The first part of the present study investigated the relationship among the number of yaw gear and motor failures and turbulence intensity (TI) at all the wind turbines under investigation with the use of in situ data. The investigation revealed that wind turbine #7 (T7), which experienced a large number of failures, was affected by terrain-induced turbulence with TI that exceeded the TI presumed for the wind turbine design class to which T7 belongs. Subsequently, a computational fluid dynamics (CFD) simulation was performed to examine if the abovementioned observed wind flow characteristics could be successfully simulated. The CFD software package that was used in the present study was RIAM-COMPACT, which was developed by the first author of the present paper. RIAM-COMPACT is a nonlinear, unsteady wind prediction model that uses large-eddy simulation (LES) for the turbulence model. RIAM-COMPACT is capable of simulating flow collision, separation, and reattachment and also various unsteady turbulence–eddy phenomena that are caused by flow collision, separation, and reattachment. A close examination of computer animations of the streamwise (x) wind velocity revealed the following findings: As we predicted, wind flow that was separated from a micro-topographical feature (micro-scale terrain undulations) upstream of T7 generated large vortices. These vortices were shed downstream in a nearly periodic manner, which in turn generated terrain-induced turbulence, affecting T7 directly. Finally, the temporal change of the streamwise (x) wind velocity (a non-dimensional quantity) at the hub-height of T7 in the period from 600 to 800 in non-dimensional time was re-scaled in such a way that the average value of the streamwise (x) wind velocity for this period was 8.0 m/s, and the results of the analysis of the re-scaled data were discussed. With the re-scaled full-scale streamwise wind velocity (m/s) data (total number of data points: approximately 50,000; time interval: 0.3 s), the time-averaged streamwise (x) wind velocity and TI were evaluated using a common statistical processing procedure adopted for in situ data. Specifically, 10-min moving averaging (number of sample data points: 1932) was performed on the re-scaled data. Comparisons of the evaluated TI values to the TI values from the normal turbulence model in IEC61400-1 Ed.3 (2005) revealed the following: Although the evaluated TI values were not as large as those observed in situ, some of the evaluated TI values exceeded the values for turbulence class A, suggesting that the influence of terrain-induced turbulence on the wind turbine was well simulated.

In the present study, we perform numerical simulations considering various stable atmospheric conditions for a small-scale simple topography. Based on the obtained simulation results, we visualize the flow field and discuss drastic changes in the flow patterns. A flow pattern similar to the potential flow suddenly appears around an isolated hill as the stability increases, regardless of the inclination angle of the hill. We show that a critical Richardson number clearly exists. Furthermore, the effect of stable stratification on the evaluation of power generation is shown for typical complex terrain. We evaluate the capacity factor (%) of a 2 MW large wind turbine based on one-year virtual mast data and consider the effect of stable stratification. It is shown, in the case of stable stratification, that the capacity factor is 2.775 times greater than that under neutral stratification.

To visualize and characterize the unsteady properties of the wake trailing behind a wind turbine, we propose a novel numerical methodology in this study. Through a wind-tunnel experiment using a smoke generator, we succeeded in visualizing a compact-type small-scale wind turbine wake using continuous scalar plumes from a single point source. Next, to simulate the above wind-tunnel experiment, we proposed a method using the passive scalar advection–diffusion equation based on a high-fidelity large-eddy simulation (LES). The actuator-line method (ALM) was adopted for the wind turbine model. We succeeded in qualitatively reproducing the wake visualization experiment in the wind tunnel described above and verified the effectiveness of the proposed numerical visualization method. To evaluate the characteristics of wakes generated by wind turbines in more detail, we conducted a quantitative comparison using a disk-shaped volume source with the same size as the swept area, set behind the wind turbine model. The results indicated that the non-dimensional time-averaged passive scalar profile in the near- and far-wake regions qualitatively matched the shape of the stream-wise velocity profile, despite having opposite signs. In other words, it was shown that if a disk-shaped volume source with the same size as the swept area is placed just behind the wind turbine, it is possible to accurately predict the behavior of the wind turbine wake. Finally, to validate the proposed method for wind farm wake management, we performed a visualization of wake flows in a virtual offshore wind farm consisting of 12 wind turbines with short separation distances. Through detailed comparison of two types of numerical results with different wind directions, we showed that the proposed method can effectively demonstrate the range of spatial influence of the wakes formed by the wind turbine with special attention.

For this study, the annual frequency of atmospheric stability and the effects of topography were investigated, using ERA5 data and data from wind observation masts installed at four locations on an island for a site under development, where bidding will soon begin. As a result, we found that a variety of atmospheric stabilities appeared at the site, and that the annual average events were not neutral but, instead, unstable. Moreover, the deviation from neutral varied depending on wind direction and the wind speed varied greatly, depending on the mast position and wind direction. Additionally, it was necessary to reproduce the wind flow separation due to topography, in order to predict the wind conditions of wind turbines located close to the island. The accuracy of the airflow simulation by large eddy simulation was validated using the mast-to-mast wind speed ratio. For simulations, we used the commercial software RIAM-COMPACT, which has been widely used in Japan, as it allows the atmospheric stability to be freely set. As a result, we found that the accuracy could be improved by refining the inflow wind direction and taking the average of the results calculated under several atmospheric stability conditions.

Accurate wind estimation is essential for wind resource assessment. In this study, using scanning lidar measurements and high-resolution WRF simulations from two nearshore areas in Japan, we developed two extensions of the Temporal Correction (TC) method, which corrects wind fields generated by the Weather Research and Forecasting (WRF) model using on-site measurements. First, when using a single measurement point for correction, we derived two empirical formulas to predict appropriate correction coefficients based on reference–target correlation coefficients of wind speed obtained from WRF simulations and developed a method (TC-pred) using these formulas. TC-pred was shown to have higher wind speed estimation accuracy and a broader range of applicability than the conventional TC method. Next, we extended the TC-pred method to allow the use of multiple measurement points as references by introducing a weighting formula for each reference point. Wind speed estimation accuracy improved as the number of reference points increased, primarily because the probability of including reference points with high reference–target correlation coefficients increased. This suggests that it is effective for the suppression of wind estimation uncertainty to determine measurement layout such that the correlation coefficient between at least one reference point and each target point in the target area exceeds a certain value.

Accurate wake prediction under realistic atmospheric conditions is essential for wind-farm energy assessment. This study investigated the influence of wind-direction variability on single-turbine wake prediction using SCADA and vertical-LiDAR measurements from two full-scale coastal wind turbines. First, the effects of inflow conditions on wake-induced velocity deficits were examined for sea-wind and land-wind cases. Predictions obtained by applying the NOJ model to 10 min mean data were then compared with those based on 0.25 Hz line-of-sight data. Accounting for instantaneous wind-direction variations improved agreement with the measured wind-speed ratios, reducing the mean bias error from −0.075 to 0.011 and the root-mean-square error from 0.156 to 0.062. Based on these findings, a rotor-averaging method was developed using the expected overlap between the wake region and the downstream rotor, calculated from the probability distributions of wind direction and its standard deviation. The proposed method reduced the peak overlap near the wake center and extended the wake influence over a wider directional range. Its influence was limited in the near-wake region but increased with downstream distance. Comparison with full-scale measurements showed that the proposed approach improved power-ratio predictions, achieving the lowest mean bias error and root-mean-square error of 0.026 and 0.047, respectively, under land-wind conditions. These results demonstrate that probabilistic rotor overlap provides a simple method for incorporating wind-direction variability into engineering wake models.