Research Output
Comparison of artificial neural network and multiple regression for partial discharge sources recognition
  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 a several PD measurement 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.

  • Date:

    30 August 2018

  • Publication Status:

    Published

  • Publisher

    Institute of Electrical and Electronics Engineers

  • DOI:

    10.1109/IEEEGCC.2017.8448033

  • Funders:

    New Funder

Citation

Mas'ud, A. A., Muhammad-Sukki, F., Albarracín, R., Ardila-Rey, J. A., Abu-Bakar, S. H., Aziz, N. F. A., …Muhtazaruddin, M. N. (2018). Comparison of artificial neural network and multiple regression for partial discharge sources recognition. In 2017 9th IEEE-GCC Conference and Exhibition (GCCCE). , (519-522). https://doi.org/10.1109/IEEEGCC.2017.8448033

Authors

Keywords

Partial discharge, Regression analysis, Artificial neural network

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