Overhead transmission lines dynamic rating estimation for renewable energy integration using machine learning

The increasing electrical power generation from renewable energy resources poses new challenges to the electrical grid. These challenges are mainly related to the inconsistent nature of renewable energies and the burden on transmission lines due to the increasing generated power. In this paper, the...

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Autores principales:	Abdelrahman Sobhy, Tamer F. Megahed, Mohammed Abo-Zahhad
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Renewable energy Dynamic line rating Machine learning Random forest regressor Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/aeaf4ba562ac4ff88d16c35d646bc92c
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id	oai:doaj.org-article:aeaf4ba562ac4ff88d16c35d646bc92c
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spelling	oai:doaj.org-article:aeaf4ba562ac4ff88d16c35d646bc92c2021-11-18T04:49:25ZOverhead transmission lines dynamic rating estimation for renewable energy integration using machine learning2352-484710.1016/j.egyr.2021.07.060https://doaj.org/article/aeaf4ba562ac4ff88d16c35d646bc92c2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721005254https://doaj.org/toc/2352-4847The increasing electrical power generation from renewable energy resources poses new challenges to the electrical grid. These challenges are mainly related to the inconsistent nature of renewable energies and the burden on transmission lines due to the increasing generated power. In this paper, the correlation between renewable energy generation and the transmission capability of electrical power lines is studied. In order to facilitate the accommodation of renewable energies the actual dynamic line rating (DLR) is estimated based on actual weather conditions. To achieve this purpose a new DLR estimation method using machine learning is presented. Line rating depends on various meteorological factors (wind speed, wind direction, ambient temperature, and solar radiation). A machine-learning model was trained using Random Forest (RF) regressor to estimate the rating of a transmission line based on these meteorological factors. The data used for model training is actual measurements of weather conditions, conductor temperature, and conductor current. Results, where the trained model is used to estimate the rating of an ACSR Lynx conductor with a thermal limit of 80°C, are presented.Abdelrahman SobhyTamer F. MegahedMohammed Abo-ZahhadElsevierarticleRenewable energyDynamic line ratingMachine learningRandom forest regressorElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 804-813 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Renewable energy Dynamic line rating Machine learning Random forest regressor Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Renewable energy Dynamic line rating Machine learning Random forest regressor Electrical engineering. Electronics. Nuclear engineering TK1-9971 Abdelrahman Sobhy Tamer F. Megahed Mohammed Abo-Zahhad Overhead transmission lines dynamic rating estimation for renewable energy integration using machine learning
description	The increasing electrical power generation from renewable energy resources poses new challenges to the electrical grid. These challenges are mainly related to the inconsistent nature of renewable energies and the burden on transmission lines due to the increasing generated power. In this paper, the correlation between renewable energy generation and the transmission capability of electrical power lines is studied. In order to facilitate the accommodation of renewable energies the actual dynamic line rating (DLR) is estimated based on actual weather conditions. To achieve this purpose a new DLR estimation method using machine learning is presented. Line rating depends on various meteorological factors (wind speed, wind direction, ambient temperature, and solar radiation). A machine-learning model was trained using Random Forest (RF) regressor to estimate the rating of a transmission line based on these meteorological factors. The data used for model training is actual measurements of weather conditions, conductor temperature, and conductor current. Results, where the trained model is used to estimate the rating of an ACSR Lynx conductor with a thermal limit of 80°C, are presented.
format	article
author	Abdelrahman Sobhy Tamer F. Megahed Mohammed Abo-Zahhad
author_facet	Abdelrahman Sobhy Tamer F. Megahed Mohammed Abo-Zahhad
author_sort	Abdelrahman Sobhy
title	Overhead transmission lines dynamic rating estimation for renewable energy integration using machine learning
title_short	Overhead transmission lines dynamic rating estimation for renewable energy integration using machine learning
title_full	Overhead transmission lines dynamic rating estimation for renewable energy integration using machine learning
title_fullStr	Overhead transmission lines dynamic rating estimation for renewable energy integration using machine learning
title_full_unstemmed	Overhead transmission lines dynamic rating estimation for renewable energy integration using machine learning
title_sort	overhead transmission lines dynamic rating estimation for renewable energy integration using machine learning
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/aeaf4ba562ac4ff88d16c35d646bc92c
work_keys_str_mv	AT abdelrahmansobhy overheadtransmissionlinesdynamicratingestimationforrenewableenergyintegrationusingmachinelearning AT tamerfmegahed overheadtransmissionlinesdynamicratingestimationforrenewableenergyintegrationusingmachinelearning AT mohammedabozahhad overheadtransmissionlinesdynamicratingestimationforrenewableenergyintegrationusingmachinelearning
_version_	1718424999183777792

Overhead transmission lines dynamic rating estimation for renewable energy integration using machine learning

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