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

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.

Citation

Mas'ud, A. A., Muhammad-Sukki, F., Albarracín, R., Ardila-Rey, J. A., Abu-Bakar, S. H., Aziz, N. F. A., Bani, N. A., & Muhtazaruddin, M. N. (2017, May). Comparison of artificial neural network and multiple regression for partial discharge sources recognition. Presented at 9th IEEE-GCC Conference and Exhibition 2017 (GCCCE 2017), Manama, Bahrain

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 9th IEEE-GCC Conference and Exhibition 2017 (GCCCE 2017)
• Start Date: May 8, 2017
• End Date: May 11, 2017
• Acceptance Date: May 31, 2016
• Online Publication Date: Aug 30, 2018
• Publication Date: 2018
• Deposit Date: Dec 1, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 519-522
• Series Title: Proceedings of the IEEE GCC conference and exhibition
• Series ISSN: 2473-9391
• Book Title: 2017 9th IEEE-GCC Conference and Exhibition (GCCCE)
• ISBN: 9781538627563
• DOI: https://doi.org/10.1109/IEEEGCC.2017.8448033
• Keywords: Partial discharge, Regression analysis, Artificial neural network
• Public URL: http://researchrepository.napier.ac.uk/Output/2703611
• Publisher URL: https://doi.org/10.1109/IEEEGCC.2017.8448033