Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/33591
metadata.artigo.dc.title: Real-time system for automatic detection and classification of single and multiple power quality disturbances
metadata.artigo.dc.creator: Ribeiro, Eduardo G.
Mendes, Thais M.
Dias, Guilherme L.
Faria, Euler R. S.
Viana, Felipe M.
Barbosa, Bruno H. G.
Ferreira, Danton D.
metadata.artigo.dc.subject: Higher-order statistics
Neuro-tree
Real-time
LabVIEW software
metadata.artigo.dc.publisher: Elsevier
metadata.artigo.dc.date.issued: Nov-2018
metadata.artigo.dc.identifier.citation: RIBEIRO, E. G. et al. Real-time system for automatic detection and classification of single and multiple power quality disturbances. Measurement, [S.l.], v. 128, p. 276-283, Nov. 2018.
metadata.artigo.dc.description.abstract: It is known that the quality of power has been the subject of several researches aiming to provide relevant information to users of electrical systems that are becoming increasingly smart. This study presents an approach for single and multiple power quality disturbance detection and classification using multidimensional analysis, higher-order statistics and a neuro-tree based classifier. The system was implemented in an FPGA (Field Programmable Gate Array), a real-time processor and a remote computer, with LabVIEW interface. This implementation enables real-time execution and its application to monitor smart grids. It is able to detect deviations in the measured voltage waveform from the nominal one and classify 20 classes of single and multiple disturbances with a global efficiency upper to 97%.
metadata.artigo.dc.identifier.uri: https://www.sciencedirect.com/science/article/pii/S0263224118305852
http://repositorio.ufla.br/jspui/handle/1/33591
metadata.artigo.dc.language: en_US
Appears in Collections:DAT - Artigos publicados em periódicos

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