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dc.contributor.authorKumer, Valerie-Marie
dc.contributor.authorReuder, Joachim
dc.contributor.authorDorninger, Manfred
dc.contributor.authorZauner, Rudolf
dc.contributor.authorGrubišić, Vanda
dc.date.accessioned2016-10-04T07:22:19Z
dc.date.available2016-10-04T07:22:19Z
dc.date.issued2016-07-10
dc.PublishedRenewable Energy 2016, 99:898-910eng
dc.identifier.issn1879-0682en_US
dc.identifier.urihttps://hdl.handle.net/1956/12900
dc.description.abstractThis study shows that turbulent kinetic energy (TKE) estimates, derived from static LiDARs in Doppler Beam Swing (DBS) mode, permit a qualitative and quantitative characterization and analysis of turbulent structures as wind turbine wakes, and convective or shear generated eddies in the lower atmospheric boundary layer. The analysed data, collected by a WINDCUBE™ v1 in a wind park in Austria, is compared to WINDCUBE™ v1 and sonic data from the WINd Turbine Wake EXperiment Wieringermeer (WINTWEX-W). Although turbulence measurements with a WINDCUBE™ v1 are limited to a specific length scale, processed measurements above this threshold are in a good agreement with sonic anemometer data. In contrast to the commonly used turbulence intensity, the calculation of TKE not only provides an appropriate measure of turbulence intensities but also gives an insight into its origin. The processed data show typical wake characteristics, as flow decelerations, turbulence enhancement and wake rotation. By comparing these turbulence characteristics to other turbulent structures in the atmospheric boundary layer, we found that convection driven eddies in the surface layer have similar turbulence characteristics as turbine wakes, which makes convective weather situations relevant for wind turbine fatigue considerations.en_US
dc.language.isoengeng
dc.publisherElsevieren_US
dc.rightsAttribution CC BY-NC-NDeng
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/eng
dc.subjectLiDAReng
dc.subjectTurbulenceeng
dc.subjectWind turbine wakeseng
dc.titleTurbulent kinetic energy estimates from profiling wind LiDAR measurements and their potential for wind energy applicationsen_US
dc.typePeer reviewed
dc.typeJournal article
dc.date.updated2016-08-31T12:15:56Z
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2016 the authorsen_US
dc.identifier.doihttps://doi.org/10.1016/j.renene.2016.07.014
dc.identifier.cristin1372555


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