UBC Theses and Dissertations
High frequency data analysis for wind energy applications Escalante Soberanis, Mauricio Alberto
High frequency data (HFD) of three site studies in different geographic locations were analyzed to reproduce the power spectrum illustrated by Van der Hoven in 1957. His work represents the basis of wind energy standards such as averaging and variability in the frequency domain. The results presented in this thesis unveil discrepancies with Van der Hoven’s approach. A major eddy-energy peak is illustrated at a period of 2 days and a smaller eddy-energy peak contribution at frequencies higher than the region known as the spectrum gap. The variance in the microscale region was calculated by integrating the Power Spectral Density (PSD) over the corresponding range of frequencies. The economic value of this energy variance based on the turbulence kinetic energy of the wind data set is calculated. It is also concluded that, given the results of the study, HFD analysis in the frequency domain uncovers eddy-energy peaks that determine energy fluctuations in the short and long terms. 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 turbulence order is applied to calculate the energy dissipation rate ε through the identification of the inertial subrange. The time scale in the variations of both parameters was successfully calculated 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 4am to 8am, and from 8pm to 12pm. The cross-correlation function also determines delay time scales in the range of 10-20 minutes and approximately 2 hours. The energy dissipation rate can be calculated to characterize wind variability in a particular site that might affect the performance of a wind turbine. With these results, more information is generated that can be used in the wind turbine’s control system routines to improve its response under wind turbulence variations.
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