Comparison of Artificial Neural Network and Multiple Regression for Partial Discharge Sources Recognition

Abdullahi Abubakar Masud,Mohd Nabil Muhtazaruddin,Ricardo Albarracin,Siti Hawa Abu-Bakar,Jorge Alfredo Ardila-Rev,Nurul Aini Bani,Firdaus Muhammad-Sukki,Nur Fadilah Ab Aziz

doi:10.1109/ieeegcc.2017.8448033

Comparison of Artificial Neural Network and Multiple Regression for Partial Discharge Sources Recognition

Abdullahi Abubakar Masud, Mohd Nabil Muhtazaruddin + Show 6 more

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https://doi.org/10.1109/ieeegcc.2017.8448033

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Publication Date: May 1, 2017

License type: cc-by-nc

Affiliation: Jubail Industrial College, University of Technology Malaysia, University of Kuala Lumpur, Federico Santa María Technical University, Robert Gordon University, Universiti Tenaga Nasional

#Partial Discharge Defects #Partial Discharge + Show 8 more

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Abstract

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.

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