Article Dans Une Revue Electric Power Systems Research Année : 2025

Topological Data Analysis for fault classification on transmission lines

Résumé

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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Dates et versions

hal-05263230 , version 1 (21-10-2025)

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Eloi Gravot, Sergio Torregrosa, Nicolas Hascoët, Xavier Kestelyn, Francisco Chinesta. Topological Data Analysis for fault classification on transmission lines. Electric Power Systems Research, 2025, 248, pp.111915. ⟨10.1016/j.epsr.2025.111915⟩. ⟨hal-05263230⟩
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