Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources
This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-Ampli...
Saved in:
Main Author: | |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2017
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/77115/1/NurulAiniBani2017_ComparisonofthePerformanceofArtificial.pdf http://eprints.utm.my/id/eprint/77115/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029061428&doi=10.3390%2fen10071060&partnerID=40&md5=87b8a80ff251313b38333f4bdd774dea |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.77115 |
---|---|
record_format |
eprints |
spelling |
my.utm.771152018-05-31T09:36:52Z http://eprints.utm.my/id/eprint/77115/ Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources Bani, N. A. T Technology (General) This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-Amplitude-number (f-q-n) measurements. Two PD fault classes considered are internal discharges in voids and surface discharges. In the void class, there are single voids, serial voids and parallel voids in polyethylene terephthalate (PET), while the surface discharge class comprises four different surface discharge arrangements on pressboard in oil at different voltages and angular positioning of the ground electrode on the respective pressboards. Previously, the ANN and FL have been investigated for PD classification, but there is no work reported in the literature that compares their performance, specifically when applied for real time PD detection problem. As expected, both the ANN and FL can recognize PD defect classes, but the results show that the ANN appears to be more robust as compared to the FL, but these conclusions required to be further investigated with complex PD examples. Finally, both the ANN and FL were assessed as practical PD classification. Despite of the limitations of the ANN, it is concluded that the ANN is better suited for practical PD recognition because of its ability to provide accurate recognition values and the severity level of PD defects. MDPI AG 2017 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/77115/1/NurulAiniBani2017_ComparisonofthePerformanceofArtificial.pdf Bani, N. A. (2017) Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources. Energies, 10 (7). ISSN 1996-1073 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029061428&doi=10.3390%2fen10071060&partnerID=40&md5=87b8a80ff251313b38333f4bdd774dea DOI:10.3390/en10071060 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
language |
English |
topic |
T Technology (General) |
spellingShingle |
T Technology (General) Bani, N. A. Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources |
description |
This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-Amplitude-number (f-q-n) measurements. Two PD fault classes considered are internal discharges in voids and surface discharges. In the void class, there are single voids, serial voids and parallel voids in polyethylene terephthalate (PET), while the surface discharge class comprises four different surface discharge arrangements on pressboard in oil at different voltages and angular positioning of the ground electrode on the respective pressboards. Previously, the ANN and FL have been investigated for PD classification, but there is no work reported in the literature that compares their performance, specifically when applied for real time PD detection problem. As expected, both the ANN and FL can recognize PD defect classes, but the results show that the ANN appears to be more robust as compared to the FL, but these conclusions required to be further investigated with complex PD examples. Finally, both the ANN and FL were assessed as practical PD classification. Despite of the limitations of the ANN, it is concluded that the ANN is better suited for practical PD recognition because of its ability to provide accurate recognition values and the severity level of PD defects. |
format |
Article |
author |
Bani, N. A. |
author_facet |
Bani, N. A. |
author_sort |
Bani, N. A. |
title |
Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources |
title_short |
Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources |
title_full |
Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources |
title_fullStr |
Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources |
title_full_unstemmed |
Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources |
title_sort |
comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources |
publisher |
MDPI AG |
publishDate |
2017 |
url |
http://eprints.utm.my/id/eprint/77115/1/NurulAiniBani2017_ComparisonofthePerformanceofArtificial.pdf http://eprints.utm.my/id/eprint/77115/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029061428&doi=10.3390%2fen10071060&partnerID=40&md5=87b8a80ff251313b38333f4bdd774dea |
_version_ |
1643657502476206080 |
score |
13.18916 |