ETDs @PUC-Rio
| Título: | SHORT-TERM HOURLY LOAD FORECASTING MODEL. A NEW APPROACH: HIBRID MODEL | ||||||||||||
| Autor: |
TOMAS HOSHIBA KAWABATA |
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| Colaborador(es): |
REINALDO CASTRO SOUZA - Orientador |
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| Catalogação: | 25/JUL/2002 | Língua(s): | PORTUGUESE - BRAZIL |
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| Tipo: | TEXT | Subtipo: | THESIS | ||||||||||
| Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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| Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=2773&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=2773&idi=2 |
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| DOI: | https://doi.org/10.17771/PUCRio.acad.2773 | ||||||||||||
| Resumo: | |||||||||||||
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When a kind of fault occurs in a Transmission Line, its
exact location is essential for a fast reclosing of the
Electric System. Methods that use voltages and currents
from only one terminal contain simplifications that can to
cause mistakes. This paper presents an investigation about
application of Artificial Neural Network (ANN) obtaining a
new way of identification for the type of the short circuit
and its location, using data obtained only in one terminal.
The work consists on the following 4 main parts:
bibliographical study of Neural Network`s area;
simulations of faults in order to obtain of patterns;
definition and implementation of Neural Network`s models
for identification and location of the fault; and studies
of cases.
In the bibliographical study step on ANN, it was verified
that the topologies for the more usual nets are Feed-
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