Autonomous neural models for the classification of events in power distribution networks
This paper presents a method for automatic classification of faults and transients in power distribution networks, based on voltage oscillographies of the distribution networks feeders. For signal preprocessing, the discrete wavelet transform was used with the performances of several families of wav...
| Principais autores: | Lazzaretti, Andre Eugênio, Ferreira, Vitor Hugo, Vieira Neto, Hugo, Riella, Rodrigo Jardim, Omori, Julio Shigeaki |
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| Formato: | Artigo |
| Idioma: | Português |
| Publicado em: |
Curitiba
2013
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http://repositorio.utfpr.edu.br/jspui/handle/1/654 http://dx.doi.org/10.1007/s40313-013-0064-8 |
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riut-1-6542015-03-07T06:12:20Z Autonomous neural models for the classification of events in power distribution networks Lazzaretti, Andre Eugênio Ferreira, Vitor Hugo Vieira Neto, Hugo Riella, Rodrigo Jardim Omori, Julio Shigeaki Energia elétrica - Distribuição Redes neurais (Computação) Visão por computador Wavelets (Matemática) ATP (Programa de computador) Electric power distribution Neural networks (Computer science) Computer vision Wavelets (Mathematics) ATP (Computer program) This paper presents a method for automatic classification of faults and transients in power distribution networks, based on voltage oscillographies of the distribution networks feeders. For signal preprocessing, the discrete wavelet transform was used with the performances of several families of wavelet functions being compared. In the classification stage, three neural models were assessed: multilayer perceptrons, radial basis function networks, and support vector machines. The models were trained autonomously, i.e., using automatic model selection and complexity control. Promising results were obtained using a set of simulations generated using the Alternative Transients Program (ATP). Initial results obtained for real data acquired from a set of oscillograph loggers installed in a distribution network are also presented. 5000 2013-11-21T21:30:08Z 2013-10 article LAZZARETTI, André Eugênio et al. Autonomous neural models for the classification of events in power distribution networks. Journal of Control, Automation and Electrical Systems, v. 24, n. 5, p. 612-622, out. 2013. Disponível em: <http://link.springer.com/article/10.1007%2Fs40313-013-0064-8>. Acesso em: 11 nov. 2013. 2195-3899 http://repositorio.utfpr.edu.br/jspui/handle/1/654 http://dx.doi.org/10.1007/s40313-013-0064-8 por Journal of Control, Automation and Electrical Systems http://link.springer.com/article/10.1007%2Fs40313-013-0064-8 application/pdf Curitiba |
| institution |
Universidade Tecnológica Federal do Paraná |
| collection |
RIUT |
| language |
Português |
| topic |
Energia elétrica - Distribuição Redes neurais (Computação) Visão por computador Wavelets (Matemática) ATP (Programa de computador) Electric power distribution Neural networks (Computer science) Computer vision Wavelets (Mathematics) ATP (Computer program) |
| spellingShingle |
Energia elétrica - Distribuição Redes neurais (Computação) Visão por computador Wavelets (Matemática) ATP (Programa de computador) Electric power distribution Neural networks (Computer science) Computer vision Wavelets (Mathematics) ATP (Computer program) Lazzaretti, Andre Eugênio Ferreira, Vitor Hugo Vieira Neto, Hugo Riella, Rodrigo Jardim Omori, Julio Shigeaki Autonomous neural models for the classification of events in power distribution networks |
| description |
This paper presents a method for automatic classification of faults and transients in power distribution networks, based on voltage oscillographies of the distribution networks feeders. For signal preprocessing, the discrete wavelet transform was used with the performances of several families of wavelet functions being compared. In the classification stage, three neural models were assessed: multilayer perceptrons, radial basis function networks, and support vector machines. The models were trained autonomously, i.e., using automatic model selection and complexity control. Promising results were obtained using a set of simulations generated using the Alternative Transients Program (ATP). Initial results obtained for real data acquired from a set of oscillograph loggers installed in a distribution network are also presented. |
| format |
Artigo |
| author |
Lazzaretti, Andre Eugênio Ferreira, Vitor Hugo Vieira Neto, Hugo Riella, Rodrigo Jardim Omori, Julio Shigeaki |
| author_sort |
Lazzaretti, Andre Eugênio |
| title |
Autonomous neural models for the classification of events in power distribution networks |
| title_short |
Autonomous neural models for the classification of events in power distribution networks |
| title_full |
Autonomous neural models for the classification of events in power distribution networks |
| title_fullStr |
Autonomous neural models for the classification of events in power distribution networks |
| title_full_unstemmed |
Autonomous neural models for the classification of events in power distribution networks |
| title_sort |
autonomous neural models for the classification of events in power distribution networks |
| publisher |
Curitiba |
| publishDate |
2013 |
| citation |
LAZZARETTI, André Eugênio et al. Autonomous neural models for the classification of events in power distribution networks. Journal of Control, Automation and Electrical Systems, v. 24, n. 5, p. 612-622, out. 2013. Disponível em: <http://link.springer.com/article/10.1007%2Fs40313-013-0064-8>. Acesso em: 11 nov. 2013. 2195-3899 |
| url |
http://repositorio.utfpr.edu.br/jspui/handle/1/654 http://dx.doi.org/10.1007/s40313-013-0064-8 |
| _version_ |
1805316674050588672 |
| score |
10,814766 |