Denoising Autoencoder for Partial Discharge Identification in Instrument Transformers

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorSpecialized Maintenance Center-
Autor(es): dc.contributorHigh Voltage Equipments - Hvex-
Autor(es): dc.creatorCrivelaro, Matheus Goes-
Autor(es): dc.creatorRodrigues, Douglas-
Autor(es): dc.creatorGifalli, André-
Autor(es): dc.creatorPapa, João Paulo-
Autor(es): dc.creatorGonzales, Carlos Guilherme-
Autor(es): dc.creatorDe Souza, André Nunes-
Autor(es): dc.creatorDa Silva, Gustavo Vinícius-
Autor(es): dc.creatorNeto, Erasmo Silveira-
Data de aceite: dc.date.accessioned2025-08-21T17:37:59Z-
Data de disponibilização: dc.date.available2025-08-21T17:37:59Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/IWSSIP62407.2024.10634022-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309543-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309543-
Descrição: dc.descriptionAnalyzing Partial Discharge (PD) signals is crucial to assessing the health of insulation in high-voltage systems. Nevertheless, noise often distorts these signals, hindering the ability to obtain precise information. This paper proposes a novel deep-learning approach using two denoising autoencoders (DAEs) to learn data representations and eliminate noise during reconstruction. By leveraging DAEs' capacity to capture essential features within the latent space, this method enhances the analysis of PD signals and yields more accurate results. This paper investigates the effectiveness of two deep-learning architectures for denoising partial discharge signals in high-voltage insulation systems. Experimental results carried out on a PD dataset demonstrated the efficiency of the Linear AE model in removing noise in sets A, B, and C suggesting that DAEs hold great promise in PD signal denoising.-
Descrição: dc.descriptionSão Paulo State University Department of Electrical Engineering-
Descrição: dc.descriptionSão Paulo State University Department of Computing-
Descrição: dc.descriptionIsa Cteep Specialized Maintenance Center-
Descrição: dc.descriptionHigh Voltage Equipments - Hvex Department of Electrical Engineering-
Descrição: dc.descriptionSão Paulo State University Department of Electrical Engineering-
Descrição: dc.descriptionSão Paulo State University Department of Computing-
Idioma: dc.languageen-
Relação: dc.relationInternational Conference on Systems, Signals, and Image Processing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectdenoising autoencoder-
Palavras-chave: dc.subjectpartial discharge signal-
Título: dc.titleDenoising Autoencoder for Partial Discharge Identification in Instrument Transformers-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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