# Can LiDARs Replace Meteorological Masts in Wind Energy?
## 4 Wind Resource, Power and Load Comparison
This is because the number of 10-min average wind speed data for the former is 47,046, while that for the latter is only 39,996. However, most of the extra data from the sonic anemometer was measured during lower wind speeds which were below the cut-in wind speed for the reference turbine, and consequently they have minimum effect on the overall power output estimation. As presented in Figure 10, the LiDAR-measured turbulence intensities are larger than those measured by the sonic anemometer. To quantify the effect of this difference on wind turbine fatigue load estimation, we perform aeroelastic simulations of the NREL 5-MW wind turbine subjected to inflow turbulence corresponding to the sonic anemometer and LiDAR measurements. The simulations are performed using OpenFAST, an open-source aero-hydro-servo-elastic w  
Figure 17: Power distribution of the site for the NREL 5-MW wind turbine. (**a**) Histogram of frequency of occurrence of power values estimated from the measured wind speed; (**b**) Wind turbine power duration curve. tool developed by NREL [25]. The inflow turbulent flow field required by OpenFAST is generated using TurbSim [37]. The simulations are carried out for 12 sets of wind speeds (between 3 and 24 m/s) and turbulence intensities, each for the sonic anemometer and the LiDAR. To this end, the turbulence intensity for each wind speed is set to the values measured by the sonic anemometer and the LiDAR (cf. Figure 10). The simulation results are processed for fatigue load analysis using MLife [38]. Table 2 describes the simulation cases and major parameters. These analyses essentially correspond to design load case (DLC) 1.2 in the IEC standard [30], which requires fatigue analysis during normal power production mode, with a normal turbulence model and wind speeds between the cut-in and the cut-out speeds. Note that we have not considered yaw misalignment or multiple random seeds for generating turbulent wind field. Figure 18 shows the damage equivalent loads (DELs) for the blade root and the tower base bending moment obtained from the simulations. DEL is widely used approach for evaluating overall fatigue damage that a wind turbine may experience for the given wind speed and turbulence distribution. The distribution of DELs for the 10-min time-history of loads as a function of mean wind speed are shown in Figure 18a,b. The DEL of the blade root in-plane bending moment and out-of plane bending moment for the sonic anemometer and the LiDAR are close. Only for \(\overline{V}=7\) m/s, the out-of-plane bending moment is about 22% lower for the LiDAR. Similarly, the DEL of the tower base bending moment for the sonic anemometer and the LiDAR also show similar tendency. The differences are larger for the fore-aft bending moment, with the largest difference at \(\overline{V}=17\) m/s where the DEL for the LiDAR measurements is 21% larger than that for the sonic anemometer measurements. Comparison of the lifetime DELs (20 years) is shown in Figure 18c. The lifetime DELs are computed using the Weibull wind speed distributions for both the devices (cf. Figure 16). Agreement of the lifetime DEL for the blade root bending moments is good for the sonic anemometer and the LiDAR wind field distributions. The LiDAR wind speed overestimate the in-plane and out-of-plane lifetime DEL by 2% and 6% respectively. However, for the tower base moment, the differences are slightly larger. When the LiDAR wind speed distribution is used, side-to-side and fore-aft lifetime DELs are overestimated by about 13% and 11% respectively. However, the difference may also be because of performing only one simulation for each wind speed. It is recommended to perform around six simulations for each wind speed with varying random seeds for generating wind flow fields, and then to take the ensemble of those simulations.