Fault Classification in Transmission Lines Using Random Forest and Notch Filter

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorFonseca, Gabriel A.-
Autor(es): dc.creatorFerreira, Danton D.-
Autor(es): dc.creatorCosta, Flávio B.-
Autor(es): dc.creatorAlmeida, Aryfrance R.-
Data de aceite: dc.date.accessioned2026-02-09T11:53:49Z-
Data de disponibilização: dc.date.available2026-02-09T11:53:49Z-
Data de envio: dc.date.issued2022-04-12-
Data de envio: dc.date.issued2022-04-12-
Data de envio: dc.date.issued2021-10-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/49743-
Fonte completa do material: dc.identifierhttps://doi.org/10.1007/s40313-021-00844-4-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1149843-
Descrição: dc.descriptionOverhead energy transmission lines are highly susceptible to failure. To deal with this problem, some researchers have proposed different preprocessing stages, which comprise mainly feature extraction, selection, and dimension reduction for fault classification in transmission lines. The common techniques applied in the preprocessing stage are the wavelet and Fourier transforms. For the classification stage, the most used method is artificial neural network. This work aims to show the use of random forest method with a simple preprocessing step based on notch filter to classify faults in transmission lines. The performance of the model was compared with that obtained by a neural network to show its efficiency. Using k-fold cross-validation to train, test, and compare the models, it was obtained the mean accuracy of 89.59% for the neural network and 91.96% for the random forest for testing data. In the validation process, it was obtained accuracy of 96.49% and 91.49% for neural network and random forest models, respectively. Although the neural network model has shown better generalization capacity, the random forest model performed about eight times faster than the neural network.-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer Nature-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceJournal of Control, Automation and Electrical Systems-
Palavras-chave: dc.subjectRandom forest-
Palavras-chave: dc.subjectArtificial neural networks-
Palavras-chave: dc.subjectNotch filter-
Palavras-chave: dc.subjectTransmission lines-
Palavras-chave: dc.subjectFault classification-
Palavras-chave: dc.subjectCross-validation-
Palavras-chave: dc.subjectFloresta Aleatória-
Palavras-chave: dc.subjectRedes neurais artificiais-
Palavras-chave: dc.subjectLinhas de transmissão-
Palavras-chave: dc.subjectValidação cruzada-
Título: dc.titleFault Classification in Transmission Lines Using Random Forest and Notch Filter-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

Não existem arquivos associados a este item.