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Wavelet group method of data handling for fault prediction in electrical power insulators

2020-12Journal article Restricted access
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dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.authorDal Molin Ribeiro, Matheus Henrique
dc.contributor.authorNied, Ademir
dc.contributor.authorMariani, Viviana Cocco
dc.contributor.authorCoelho, Leandro dos Santos
dc.contributor.authorMenegat da Rocha, Diovana Fatima
dc.contributor.authorGrebogi, Rafael Bartnik
dc.contributor.authorRuano, Antonio
dc.date.accessioned2021-06-18T16:25:45Z
dc.date.available2021-06-18T16:25:45Z
dc.date.issued2020-12
dc.description.abstractElectric power is increasingly being used in the globalized day-to-day and keeping the electric power system running is necessary. Insulators are important components of the electric power system. In case of failure in these components, there may be disconnections and, consequently, no electricity. Contaminated insulators can develop irreversible failures if they are not inspected. One equipment used for the inspection of the electric power system is the ultrasound, which generates an audible noise based on a time series that is used to identify possible failures. the time series forecast can be used for possible prediction of the development of failure. In this paper, a hybrid method that uses Wavelet Energy Coefficient (WEC) for feature extraction and Group Method of Data Handling (GMDH) for time series prediction is proposed, being defined as Wavelet GMDH. For comparison and validation of the proposed method, a benchmark is made with well-established algorithms such as Long Short-Term Memory (LSTM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). For a fairer analysis, these algorithms are also evaluated based on the same data extraction with WEC. the proposed method proved to have good accuracy comparing with LSTM and ANFIS, and is much faster than the compared methods.
dc.description.sponsorshipCoordination for the Improvement of Higher Education Personnel (CAPES)
dc.description.sponsorshipNational Council of Scientific and Technologic Development of Brazil -(CNPq) [307958/2019-1-PQ, 307966/2019-4-PQ, GS2404659/2016-0-Univ, GS2405101/2016-3-Univ]
dc.description.sponsorshipPRONEX 'Fundacao Araucaria'Fundacao Araucaria [042/2018]
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1016/j.ijepes.2020.106269
dc.identifier.issn0142-0615
dc.identifier.urihttp://hdl.handle.net/10400.1/15664
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.subjectGroup method of data handling
dc.subjectWavelet transform
dc.subjectElectric power system
dc.subject.otherEngineering
dc.titleWavelet group method of data handling for fault prediction in electrical power insulators
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/157684/PT
oaire.citation.startPage106269
oaire.citation.titleInternational Journal of Electrical Power & Energy Systems
oaire.citation.volume123
oaire.fundingStream6817 - DCRRNI ID
person.familyNameRuano
person.givenNameAntonio
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccess
rcaap.typearticle
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2
relation.isProjectOfPublicationbd1b0ac0-d3d9-4f4b-b325-fdd17869253b
relation.isProjectOfPublication.latestForDiscoverybd1b0ac0-d3d9-4f4b-b325-fdd17869253b

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