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Descripción
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| This paper compares the capabilities of the artificial neural network (ANN) and multiple linear regression (MLR) for recognizing and discriminating partial discharge (PD) defects. Statistical fingerprints obtained from several PD measurements were applied for training and testing both the ANN and MLR. The result indicates that for both the ANN and MLR trained and tested with the same insulation defect, the ANN has better recognition capability. But, when both ANN and MLR were trained and tested with different PD defects, the MLR is generally more sensitive in discriminating them. In this paper, the results were evaluated for practical PD recognition and it shows that both of them can be used simultaneously for both online and offline PD detection. | |
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Internacional
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Si |
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Nombre congreso
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9th IEEE GCC 2017 |
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Tipo de participación
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960 |
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Lugar del congreso
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Dubai, Emiratos Árabes Unidos |
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Revisores
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Si |
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ISBN o ISSN
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978-1-5386-2756-3 |
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DOI
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10.1109/IEEEGCC.2017.8448033 |
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Fecha inicio congreso
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09/05/2017 |
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Fecha fin congreso
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11/05/2017 |
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Desde la página
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1 |
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Hasta la página
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9 |
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Título de las actas
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2017 9th IEEE-GCC Conference and Exhibition (GCCCE) |