دانلود مقاله ISI انگلیسی شماره 56615
ترجمه فارسی عنوان مقاله

تجزیه و تحلیل اتلاف انرژی و آشفتگی انرژی جنبشی با استفاده از داده فرکانس بالا برای برنامه های کاربردی انرژی بادی

عنوان انگلیسی
Analysis of energy dissipation and turbulence kinetic energy using high frequency data for wind energy applications
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
56615 2016 9 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Wind Engineering and Industrial Aerodynamics, Volume 151, April 2016, Pages 137–145

ترجمه کلمات کلیدی
انرژی بادی؛ آشوب؛ اتلاف؛ اطلاعات فرکانس بالا - چگالی طیفی؛ همبستگی
کلمات کلیدی انگلیسی
Wind energy; Turbulence; Dissipation; High frequency data; Spectral density; Cross-correlation
پیش نمایش مقاله
پیش نمایش مقاله  تجزیه و تحلیل اتلاف انرژی و آشفتگی انرژی جنبشی با استفاده از داده فرکانس بالا برای برنامه های کاربردی انرژی بادی

چکیده انگلیسی

An algorithm was developed to detect delay times in the turbulence kinetic energy (TKE) and the energy dissipation rate ε on a continuous basis (thereby identifying the highest cross-correlation coefficients between them). The Kolmogorov theory in the microscale is applied to calculate the energy dissipation rate ε through the identification of the inertial subrange. We illustrate how the variations in these two parameters happen simultaneously at all times, but indicate a time delay in those variations. The time scale in the variations of both parameters was determined and it is close to the time the air takes to circulate between the surface and the top of the atmosphere’s mixed layer. High correlation coefficients are found in the three site studies from 4 am to 8 am, and from 8 pm to 12 pm. The cross-correlation function also determines delay time scales in the range of 10–20 min. The energy dissipation rate can be calculated to characterize wind variability in a particular site that might affect the performance of a wind turbine. The autocorrelation function of the TKE was also calculated to illustrate how diurnal variations can be more intense in one site than in another one. With these results, more information is generated that can be incorporated into the wind turbine’s control system routines to improve its response under wind turbulence variations.