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
Hlavní autori: Gravot, Eloi, Torregrosa, Sergio, Hascoët, Nicolas, Kestelyn, Xavier, Chinesta, Francisco
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.11.2025
Elsevier
Predmet:
Engineering Sciences
Fault classification
Machine Learning
Signal processing
Topological Data Analysis
Transmission grid
Signal processing
Fault classification
Transmission grid
Machine Learning
Topological Data Analysis
Fault classification Signal processing Topological Data Analysis Transmission grid Machine Learning
ISSN:0378-7796, 1873-2046
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Shrnutí: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
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2025.111915