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dc.contributor.authorGuseinoviene, Eleonora
dc.contributor.authorSenulis, Audrius
dc.contributor.authorAkıncı, Tahir Çetin
dc.contributor.authorŞeker, Serhat
dc.date.accessioned2021-12-12T17:02:45Z
dc.date.available2021-12-12T17:02:45Z
dc.date.issued2014
dc.identifier.isbn978-1-4799-3787-5
dc.identifier.urihttps://hdl.handle.net/20.500.11857/3527
dc.description9th International Conference on Ecological Vehicles and Renewable Energies (EVER) -- MAR 25-27, 2014 -- Monte Carlo, MONACO -- Monaco Dev Durable, IEEE Ind Applicat Soc, IEEE Power Elect Soc, IEEE Magnet Socen_US
dc.description.abstractIn this paper, wind speed measurements are considered for Mardin region in Turkey. Mardin city is situated in East part of Turkey. Mardin is a mountainous area suitable for wind. And also, its wind energy potential is at an acceptable level. Wind speed data in this study, is obtained from the national meteorology station. This real-time measured data is suitable to international criteria. This annual data is related to the measurements of 2011, from January 1, 2011 to December 31, 2011. There are two important aspects of the study. First one is related to calculation of the statistical parameters for wind speed data. Here four parameters of a statistical distribution are calculated. These are mean value, standard deviation, skewness and kurtosis. Last two parameters are used to determine the non-Gaussian (or Gaussian) characteristics. In this study, the skewness and kurtosis parameters are are calculated and compared. Parameters indicate asymmetrical and non-Gaussian characteristics. Hence the wind speed distribution is defined by Weibull distribution and its probability density function is shown for shape parameter and average speed of the wind. Thus the histogram and Weibull probability density function are calculated as well as the statistical findings. As a second aspect of the study, data is accepted as non-stationary. So its Continuous Wavelet Transform is used for the determination of the seasonal transitions and determined the time-frequency content. Thanks to the continuous wavelet transform, low and high frequency regions of wind speed data are distinguished. Continuous wavelet transform findings are quite satisfactory.en_US
dc.language.isoengen_US
dc.publisherIeeeen_US
dc.relation.ispartof2014 Ninth International Conference On Ecological Vehicles and Renewable Energies (Ever)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectwind speed dataen_US
dc.subjectweibull distributionen_US
dc.subjectcontinuous wavelet transformen_US
dc.titleStatistical and Continuous Wavelet Analysis of Wind Speed Data in Mardin-Turkeyen_US
dc.typeproceedingsPaper
dc.authoridAKINCI, Tahir Cetin/0000-0002-4657-6617
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid15759574900
dc.authorscopusid12143193300
dc.authorscopusid16229256000
dc.authorscopusid7005822797
dc.identifier.wosWOS:000360288900052en_US
dc.identifier.scopus2-s2.0-84904498447en_US
dc.authorwosidAKINCI, Tahir Cetin/AAB-3397-2021
dc.authorwosidSenulis, Audrius/AAL-9907-2020


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