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dc.contributor.authorGazzea, Michele
dc.contributor.authorPacevicius, Michael Felix
dc.contributor.authorDammann, Dyre Oliver
dc.contributor.authorSapronova, Alla
dc.contributor.authorLunde, Torleif Markussen
dc.contributor.authorArghandeh, Reza
dc.date.accessioned2021-11-09T08:42:34Z
dc.date.available2021-11-09T08:42:34Z
dc.date.created2021-09-09T01:06:08Z
dc.date.issued2021
dc.identifier.citationIEEE Transactions on Power Delivery. 2021, .en_US
dc.identifier.issn0885-8977
dc.identifier.urihttps://hdl.handle.net/11250/2828511
dc.description.abstractVegetation Management is a significant preventive maintenance expense in many power transmission and distribution companies. Traditional Vegetation Management operational practices have proven ineffective and are rapidly becoming obsolete due to the lack of frequent inspection of vegetation and environmental states. The rise of satellite imagery data and machine learning provides an opportunity to close the loop with continuous data-driven vegetation monitoring. This paper proposes an automated framework for monitoring vegetation along power lines using high-resolution satellite imagery and a semi-supervised machine learning algorithm. The proposed satellite-based vegetation monitoring framework aims to reduce the cost and time of power line monitoring by partially replacing ground patrols and helicopter or drone inspection with satellite data analytics. It is implemented and demonstrated for a power distribution system operator (DSO) in the west of Norway. For further assessment, the satellite-based algorithm outcomes are compared with LiDAR survey data collected by helicopters. The results show the potential of the solution for reducing the monitoring costs for electric utilities.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleAutomated Power Lines Vegetation Monitoring using High-Resolution Satellite Imageryen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.source.pagenumber10en_US
dc.source.journalIEEE Transactions on Power Deliveryen_US
dc.identifier.doi10.1109/TPWRD.2021.3059307
dc.identifier.cristin1932630
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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