Publication: Comparison of artificial neural network and multiple regression for partial discharge sources recognition
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Date
2018
Authors
Abubakar Masud A.
Muhammad-Sukki F.
Albarracin R.
Alfredo Ardila-Rev J.
Hawa Abu-Bakar S.
Fadilah Ab Aziz N.
Bani N.A.
Nabil Muhtazaruddin M.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
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. � 2017 IEEE.
Description
Defects; Linear regression; Neural networks; Regression analysis; Insulation defects; Multiple linear regressions; Multiple regressions; Offline; Partial discharge sources; Pd detections; PD measurements; Training and testing; Partial discharges