@inproceedings { , title = {Comparison of artificial neural network and multiple regression for partial discharge sources recognition.}, 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.}, conference = {9th IEEE-GCC conference and exhibition 2017 (GCCCE 2017)}, doi = {10.1109/IEEEGCC.2017.8448033}, eissn = {2473-9391}, isbn = {9781538627563}, note = {COMPLETED -- Now on IEEExplore 6/9/2018 LM -- Still not on IEEExplore 21/8/2018, 23/7/2018, 2/7/2018, 25/5/2018, 14/5/2018, 26/3/2018, 26/2/2018, 26/1/2018 LM -- Check for final publication in IEEEXplore 13/11/2017 LM -- Info via contact 13/11/2017 LM ADDITIONAL INFORMATION: Muhammad-Sukki, Firdaus}, pages = {519-522}, publicationstatus = {Published}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, url = {http://hdl.handle.net/10059/2671}, keyword = {Partial discharge, Regression analysis, Artificial neural network}, year = {2018}, author = {Mas'ud, Abdullahi Abubakar and Muhammad-Sukki, Firdaus and Albarracín, Ricardo and Ardila-Rey, Jorge Alfredo and Abu-Bakar, Siti Hawa and Aziz, Nur Fadilah Ab and Bani, Nurul Aini and Muhtazaruddin, Mohd Nabil} }