Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10963
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dc.contributor.authorYurdakul, Ogun-
dc.contributor.authorEser, Fatih-
dc.contributor.authorSivrikaya, Fikret-
dc.contributor.authorAlbayrak, Sahin-
dc.date.accessioned2020-12-01T06:47:41Z-
dc.date.available2020-12-01T06:47:41Z-
dc.date.issued2020-07-30-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/12088-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-10963-
dc.description.abstractPower system frequency plays a pivotal role in ensuring the security, adequacy, and integrity of a power system. While some frequency response services are automatically delivered to maintain the frequency within the stipulated limits, certain cases may require that system operators (SOs) manually intervene-against the clock-to take the necessary preventive or corrective actions. As such, SOs can be greatly aided by practical tools that afford them greater temporal leeway. To this end, we propose a methodology to forecast the power system frequency in the subsequent minute. We perform an extensive analysis so as to identify the factors that influence power system frequency. By effectively exploiting the identified factors, we develop a forecasting methodology that harnesses the long short-term memory model. We demonstrate the effectiveness of the proposed methodology on Great Britain transmission system frequency data using comparative assessments with selected benchmarks based on various evaluation metrics.en
dc.description.sponsorshipDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berlinen
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc600 Technik, Technologiede
dc.subject.otherforecastingen
dc.subject.otherfrequency controlen
dc.subject.otherfrequency responseen
dc.subject.otherlong short-term memoryen
dc.subject.otherLSTMen
dc.subject.otherrecurrent neural networken
dc.subject.otherRNNen
dc.titleVery short-term power system frequency forecastingen
dc.typeArticleen
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn2169-3536-
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1109/ACCESS.2020.3013165en
dcterms.bibliographicCitation.journaltitleIEEE Accessen
dcterms.bibliographicCitation.originalpublisherplaceNew York, NYen
dcterms.bibliographicCitation.volume8en
dcterms.bibliographicCitation.pageend141245en
dcterms.bibliographicCitation.pagestart141234en
dcterms.bibliographicCitation.originalpublishernameIEEEen
Appears in Collections:FG Agententechnologien in betrieblichen Anwendungen und der Telekommunikation (AOT) » Publications

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