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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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Main Authors: Asman S.H., Aziz N.F.A., Kadir M.Z.A.A., Amirulddin U.A.U., Roslan N., Elsanabary A.
Other Authors: 57194493395
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Subjects:
accuracy
computational time
k-Nearest Neighbor (k-NN)
lightning fault
Support Vector Machine (SVM)
Electric lines
Electric power transmission
Learning algorithms
MATLAB
Motion compensation
Nearest neighbor search
Transmissions
Accuracy
Computational time
Failure trees
Fault classification
K-near neighbor
Lightning faults
Lightning strikes
Support vector machine
Support vectors machine
Transmission-line
Support vector machines
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id my.uniten.dspace-34642
record_format dspace
spelling my.uniten.dspace-346422024-10-14T11:21:21Z Lightning Fault Classification for Transmission Line Using Support Vector Machine Asman S.H. Aziz N.F.A. Kadir M.Z.A.A. Amirulddin U.A.U. Roslan N. Elsanabary A. 57194493395 57221906825 25947297000 26422804600 57205233093 57221120034 accuracy computational time k-Nearest Neighbor (k-NN) lightning fault Support Vector Machine (SVM) Electric lines Electric power transmission Learning algorithms MATLAB Motion compensation Nearest neighbor search Transmissions Accuracy Computational time Failure trees Fault classification K-near neighbor Lightning faults Lightning strikes Support vector machine Support vectors machine Transmission-line Support vector machines 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. � 2023 IEEE. Final 2024-10-14T03:21:21Z 2024-10-14T03:21:21Z 2023 Conference Paper 10.1109/APL57308.2023.10181525 2-s2.0-85166739771 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166739771&doi=10.1109%2fAPL57308.2023.10181525&partnerID=40&md5=832772eb5c1227dd7187f9e2083f9348 https://irepository.uniten.edu.my/handle/123456789/34642 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic accuracy
computational time
k-Nearest Neighbor (k-NN)
lightning fault
Support Vector Machine (SVM)
Electric lines
Electric power transmission
Learning algorithms
MATLAB
Motion compensation
Nearest neighbor search
Transmissions
Accuracy
Computational time
Failure trees
Fault classification
K-near neighbor
Lightning faults
Lightning strikes
Support vector machine
Support vectors machine
Transmission-line
Support vector machines
spellingShingle accuracy
computational time
k-Nearest Neighbor (k-NN)
lightning fault
Support Vector Machine (SVM)
Electric lines
Electric power transmission
Learning algorithms
MATLAB
Motion compensation
Nearest neighbor search
Transmissions
Accuracy
Computational time
Failure trees
Fault classification
K-near neighbor
Lightning faults
Lightning strikes
Support vector machine
Support vectors machine
Transmission-line
Support vector machines
Asman S.H.
Aziz N.F.A.
Kadir M.Z.A.A.
Amirulddin U.A.U.
Roslan N.
Elsanabary A.
Lightning Fault Classification for Transmission Line Using Support Vector Machine
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. � 2023 IEEE.
author2 57194493395
author_facet 57194493395
Asman S.H.
Aziz N.F.A.
Kadir M.Z.A.A.
Amirulddin U.A.U.
Roslan N.
Elsanabary A.
format Conference Paper
author Asman S.H.
Aziz N.F.A.
Kadir M.Z.A.A.
Amirulddin U.A.U.
Roslan N.
Elsanabary A.
author_sort Asman S.H.
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 Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
_version_ 1814061189193269248
score 13.226497

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