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Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

2022-03-22Journal article Open access
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dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.authorBruns, Rafael
dc.contributor.authorSartori, Andreza
dc.contributor.authorMeyer, Luiz Henrique
dc.contributor.authorGarcia, Raul
dc.contributor.authorLEITHARDT, VALDERI
dc.date.accessioned2023-02-01T18:39:26ZPT
dc.date.available2023-02-01T18:39:26ZPT
dc.date.issued2022-03-22PT
dc.date.updated2022-04-04T10:55:56Z
dc.description.abstractOutdoor insulators may experience stress due to severe environmental conditions, such as pollution and contamination. Through the identification of partial discharges by ultrasonic noise, it is possible to assess the possibility of a power grid failure occurring. In this paper, ensemble models are used to analyze an ultrasonic signal from an ultrasonic microphone Pettersson M500. As the insulators are susceptible to developing irreversible failures, it will be evaluated whether the ultrasonic signal will remain over time, so that it is possible to assess whether the discharges being captured can result in a failure in contaminated polymeric insulators, evaluated in a high voltage laboratory under controlled conditions. The ensemble models were used in this paper because they typically require less computational effort than techniques based on deep learning and have acceptable performance for the problem at hand. The bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble models are evaluated, and the best result of each model is used to compare the differences between the models. The bagging ensemble learning model proved to be faster and have lower error than other ensemble models, long short-term memory (LSTM), and nonlinear autoregressive (NAR).pt_PT
dc.description.sponsorshipFS/27-2020
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/access.2022.3161506pt_PT
dc.identifier.issn2169-3536PT
dc.identifier.slugcv-prod-2974477
dc.identifier.urihttp://hdl.handle.net/10400.26/43563PT
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectDeep learning,pt_PT
dc.subjectmachine learning, ipt_PT
dc.subjectnsulator testingpt_PT
dc.titleAnalysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methodspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage33991pt_PT
oaire.citation.startPage33980pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume10pt_PT
person.familyNameStefenon
person.familyNameBruns
person.familyNameSartori
person.familyNameMeyer
person.familyNameGarcia
person.familyNameREIS QUIETINHO LEITHARDT
person.givenNameStefano Frizzo
person.givenNameRafael
person.givenNameAndreza
person.givenNameLuiz Henrique
person.givenNameRaul
person.givenNameVALDERI
person.identifier916543
person.identifierJsOq45sAAAAJ&hl=pt-PT
person.identifier.ciencia-id4019-BB36-7F74
person.identifier.ciencia-id0614-5834-E7F3
person.identifier.orcid0000-0002-3723-616X
person.identifier.orcid0000-0002-3414-0031
person.identifier.orcid0000-0002-3982-8767
person.identifier.orcid0000-0002-4849-4041
person.identifier.orcid0000-0002-7389-4696
person.identifier.orcid0000-0003-0446-9271
person.identifier.ridAAD-7639-2019
person.identifier.ridE-9303-2016
person.identifier.scopus-author-id57194147390
person.identifier.scopus-author-id35303109600
rcaap.cv.cienciaid0614-5834-E7F3 | Valderi Reis Quietinho Leithardt
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationae8b0861-1e25-47fb-bcdb-a44c98768634
relation.isAuthorOfPublicationa47d2c57-ea7a-4097-9e94-34087184c032
relation.isAuthorOfPublication9c24ac3c-b7ec-48e9-abb0-8f9485e3b4f1
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relation.isAuthorOfPublication.latestForDiscoveryf2b8d95b-8342-4067-9a99-08811cb97ee9

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