Autor(es)

Crivelaro, Matheus Goes ; Rodrigues, Douglas ; Gifalli, Andre ; Papa, Joao Paulo ; Gonzales, Carlos Guilherme ; de Souza, Andre Nunes ; da Silva, Gustavo Vinicius ; Silveira Neto, Erasmo

• Tipo de documento: Artigo Científico
• Fonte: 2024 31ST INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING, IWSSIP 2024; v. N/A, p. 7-pg., 2024-01-01.

Resumo

Analyzing 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. (AU)

• Processo FAPESP: 23/03726-4 - Estudo e Desenvolvimento de Algoritmos Multimétodo Multiobjetivos
• Beneficiário: Douglas Rodrigues
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa Aplicada
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 23/14427-8 - Ciência de Dados para a Indústria Inteligente (CDII)
• Beneficiário: José Alberto Cuminato
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada