Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/49743
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dc.creatorFonseca, Gabriel A.-
dc.creatorFerreira, Danton D.-
dc.creatorCosta, Flávio B.-
dc.creatorAlmeida, Aryfrance R.-
dc.date.accessioned2022-04-12T22:30:33Z-
dc.date.available2022-04-12T22:30:33Z-
dc.date.issued2021-11-
dc.identifier.citationFONSECA, G. A. et al. Fault Classification in Transmission Lines Using Random Forest and Notch Filter. Journal of Control, Automation and Electrical Systems, [S. I.], v. 33, p. 598–609, Apr. 2022. DOI: https://doi.org/10.1007/s40313-021-00844-4.pt_BR
dc.identifier.urihttps://doi.org/10.1007/s40313-021-00844-4pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/49743-
dc.description.abstractOverhead 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.pt_BR
dc.languageenpt_BR
dc.publisherSpringer Naturept_BR
dc.rightsrestrictAccesspt_BR
dc.sourceJournal of Control, Automation and Electrical Systemspt_BR
dc.subjectRandom forestpt_BR
dc.subjectArtificial neural networkspt_BR
dc.subjectNotch filterpt_BR
dc.subjectTransmission linespt_BR
dc.subjectFault classificationpt_BR
dc.subjectCross-validationpt_BR
dc.subjectFloresta Aleatóriapt_BR
dc.subjectRedes neurais artificiaispt_BR
dc.subjectLinhas de transmissãopt_BR
dc.subjectValidação cruzadapt_BR
dc.titleFault Classification in Transmission Lines Using Random Forest and Notch Filterpt_BR
dc.typeArtigopt_BR
Appears in Collections:DAT - Artigos publicados em periódicos
DEG - Artigos publicados em periódicos

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