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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorGonzalez-Jimenez, David
dc.contributor.authordel-Olmo, Jon
dc.contributor.authorPoza, Javier
dc.contributor.authorGarramiola, Fernando
dc.contributor.authorMadina, Patxi
dc.date.accessioned2023-01-11T16:21:23Z
dc.date.available2023-01-11T16:21:23Z
dc.date.issued2023
dc.identifier.issn1424-8220en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=170548en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5940
dc.description.abstractLow-frequency oscillations (LFO) occur in railway electrification systems due to the incorporation of new trains with switching converters. As a result, the increased harmonic content can cause catenary stability problems under certain conditions. Most of the research published on this topic to date is focused on modelling the event and analysing it using frequency spectrums. However, in recent years, due to the new technologies linked to Big Data (BD) and data mining (DM), a new opportunity to study and detect LFO events by means of machine-learning (ML) methods has emerged. Trains continuously collect data from the most important catenary variables, which offers new resources for analysing this type of event. Therefore, this article presents the design and implementation of a data-driven LFO event detection strategy for AC railway network scenarios. Compared to previous investigations, a new approach to analyse and detect LFO events, based on field data and ML, is presented. To obtain the most appropriate detection approach for the context of this application, on the one hand, this investigation includes a comparison of machine-learning algorithms (support vector machine, logistic regression, random forest, k-nearest neighbours, naïve Bayes) which have been trained with real field data. On the other hand, an analysis of key parameters and features to optimize event detection is also included. Thus, the most significant result of this work is the high metric values of the solution, reaching values above 97% in accuracy and 93% in F-1 score with the random forest algorithm. In addition, the applicability and training of data-driven methods with real field data are demonstrated. This automatic detection strategy can help with speeding up and improving LFO detection tasks that used to be performed manually. Finally, it is worth mentioning that this research has been structured based on the CRISP-DM methodology, established as the de facto approach for industrial DM projects.en
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2022 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectfault detectionen
dc.subjectlow-frequency oscillationen
dc.subjectrailway catenaryen
dc.subjectCRISP-DMen
dc.subjectData miningen
dc.subjectmachine learningen
dc.subjectFault diagnosisen
dc.subjectfield dataseten
dc.subjectdata-drivenen
dc.titleData‐Driven Low‐Frequency Oscillation Event Detection Strategy for Railway Electrification Networksen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceSensorsen
local.contributor.groupAccionamientos aplicados a la tracción y a la generación de energía eléctricaes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/s23010254en
local.source.detailsVol. 23. N. 1. 254en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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