Analysis of energy dissipation and turbulence kinetic energy using high frequency data for wind energy applications

• We used High Frequency Data of wind speed to cross-correlate the turbulence kinetic energy and the energy dissipation rate.
• We establish that the changes between those parameters do not necessarily happen at the same time.
• We used the Kolmogorov turbulence theory in the microscale to estimate the energy dissipation rate.
• We proposed our method to anticipate changes in the turbulence that leads to estimation of wind data dispersion.
• We propose to use our results as an alternative source of information to better control wind turbines.

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.