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...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
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IEEE
2023
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| _version_ | 1825949037418774528 |
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| author | Asman, Saidatul Habsah Ab Aziz, Nur Fadilah Ab Kadir, Mohd Zainal Abidin Ungku Amirulddin, Ungku Anisa Roslan, Nurzanariah Elsanabary, Ahmed |
| 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 |
| collection | UPM |
| 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. |
| first_indexed | 2024-03-06T08:38:24Z |
| format | Conference or Workshop Item |
| id | upm.eprints-37453 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T08:38:24Z |
| publishDate | 2023 |
| publisher | IEEE |
| record_format | dspace |
| spelling | 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 |
| 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 |
| title | 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_short | Lightning fault classification for transmission line using support vector machine |
| title_sort | lightning fault classification for transmission line using support vector machine |
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