Publication:
A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System

Simple item page
dc.citedby1
dc.contributor.authorKhaleefah S.H.en_US
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorKhattak U.F.en_US
dc.contributor.authorYaacob S.S.en_US
dc.contributor.authorAlanda A.en_US
dc.contributor.authorid57188929678en_US
dc.contributor.authorid37036085800en_US
dc.contributor.authorid55652730500en_US
dc.contributor.authorid57193278880en_US
dc.contributor.authorid55958483000en_US
dc.contributor.authorid57203718850en_US
dc.date.accessioned2025-03-03T07:47:05Z
dc.date.available2025-03-03T07:47:05Z
dc.date.issued2024
dc.description.abstractProgressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models. ? 2024, Politeknik Negeri Padang. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.62527/joiv.8.2.2701
dc.identifier.epage818
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85196080510
dc.identifier.spage812
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85196080510&doi=10.62527%2fjoiv.8.2.2701&partnerID=40&md5=72264eb88abfd23f18c8635f5244fa92
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37061
dc.identifier.volume8
dc.pagecount6
dc.publisherPoliteknik Negeri Padangen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleInternational Journal on Informatics Visualization
dc.titleA Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission Systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS