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Application of ANFIS and ANN for partial discharge localization in oil through acoustic emission

This article presents an examination on the acoustic partial discharge (PD) localization in oil based on adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches. Impedance matching circuit (IMC) was used to measure the electrical PD. The acoustic PD was obtained...

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Main Authors: Hashim, Ahmad Hafiz Mohd, Azis, Norhafiz, Jasni, Jasronita, Radzi, Mohd Amran Mohd, Kozako, Masahiro, Jamil, Mohamad Kamarol Mohd, Yaakub, Zaini
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2023
Online Access:http://psasir.upm.edu.my/id/eprint/106638/1/Application%20of%20ANFIS%20and%20ANN.pdf
http://psasir.upm.edu.my/id/eprint/106638/
https://ieeexplore.ieee.org/document/10092794/
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spelling my.upm.eprints.1066382024-10-11T08:26:42Z http://psasir.upm.edu.my/id/eprint/106638/ Application of ANFIS and ANN for partial discharge localization in oil through acoustic emission Hashim, Ahmad Hafiz Mohd Azis, Norhafiz Jasni, Jasronita Radzi, Mohd Amran Mohd Kozako, Masahiro Jamil, Mohamad Kamarol Mohd Yaakub, Zaini This article presents an examination on the acoustic partial discharge (PD) localization in oil based on adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches. Impedance matching circuit (IMC) was used to measure the electrical PD. The acoustic PD was obtained through an acoustic emission (AE) sensor and preamplifier gain unit. In total, 112 coordinates for each of the AE sensors were utilized to evaluate the location of the PD. Once the voltage reached 30 kV, the electrical and acoustic PDs were recorded. Next, the data were preprocessed by moving average (MA) and analyzed by time of arrival (TOA), ANFIS, and ANN. The distance between PD and AE sensor was calculated based on TOA to determine the PD location. These information were used as an input to train the network by optimizing epoch and neuron for ANFIS and ANN in order to locate PD. ANFIS has the best percentage of PD source prediction based on root mean square error (RMSE) and coefficient of determination ( R2) as compared to ANN. Meanwhile, the computation time for ANN is 1.75 s faster than ANFIS to perform PD localization based on AE PD signals. Institute of Electrical and Electronics Engineers 2023-06-03 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/106638/1/Application%20of%20ANFIS%20and%20ANN.pdf Hashim, Ahmad Hafiz Mohd and Azis, Norhafiz and Jasni, Jasronita and Radzi, Mohd Amran Mohd and Kozako, Masahiro and Jamil, Mohamad Kamarol Mohd and Yaakub, Zaini (2023) Application of ANFIS and ANN for partial discharge localization in oil through acoustic emission. IEEE Transactions on Dielectrics and Electrical Insulation, 30 (3). pp. 1247-1254. ISSN 1070-9878; ESSN: 1558-4135 https://ieeexplore.ieee.org/document/10092794/ 10.1109/tdei.2023.3264958
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description This article presents an examination on the acoustic partial discharge (PD) localization in oil based on adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches. Impedance matching circuit (IMC) was used to measure the electrical PD. The acoustic PD was obtained through an acoustic emission (AE) sensor and preamplifier gain unit. In total, 112 coordinates for each of the AE sensors were utilized to evaluate the location of the PD. Once the voltage reached 30 kV, the electrical and acoustic PDs were recorded. Next, the data were preprocessed by moving average (MA) and analyzed by time of arrival (TOA), ANFIS, and ANN. The distance between PD and AE sensor was calculated based on TOA to determine the PD location. These information were used as an input to train the network by optimizing epoch and neuron for ANFIS and ANN in order to locate PD. ANFIS has the best percentage of PD source prediction based on root mean square error (RMSE) and coefficient of determination ( R2) as compared to ANN. Meanwhile, the computation time for ANN is 1.75 s faster than ANFIS to perform PD localization based on AE PD signals.
format Article
author Hashim, Ahmad Hafiz Mohd
Azis, Norhafiz
Jasni, Jasronita
Radzi, Mohd Amran Mohd
Kozako, Masahiro
Jamil, Mohamad Kamarol Mohd
Yaakub, Zaini
spellingShingle Hashim, Ahmad Hafiz Mohd
Azis, Norhafiz
Jasni, Jasronita
Radzi, Mohd Amran Mohd
Kozako, Masahiro
Jamil, Mohamad Kamarol Mohd
Yaakub, Zaini
Application of ANFIS and ANN for partial discharge localization in oil through acoustic emission
author_facet Hashim, Ahmad Hafiz Mohd
Azis, Norhafiz
Jasni, Jasronita
Radzi, Mohd Amran Mohd
Kozako, Masahiro
Jamil, Mohamad Kamarol Mohd
Yaakub, Zaini
author_sort Hashim, Ahmad Hafiz Mohd
title Application of ANFIS and ANN for partial discharge localization in oil through acoustic emission
title_short Application of ANFIS and ANN for partial discharge localization in oil through acoustic emission
title_full Application of ANFIS and ANN for partial discharge localization in oil through acoustic emission
title_fullStr Application of ANFIS and ANN for partial discharge localization in oil through acoustic emission
title_full_unstemmed Application of ANFIS and ANN for partial discharge localization in oil through acoustic emission
title_sort application of anfis and ann for partial discharge localization in oil through acoustic emission
publisher Institute of Electrical and Electronics Engineers
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/106638/1/Application%20of%20ANFIS%20and%20ANN.pdf
http://psasir.upm.edu.my/id/eprint/106638/
https://ieeexplore.ieee.org/document/10092794/
_version_ 1814054608109043712
score 13.211869

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