Publication:
Fault Classification and Location in Three-Phase Transmission Lines Using Wavelet-based Machine Learning

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dc.contributor.authorZerahny C.K.Y.en_US
dc.contributor.authorYun L.K.en_US
dc.contributor.authorRaymond W.J.K.en_US
dc.contributor.authorMei K.T.en_US
dc.contributor.authorid57442220000en_US
dc.contributor.authorid57211330420en_US
dc.contributor.authorid55193255600en_US
dc.contributor.authorid57220873063en_US
dc.date.accessioned2023-05-29T09:09:56Z
dc.date.available2023-05-29T09:09:56Z
dc.date.issued2021
dc.descriptionDiscrete wavelet transforms; Electric fault location; Electric lines; Machine learning; Neural networks; Wavelet decomposition; Fault classification; Fault estimation; Fault zone; Long transmission lines; Machine learning models; Phase transmission; Three phase; Three phasis; Transmission-line; Two phase; Locationen_US
dc.description.abstractA long transmission line was simulated to collect fault data from one end of the line, which makes it a cost-efficient approach. Using Discrete Wavelet Transform (DWT), the essential characteristics of the fault type and its location can be extracted. Twelve types of mother wavelets with decomposition levels up to level 9 were compared and Haar wavelet was found to be most suitable. The resulting output was used as features to train several machine learning models for location and classification of faults. Fault estimation was carried out using the features extracted. By relying on the fault estimation, the search area for the fault can be reduced, thus decreasing the time needed to locate the actual fault. The artificial neural network (ANN) performed very well for fault classification having up to 100% accuracy. Another ANN was used for fault zone location and the accuracy obtained was 95.9%. Other machine learning models perform slightly poorer than ANN but had acceptable accuracy for fault location and classification. The results obtained considered single-phase to ground, two-phase, two-phase to ground and three-phase to ground faults. The faults occurred at various fault inception angles. The faults also included low and high fault impedances. The results indicate that this approach managed to detect and locate the fault zone with reasonable accuracy on a long transmission line model using data measured from one end only. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICIAS49414.2021.9642641
dc.identifier.scopus2-s2.0-85124166507
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85124166507&doi=10.1109%2fICIAS49414.2021.9642641&partnerID=40&md5=f41f640e1d116e15bf7899c9d9fa94b1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26395
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleInternational Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021
dc.titleFault Classification and Location in Three-Phase Transmission Lines Using Wavelet-based Machine Learningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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