New Algorithm Applied to Transformers’ Failures Detection Based on Karhunen-Loève Transform

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorCastro, Bruno Albuquerque de-
Autor(es): dc.creatorBinotto, Amanda-
Autor(es): dc.creatorArdila-Rey, Jorge Alfredo-
Autor(es): dc.creatorFraga, Jose Renato Castro Pompeia-
Autor(es): dc.creatorSmith, Colin-
Autor(es): dc.creatorAndreoli, Andre Luiz-
Data de aceite: dc.date.accessioned2025-08-21T17:58:34Z-
Data de disponibilização: dc.date.available2025-08-21T17:58:34Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/TII.2023.3240590-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249039-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249039-
Descrição: dc.descriptionIndustry and science have been growing attention to developing systems that ensure the integrity of high voltage devices like power transformers. The goal is to avoid unexpected stoppages by detecting incipient failures before they become a major problem. In this context, the detection of discharge activity is an effective way to assess the condition operation of power transformers since this type of flaw can lead the transformer to total failure. The effectiveness of the fault diagnosis systems is related to their capability to distinguish the types of discharges since different flaws require different maintenance planning. This article proposes a new data analysis which combined the frequency spectrum of the signals with the Karhunen-Loève Transform to perform self-organization maps. The effectiveness of this analysis was validated by comparing it with the Fundamental Signals Properties Classification Technique, which is widely applied for pattern recognition.Two types of sensing techniques were assessed in order to enhance the capability of the new approach. Results indicated that the new methodology presented lower standard deviation for data classification, being a promising tool to monitoring systems.-
Descrição: dc.descriptionUniversidade Estadual Paulista, Sao Paulo, Brazil-
Descrição: dc.descriptionElectrical Engineering, Universidad Tecnica Federico santa Maria, Santiago de Chile, Chile-
Descrição: dc.descriptionR&D, IPEC Ltd, Manchester, UK-
Descrição: dc.descriptionElectrical Engineering, São Paulo State University, Bauru, Brazil-
Idioma: dc.languageen-
Relação: dc.relationIEEE Transactions on Industrial Informatics-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAcoustic emission-
Palavras-chave: dc.subjectDischarge-
Palavras-chave: dc.subjectDischarges (electric)-
Palavras-chave: dc.subjectDiscrete Fourier transforms-
Palavras-chave: dc.subjectHall effect-
Palavras-chave: dc.subjectInsulation-
Palavras-chave: dc.subjectInsulators-
Palavras-chave: dc.subjectPattern recognition-
Palavras-chave: dc.subjectPower transformer insulation-
Palavras-chave: dc.subjectSensors-
Palavras-chave: dc.subjectTransformers fault diagnosis-
Palavras-chave: dc.subjectWindings-
Título: dc.titleNew Algorithm Applied to Transformers’ Failures Detection Based on Karhunen-Loève Transform-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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