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Ponencias en congresos:
Comparison of artificial neural network and multiple regression for partial discharge sources recognition
Año:2017
Áreas de investigación
  • Ingeniería eléctrica
Datos
Descripción
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.
Internacional
Si
Nombre congreso
9th IEEE GCC 2017
Tipo de participación
960
Lugar del congreso
Dubai, Emiratos Árabes Unidos
Revisores
Si
ISBN o ISSN
978-1-5386-2756-3
DOI
10.1109/IEEEGCC.2017.8448033
Fecha inicio congreso
09/05/2017
Fecha fin congreso
11/05/2017
Desde la página
1
Hasta la página
9
Título de las actas
2017 9th IEEE-GCC Conference and Exhibition (GCCCE)
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Abdullahi A. Mas'ud (Jubail Industrial College)
  • Autor: Firdaus Muhammad-Sukki (Robert Gordon University)
  • Autor: Ricardo Albarracin Sanchez (UPM)
  • Autor: Jorge A. Ardila-Rey (UTFSM)
  • Autor: Siti Hawa Abu-Baka
  • Autor: Nur Fadilah Ab Aziz
  • Autor: Nurul Aini Bani
  • Autor: Mohd Nabil Muhtazaruddin
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  • Creador: Departamento: Ingeniería Eléctrica, Electrónica Automática y Física Aplicada
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