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Comparison of artificial neural network and multiple regression for partial discharge sources recognition.

Mas'ud, Abdullahi Abubakar; Muhammad-Sukki, Firdaus; Albarrac�n, Ricardo; Ardila-Rey, Jorge Alfredo; Abu-Bakar, Siti Hawa; Aziz, Nur Fadilah Ab; Bani, Nurul Aini; Muhtazaruddin, Mohd Nabil


Abdullahi Abubakar Mas'ud

Firdaus Muhammad-Sukki

Ricardo Albarrac�n

Jorge Alfredo Ardila-Rey

Siti Hawa Abu-Bakar

Nur Fadilah Ab Aziz

Nurul Aini Bani

Mohd Nabil Muhtazaruddin


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.


MAS'UD, A.A., MUHAMMAD-SUKKI, F., ALBARRACIN, R., ARDILA-REY, J.A., ABU-BAKAR, S.H., AZIZ, N.F.A., BANI, N.A. and MUHTAZARUDDIN, M.N. 2017. Comparison of artificial neural network and multiple regression for partial discharge sources recognition. In Proceedings of the 9th IEEE-GCC conference and exhibition 2017 (GCCCE 2017), 8-11 May 2017, Manama, Bahrain. New York: IEEE [online], article number 8448033, pages 519-522. Available from:

Conference Name 9th IEEE-GCC conference and exhibition 2017 (GCCCE 2017)
Conference Location Manama, Bahrain
Start Date May 8, 2017
End Date May 11, 2017
Acceptance Date May 31, 2016
Online Publication Date May 8, 2017
Publication Date Aug 31, 2018
Deposit Date Jan 16, 2018
Publicly Available Date Jan 16, 2018
Electronic ISSN 2473-9391
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Article Number 8448033
Pages 519-522
Series Title Proceedings of the IEEE GCC conference and exhibition
Series ISSN 2473-9391
ISBN 9781538627563
Keywords Partial discharge; Regression analysis; Artificial neural network
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