dc.creator | Maia, Amanda Costa | |
dc.creator | Silva, Guilherme Sebastiao da | |
dc.date.accessioned | 2022-11-29T19:58:20Z | |
dc.date.available | 2022-11-29T19:58:20Z | |
dc.date.submitted | 2022-11-15 | |
dc.identifier.uri | http://repositorio.ufsm.br/handle/1/27150 | |
dc.language | por | por |
dc.relation.ispartof | SEPOC 2022 | por |
dc.rights | Acesso Aberto | por |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Artificial Neural Network | eng |
dc.subject | Classification | eng |
dc.subject | Defects | eng |
dc.subject | I-V Curve | eng |
dc.subject | Multilayer Perceptron | eng |
dc.title | A New Fault Diagnostic Strategy for Photovoltaic Arrays through Artificial Neural Network | por |
dc.type | Trabalho Publicado em Evento | por |
dc.description.resumo | Photovoltaic systems’ faults can cause a power reduction, the decrease in the useful life and irreversible damages on the photovoltaic cells. This paper presents a strategy able to diagnosticate the photovoltaic array fault according to the Brazilian regulatory standard NBR 16274. Therefore, the fault diagnostic is based on the PV array I-V curve which is used to calculate the inputs of a multilayer perceptron artificial neural network (ANN). The results shown in this paper demonstrate the ANN with a compact structure and good accuracy. Moreover, it shows the ANN can classify all faults of the NBR 16274 with 99.3% of accuracy https://doi.org/10.53316/sepoc2022.025 | por |
dc.publisher.country | Brasil | por |
dc.subject.cnpq | CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA | por |