Fault Detection and Normal Operating Condition in Power Transformers via Pattern Recognition Artificial Neural Network

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorScience and Technology (IFSP)-
Autor(es): dc.creatorGifalli, André-
Autor(es): dc.creatorBonini Neto, Alfredo-
Autor(es): dc.creatorde Souza, André Nunes-
Autor(es): dc.creatorde Mello, Renan Pinal-
Autor(es): dc.creatorIkeshoji, Marco Akio-
Autor(es): dc.creatorGarbelini, Enio-
Autor(es): dc.creatorNeto, Floriano Torres-
Data de aceite: dc.date.accessioned2025-08-21T18:08:57Z-
Data de disponibilização: dc.date.available2025-08-21T18:08:57Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/asi7030041-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/298999-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/298999-
Descrição: dc.descriptionAging, degradation, or damage to internal insulation materials often contribute to transformer failures. Furthermore, combustible gases can be produced when these insulation materials experience thermal or electrical stresses. This paper presents an artificial neural network for pattern recognition (PRN) to classify the operating conditions of power transformers (normal, thermal faults, and electrical faults) depending on the combustible gases present in them. Two network configurations were presented, one with five and the other with ten neurons in the hidden layer. The main advantage of applying this model through artificial neural networks is its ability to capture the nonlinear characteristics of the samples under study, thus avoiding the need for iterative procedures. The effectiveness and applicability of the proposed methodology were evaluated on 815 real data samples. Based on the results, the PRN performed well in both training and validation (for samples that were not part of the training), with a mean squared error (MSE) close to expected (0.001). The network was able to classify the samples with a 98% accuracy rate of the 815 samples presented and with 100% accuracy in validation, showing that the methodology developed is capable of acting as a tool for diagnosing the operability of power transformers.-
Descrição: dc.descriptionSchool of Engineering São Paulo State University (UNESP), SP-
Descrição: dc.descriptionSchool of Sciences and Engineering São Paulo State University (UNESP), SP-
Descrição: dc.descriptionFederal Institute of Education Science and Technology (IFSP), SP-
Descrição: dc.descriptionSchool of Engineering São Paulo State University (UNESP), SP-
Descrição: dc.descriptionSchool of Sciences and Engineering São Paulo State University (UNESP), SP-
Idioma: dc.languageen-
Relação: dc.relationApplied System Innovation-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial intelligence-
Palavras-chave: dc.subjectclassification-
Palavras-chave: dc.subjectdissolved gas analysis (DGA)-
Palavras-chave: dc.subjectpower transformers-
Título: dc.titleFault Detection and Normal Operating Condition in Power Transformers via Pattern Recognition Artificial Neural Network-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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