Return to search

Classifica??o de dist?rbios na rede el?trica usando redes neurais e wavelets

Made available in DSpace on 2014-12-17T14:54:49Z (GMT). No. of bitstreams: 1
CrisluciKSS.pdf: 1753956 bytes, checksum: 06fc893387f3832c2cc344c281169f6d (MD5)
Previous issue date: 2008-10-13 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in systems, looking
for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment
- techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbance s occurrences in the network. This work presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution
levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks / An?lises p?s-despacho de sinais oriundos de registradores de perturba??es fornecem muitas vezes informa??es importantes para identifica??o e classifica??o de dist?rbios nos sistemas, visando a uma gest?o mais eficiente do fornecimento de
energia el?trica. Para auxiliar nessa tarefa, faz-se necess?rio recorrer a t?cnicas de processamento de sinais, a fim de automatizar o diagn?stico sobre os tipos de dist?rbio presentes nos sinais registrados. A transformada wavelet constitui-se em uma ferramenta matem?tica bastante eficaz na an?lise de sinais de tens?o ou corrente, obtidos imediatamente ap?s a ocorr?ncia de dist?rbios na rede. Este
trabalho apresenta uma metodologia baseada na transformada wavelet discreta e na compara??o de curvas de distribui??o da energia de sinais, com e sem dist?rbio, para diferentes n?veis de resolu??o de sua decomposi??o, com o objetivo
de obter descritores que permitam a sua classifica??o atrav?s do uso de redes neurais artificiais

Identiferoai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/15119
Date13 October 2008
CreatorsSantos, Crisluci Karina Souza
ContributorsCPF:09615687472, http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4781378J1, Oliveira, Jos? Tavares de, CPF:05768632468, http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4780328Y8, Bezerra, Ubiratan Holanda, CPF:04230000200, http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4787768D6, Souza, Benemar Alencar de, CPF:13253298434, http://lattes.cnpq.br/4987294390789975, Melo, Jorge Dantas de, CPF:09463097449, http://lattes.cnpq.br/7325007451912598, Leit?o, Jos? J?lio de Almeida Lins, CPF:14700204400, http://lattes.cnpq.br/5560023605852197, Medeiros J?nior, Manoel Firmino de
PublisherUniversidade Federal do Rio Grande do Norte, Programa de P?s-Gradua??o em Engenharia El?trica, UFRN, BR, Automa??o e Sistemas; Engenharia de Computa??o; Telecomunica??es
Source SetsIBICT Brazilian ETDs
LanguagePortuguese
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis
Formatapplication/pdf
Sourcereponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN
Rightsinfo:eu-repo/semantics/openAccess

Page generated in 0.0026 seconds