are then fed to a K-means clustering algorithm for fault classification. The method produces accurate and consistent results across a variety of fault records, even when tested under diverse parameterized faults and noise intensities. To investigate further, the model is applied to field records of the French transmission operator RTE (Réseau de Transport d’Electricité) without any parametrization or prior training. The accuracy reflects the generalization abilities of the approach. [Display omitted] •Topological Data Analysis for Transmission Line Fault Classification.•A simple and interpretable model with low resource requirements•Robust, tuning-free model for accurate real-time results on raw current data |
| ArticleNumber | 111915 |
| Author | Chinesta, Francisco Gravot, Eloi Torregrosa, Sergio Hascoët, Nicolas Kestelyn, Xavier |
| Author_xml | – sequence: 1 givenname: Eloi orcidid: 0009-0003-6162-7536 surname: Gravot fullname: Gravot, Eloi email: eloi.gravot@ensam.eu organization: Chimera RTE Chair @ PIMM Lab, Arts et Métiers Institute of Technology, 151 Bd de l’Hôpital, Paris, 75013, France – sequence: 2 givenname: Sergio surname: Torregrosa fullname: Torregrosa, Sergio email: sergio.torregrosa_jordan@ensam.eu organization: PIMM Lab, Arts et Métiers Institute of Technology, 151 Bd de l’Hôpital, Paris, 75013, France – sequence: 3 givenname: Nicolas surname: Hascoët fullname: Hascoët, Nicolas email: nicolas.hascoet@ensam.eu organization: Chimera RTE Chair @ PIMM Lab, Arts et Métiers Institute of Technology, 151 Bd de l’Hôpital, Paris, 75013, France – sequence: 4 givenname: Xavier surname: Kestelyn fullname: Kestelyn, Xavier email: xavier.kestelyn@ensam.eu organization: Chimera RTE Chair @ PIMM Lab, Arts et Métiers Institute of Technology, 151 Bd de l’Hôpital, Paris, 75013, France – sequence: 5 givenname: Francisco surname: Chinesta fullname: Chinesta, Francisco email: francisco.chinesta@ensam.eu organization: Chimera RTE Chair @ PIMM Lab, Arts et Métiers Institute of Technology, 151 Bd de l’Hôpital, Paris, 75013, France |
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| Cites_doi | 10.1016/j.epsr.2024.111143 10.1016/j.epsr.2024.110178 10.1109/TSG.2017.2672881 10.1016/j.epsr.2020.106437 10.1109/TPWRD.2011.2141157 10.1109/TPWRD.2006.876659 10.1016/j.epsr.2023.109526 10.21105/joss.00925 10.1109/TPWRD.2008.917919 10.1109/TII.2024.3359450 10.1016/j.epsr.2020.106914 10.1109/ACCESS.2020.2975431 10.3390/en13236447 10.3934/matersci.2020.4.364 10.1016/j.epsr.2022.108031 10.1016/j.neucom.2015.05.026 10.1109/TPAS.1982.317332 10.1007/s00202-011-0218-2 10.3390/e25111509 10.1049/iet-gtd.2013.0200 10.1016/j.apenergy.2023.120932 10.1109/TII.2022.3229497 10.1016/j.epsr.2020.106876 10.1109/JSEN.2022.3153647 |
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| Keywords | Signal processing Fault classification Transmission grid Machine Learning Topological Data Analysis Fault classification Signal processing Topological Data Analysis Transmission grid Machine Learning |
| Language | English |
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| SubjectTerms | Engineering Sciences Fault classification Machine Learning Signal processing Topological Data Analysis Transmission grid |
| Title | Topological Data Analysis for fault classification on transmission lines |
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