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Lightning fault classification for transmission line using support vector machine

Transmission lines are susceptible to a variety of phenomena that can cause system faults. The most prevalent cause of faults in the power system is lightning strikes, while other causes may include insulator failure, tree or crane encroachment. In this study, two machine learning algorithms, Suppor...

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المؤلفون الرئيسيون: Asman, Saidatul Habsah, Ab Aziz, Nur Fadilah, Ab Kadir, Mohd Zainal Abidin, Ungku Amirulddin, Ungku Anisa, Roslan, Nurzanariah, Elsanabary, Ahmed
التنسيق: Conference or Workshop Item
منشور في: IEEE 2023
الوصول للمادة أونلاين:http://psasir.upm.edu.my/id/eprint/37453/
https://ieeexplore.ieee.org/document/10181525
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id my.upm.eprints.37453
record_format eprints
spelling my.upm.eprints.374532023-09-27T10:11:14Z http://psasir.upm.edu.my/id/eprint/37453/ Lightning fault classification for transmission line using support vector machine Asman, Saidatul Habsah Ab Aziz, Nur Fadilah Ab Kadir, Mohd Zainal Abidin Ungku Amirulddin, Ungku Anisa Roslan, Nurzanariah Elsanabary, Ahmed Transmission lines are susceptible to a variety of phenomena that can cause system faults. The most prevalent cause of faults in the power system is lightning strikes, while other causes may include insulator failure, tree or crane encroachment. In this study, two machine learning algorithms, Support Vector Machine (SVM) and k-Nearest Neighbor (kNN), were used and compared to classify faults due to lightning strikes, insulator failure, tree and crane encroachment. The input variables for the models were based on the root mean square (RMS) current duration, voltage dip, and energy wavelet measured at the sending end of a line. The proposed method was implemented in the MATLAB/SIMULINK programming platform. The classification performance of the developed algorithms was evaluated using confusion matrix. Overall, SVM algorithm performed better than k-NN in terms of classification accuracy, achieving a value of 97.10% compared to k-NN’s 70.60%. Moreover, SVM also outperformed k-NN in terms of computational time, with time taken by SVM is 3.63 s compared to 10.06 s by k-NN. IEEE 2023 Conference or Workshop Item PeerReviewed Asman, Saidatul Habsah and Ab Aziz, Nur Fadilah and Ab Kadir, Mohd Zainal Abidin and Ungku Amirulddin, Ungku Anisa and Roslan, Nurzanariah and Elsanabary, Ahmed (2023) Lightning fault classification for transmission line using support vector machine. In: 12th Asia-Pacific International Conference on Lightning (APL 2023), 12-15 June 2023, Langkawi, Malaysia. . https://ieeexplore.ieee.org/document/10181525 10.1109/APL57308.2023.10181525
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/
description Transmission lines are susceptible to a variety of phenomena that can cause system faults. The most prevalent cause of faults in the power system is lightning strikes, while other causes may include insulator failure, tree or crane encroachment. In this study, two machine learning algorithms, Support Vector Machine (SVM) and k-Nearest Neighbor (kNN), were used and compared to classify faults due to lightning strikes, insulator failure, tree and crane encroachment. The input variables for the models were based on the root mean square (RMS) current duration, voltage dip, and energy wavelet measured at the sending end of a line. The proposed method was implemented in the MATLAB/SIMULINK programming platform. The classification performance of the developed algorithms was evaluated using confusion matrix. Overall, SVM algorithm performed better than k-NN in terms of classification accuracy, achieving a value of 97.10% compared to k-NN’s 70.60%. Moreover, SVM also outperformed k-NN in terms of computational time, with time taken by SVM is 3.63 s compared to 10.06 s by k-NN.
format Conference or Workshop Item
author Asman, Saidatul Habsah
Ab Aziz, Nur Fadilah
Ab Kadir, Mohd Zainal Abidin
Ungku Amirulddin, Ungku Anisa
Roslan, Nurzanariah
Elsanabary, Ahmed
spellingShingle Asman, Saidatul Habsah
Ab Aziz, Nur Fadilah
Ab Kadir, Mohd Zainal Abidin
Ungku Amirulddin, Ungku Anisa
Roslan, Nurzanariah
Elsanabary, Ahmed
Lightning fault classification for transmission line using support vector machine
author_facet Asman, Saidatul Habsah
Ab Aziz, Nur Fadilah
Ab Kadir, Mohd Zainal Abidin
Ungku Amirulddin, Ungku Anisa
Roslan, Nurzanariah
Elsanabary, Ahmed
author_sort Asman, Saidatul Habsah
title Lightning fault classification for transmission line using support vector machine
title_short Lightning fault classification for transmission line using support vector machine
title_full Lightning fault classification for transmission line using support vector machine
title_fullStr Lightning fault classification for transmission line using support vector machine
title_full_unstemmed Lightning fault classification for transmission line using support vector machine
title_sort lightning fault classification for transmission line using support vector machine
publisher IEEE
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/37453/
https://ieeexplore.ieee.org/document/10181525
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score 13.251813

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