Publication:
Artificial Intelligence Forecasting for Transmission Line Ampacity

Simple item page
dc.contributor.authorHamed Y.en_US
dc.contributor.authorAbd Rahman M.S.en_US
dc.contributor.authorAb Kadir M.Z.A.en_US
dc.contributor.authorOsman M.en_US
dc.contributor.authorAriffin A.M.en_US
dc.contributor.authorAb Aziz N.F.en_US
dc.contributor.authorid57189368701en_US
dc.contributor.authorid36609854400en_US
dc.contributor.authorid25947297000en_US
dc.contributor.authorid7201930315en_US
dc.contributor.authorid16400722400en_US
dc.contributor.authorid57221906825en_US
dc.date.accessioned2023-05-29T09:42:26Z
dc.date.available2023-05-29T09:42:26Z
dc.date.issued2022
dc.description.abstractOverhead transmission lines are used to transmit electrical energy at a high voltage over long distances. The capacity of the transmission system is usually capped at a certain level to comply with the safety and reliability conditions of the system. The limit of the current capacity is estimated based on the worst weather conditions. However, during normal weather conditions, the conductor usually has more transition capacity than its limited level. Dynamic line ratings (DLR) provide an estimation of the line current capacity based on the actual weather conditions. Hence, the transmission line can be used to its full capacity when needed. Dynamic line ratings can be modelled physically, statistically, and using machine learning and artificial intelligence. This chapter aims to address the implementation of machine learning and artificial intelligence algorithms in predicting the DLR of transmission systems. Using artificial intelligence to forecast the measurements of DLR enables operators to estimate the current maximum capacity in real-time. The advantages and limitations of each approach are detailed in this chapter as well. � 2022, Institute of Technology PETRONAS Sdn Bhd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-79606-8_16
dc.identifier.epage234
dc.identifier.scopus2-s2.0-85115394837
dc.identifier.spage217
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85115394837&doi=10.1007%2f978-3-030-79606-8_16&partnerID=40&md5=377f1fa37b4c0c6613f80c7112ca132d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27305
dc.identifier.volume383
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleStudies in Systems, Decision and Control
dc.titleArtificial Intelligence Forecasting for Transmission Line Ampacityen_US
dc.typeBook Chapteren_US
dspace.entity.typePublication
Files
Collections
SCOPUS