Comparison of artificial neural network and multiple regression for partial discharge sources recognition
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
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-237252023-05-29T14:51:16Z Comparison of artificial neural network and multiple regression for partial discharge sources recognition 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. 55330007200 36634597400 57200961058 57203987646 57203978437 57203985173 57189337505 55578437800 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 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. Final 2023-05-29T06:51:16Z 2023-05-29T06:51:16Z 2018 Conference Paper 10.1109/IEEEGCC.2017.8448033 2-s2.0-85053906096 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053906096&doi=10.1109%2fIEEEGCC.2017.8448033&partnerID=40&md5=f6ee6806b8481b4a1956a1ed10175660 https://irepository.uniten.edu.my/handle/123456789/23725 8448033 All Open Access, Green Institute of Electrical and Electronics Engineers Inc. Scopus |
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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 |
| author2 |
55330007200 |
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55330007200 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. |
| format |
Conference Paper |
| author |
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. |
| spellingShingle |
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. Comparison of artificial neural network and multiple regression for partial discharge sources recognition |
| author_sort |
Abubakar Masud A. |
| title |
Comparison of artificial neural network and multiple regression for partial discharge sources recognition |
| title_short |
Comparison of artificial neural network and multiple regression for partial discharge sources recognition |
| title_full |
Comparison of artificial neural network and multiple regression for partial discharge sources recognition |
| title_fullStr |
Comparison of artificial neural network and multiple regression for partial discharge sources recognition |
| title_full_unstemmed |
Comparison of artificial neural network and multiple regression for partial discharge sources recognition |
| title_sort |
comparison of artificial neural network and multiple regression for partial discharge sources recognition |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806425700233641984 |
| score |
13.188404 |