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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Bibliographic Details
Published in:Electric power systems research Vol. 248; p. 111915
Main Authors: Gravot, Eloi, Torregrosa, Sergio, Hascoët, Nicolas, Kestelyn, Xavier, Chinesta, Francisco
Format: Journal Article
Language:English
Published: Elsevier B.V 01.11.2025
Elsevier
Subjects:
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
Online Access:Get full text
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Summary: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