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The current state of the art in research on predictive maintenance in smart grid distribution network: Fault�s types, causes, and prediction methods�a systematic review

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dc.citedby14
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorMd Nasir N.R.en_US
dc.contributor.authorGurunathan M.en_US
dc.contributor.authorRaj P.en_US
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid57076901800en_US
dc.contributor.authorid57215588319en_US
dc.contributor.authorid57228254300en_US
dc.contributor.authorid37036085800en_US
dc.date.accessioned2023-05-29T09:06:18Z
dc.date.available2023-05-29T09:06:18Z
dc.date.issued2021
dc.descriptionArtificial intelligence; Electric power transmission networks; Fault detection; Internet of things; Predictive maintenance; Effective transmission; Exponential growth; Internet of Things (IOT); Prediction methods; Renewable energies; Smart grid systems; State of the art; Systematic Review; Smart power gridsen_US
dc.description.abstractWith the exponential growth of science, Internet of Things (IoT) innovation, and expanding significance in renewable energy, Smart Grid has become an advanced innovative thought universally as a solution for the power demand increase around the world. The smart grid is the most practical trend of effective transmission of present-day power assets. The paper aims to survey the present literature concerning predictive maintenance and different types of faults that could be detected within the smart grid. Four databases (Scopus, ScienceDirect, IEEE Xplore, and Web of Science) were searched between 2012 and 2020. Sixty-five (n = 65) were chosen based on specified exclusion and inclusion criteria. Fifty-seven percent (n = 37/65) of the studies analyzed the issues from predictive maintenance perspectives, while about 18% (n = 12/65) focused on factors-related review studies on the smart grid and about 15% (n = 10/65) focused on factors related to the experimental study. The remaining 9% (n = 6/65) concentrated on fields related to the challenges and benefits of the study. The significance of predictive maintenance has been developing over time in connection with Industry 4.0 revolution. The paper�s fundamental commitment is the outline and overview of faults in the smart grid such as fault location and detection. Therefore, advanced methods of applying Artificial Intelligence (AI) techniques can enhance and improve the reliability and resilience of smart grid systems. For future direction, we aim to supply a deep understanding of Smart meters to detect or monitor faults in the smart grid as it is the primary IoT sensor in an AMI. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5078
dc.identifier.doi10.3390/en14165078
dc.identifier.issue16
dc.identifier.scopus2-s2.0-85113314157
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85113314157&doi=10.3390%2fen14165078&partnerID=40&md5=9655448f1fa4a42aee3dfda39f5f54c5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26045
dc.identifier.volume14
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEnergies
dc.titleThe current state of the art in research on predictive maintenance in smart grid distribution network: Fault�s types, causes, and prediction methods�a systematic reviewen_US
dc.typeReviewen_US
dspace.entity.typePublication
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