Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3413
DC FieldValueLanguage
dc.contributor.authorTeimourzadeh Baboli, Payam-
dc.contributor.authorBabazadeh, Davood-
dc.contributor.authorRaeiszadeh, Amin-
dc.contributor.authorHorodyvskyy, Susanne-
dc.contributor.authorKoprek, Isabel-
dc.date.accessioned2021-04-06T14:23:10Z-
dc.date.available2021-04-06T14:23:10Z-
dc.date.issued2021-03-26-
dc.identifierdoi: 10.3390/infrastructures6040050-
dc.identifier.citationInfrastructures 6 (4): 50 (2021)de_DE
dc.identifier.issn2412-3811de_DE
dc.identifier.urihttp://hdl.handle.net/11420/9200-
dc.description.abstractWith the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.-
dc.description.abstractWith the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.en
dc.language.isoende_DE
dc.publisherMultidisciplinary Digital Publishing Institutede_DE
dc.relation.ispartofInfrastructuresde_DE
dc.rightsCC BY 4.0de_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de_DE
dc.subjectartificial neural networkde_DE
dc.subjectcondition-based maintenancede_DE
dc.subjecthealth monitoringde_DE
dc.subjectwind turbinede_DE
dc.subject.ddc600: Technikde_DE
dc.subject.ddc620: Ingenieurwissenschaftende_DE
dc.titleOptimal temperature-based condition monitoring system for wind turbinesde_DE
dc.typeArticlede_DE
dc.date.updated2021-03-26T14:05:51Z-
dc.identifier.doi10.15480/882.3413-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0130602-
tuhh.oai.showtruede_DE
tuhh.abstract.englishWith the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.de_DE
tuhh.publisher.doi10.3390/infrastructures6040050-
tuhh.publication.instituteElektrische Energietechnik E-6de_DE
tuhh.identifier.doi10.15480/882.3413-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue4de_DE
tuhh.container.volume6de_DE
dc.rights.nationallicensefalsede_DE
tuhh.container.articlenumber50de_DE
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
item.fulltextWith Fulltext-
item.mappedtypeArticle-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.creatorGNDTeimourzadeh Baboli, Payam-
item.creatorGNDBabazadeh, Davood-
item.creatorGNDRaeiszadeh, Amin-
item.creatorGNDHorodyvskyy, Susanne-
item.creatorGNDKoprek, Isabel-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.grantfulltextopen-
item.creatorOrcidTeimourzadeh Baboli, Payam-
item.creatorOrcidBabazadeh, Davood-
item.creatorOrcidRaeiszadeh, Amin-
item.creatorOrcidHorodyvskyy, Susanne-
item.creatorOrcidKoprek, Isabel-
crisitem.author.deptElektrische Energietechnik E-6-
crisitem.author.orcid0000-0001-7203-2202-
crisitem.author.orcid0000-0003-3946-7655-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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