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Artificial Intelligence Forecasting for Transmission Line Ampacity

Overhead 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 ba...

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Main Authors: Hamed Y., Abd Rahman M.S., Ab Kadir M.Z.A., Osman M., Ariffin A.M., Ab Aziz N.F.
Other Authors: 57189368701
Format: Book Chapter
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-273052023-05-29T17:42:26Z Artificial Intelligence Forecasting for Transmission Line Ampacity Hamed Y. Abd Rahman M.S. Ab Kadir M.Z.A. Osman M. Ariffin A.M. Ab Aziz N.F. 57189368701 36609854400 25947297000 7201930315 16400722400 57221906825 Overhead 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. Final 2023-05-29T09:42:26Z 2023-05-29T09:42:26Z 2022 Book Chapter 10.1007/978-3-030-79606-8_16 2-s2.0-85115394837 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115394837&doi=10.1007%2f978-3-030-79606-8_16&partnerID=40&md5=377f1fa37b4c0c6613f80c7112ca132d https://irepository.uniten.edu.my/handle/123456789/27305 383 217 234 Springer Science and Business Media Deutschland GmbH 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/
description Overhead 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.
author2 57189368701
author_facet 57189368701
Hamed Y.
Abd Rahman M.S.
Ab Kadir M.Z.A.
Osman M.
Ariffin A.M.
Ab Aziz N.F.
format Book Chapter
author Hamed Y.
Abd Rahman M.S.
Ab Kadir M.Z.A.
Osman M.
Ariffin A.M.
Ab Aziz N.F.
spellingShingle Hamed Y.
Abd Rahman M.S.
Ab Kadir M.Z.A.
Osman M.
Ariffin A.M.
Ab Aziz N.F.
Artificial Intelligence Forecasting for Transmission Line Ampacity
author_sort Hamed Y.
title Artificial Intelligence Forecasting for Transmission Line Ampacity
title_short Artificial Intelligence Forecasting for Transmission Line Ampacity
title_full Artificial Intelligence Forecasting for Transmission Line Ampacity
title_fullStr Artificial Intelligence Forecasting for Transmission Line Ampacity
title_full_unstemmed Artificial Intelligence Forecasting for Transmission Line Ampacity
title_sort artificial intelligence forecasting for transmission line ampacity
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806427372927320064
score 13.22586

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