Rotor Equivalent Wind Speed Research Articles

Belief Reliability Modeling Method for Wind Farms Considering Two-Directional Rotor Equivalent Wind Speed

Compared to conventional energy sources, wind power is a clean energy source with high intermittence and uncertainty. As a system that converts wind energy into electricity, wind farms inevitably face severe reliability issues. In this paper, based on reliability theory, a new reliability modeling method for wind farms is proposed. Firstly, a belief reliability model for wind farms is constructed. Then, a power generation model based on two-directional rotor equivalent wind speed is established to represent the wind farm performance in the belief reliability model. Finally, several numerical studies are conducted to verify the power generation model under different wind speeds and directions, to demonstrate the belief reliability model with different levels of uncertainty, and to compare the belief reliability considering two-directional rotor equivalent wind speed with other methods.

Investigation of wind veer characteristics on complex terrain using ground-based lidar

The wind direction shift with height significantly influences wind turbine performance, particularly in relation to terrain conditions. In this work, wind conditions at 12 measurement heights ranging from 40 m to 200 m using a ground lidar, Windcube V2, installed on a 16 m tall building were analysed to examine the characteristics of wind veer angles in complex terrain. The measurement campaign was carried out from January 1st to December 31st, 2022, in the southeastern part of South Korea. The terrain complexity around the ground lidar system was evaluated using the ruggedness index (RIX), whose result was 14.06 percent corresponding to complex terrain. The ground lidar measurements were compared with mesoscale data, EMD-WRF South Korea, for the data accuracy check. Wind veer frequencies and wind roses were derived to identify directional shifts with height. Furthermore, diurnal, monthly, and seasonal variations of wind veer characteristics were analysed. Wind shear exponent factor (WSE) and turbulence kinetic energy (TKE) were calculated, and wind veer profiles were constructed based on these parameters. The relative errors of wind speeds were analysed for rotor equivalent wind speed (REWS) and hub height wind speed (HHWS), with REWS with wind veer correction, REWSveer, as a reference. Additionally, atmospheric stability conditions were classified using WSE and TKE, and the vertical changes in wind veering were analysed according to the stability conditions. The findings reveal lower wind speeds exhibited larger wind veer values and fluctuations. The relative errors for the REWS and the HHWS were 0.04 % and 0.20 % on average, respectively. The study demonstrates that terrain conditions significantly impacted wind veer angles at heights below 100 m, whereas the influence diminished with increasing height above 100 m. The results could be helpful for wind farm developers to make decisions on the siting as well as the hub height of wind turbines on complex terrain

Rotor equivalent wind speed prediction based on mechanism analysis and residual correction using Lidar measurements

Efficient power generation and reduced operational cost are crucial to make wind energy a successful market player. To achieve satisfactory performance in wind turbine technology, it is essential to accurately predict the rotor equivalent wind speed (REWS). Since traditional wind transducers are difficult to measure multiple-point wind speed, related researches on predicting REWS are lacking. In this paper, a hybrid REWS prediction framework is proposed, which combines mechanism analysis and data-driven technique, aided by Lidar measurements. The framework employs the mechanism modeling and time delay analysis to achieve preliminary REWS prediction, but the accuracy is restricted by the quantity of Lidar measurement points. To enhance the prediction accuracy, a residual prediction model based on the data-driven technique is created to correct the mechanism prediction. Specifically, a novel combined modeling approach based on empirical mode decomposition and sample entropy algorithms is presented. Through reconstructing the input data of the combined model into high, middle, and low frequency components and exploring the prediction laws of different frequency components obtained by typical machine learning models, the combined residual prediction model is established. Additionally, an enhanced whale optimization algorithm named WOAmM is presented to optimize the parameters of the combined model. The proposed method is validated through a four-beam Lidar measurement scenario simulated by BLADED software. The experimental results indicate that the combined model outperforms each single model and the mechanism prediction, and improves the REWS prediction accuracy by up to75.04%, 60.92%, 51.85%, and 57.52% in the four datasets, respectively. The proposed method could facilitate the utilization of Lidar in improving the performance of wind turbines.

Analysis of Vertical Wind Shear Effects on Offshore Wind Energy Prediction Accuracy Applying Rotor Equivalent Wind Speed and the Relationship with Atmospheric Stability

If the wind speed that passed through a wind turbine rotor disk area is constant, the hub height wind speed (HHWS) could be representative of the wind speed over the rotor disk area. However, this assumption cannot be applied to the large wind turbine, because of the wind shear effect by atmospheric stability. This is because the hub height wind speed cannot represent the vertical wind shear effect from the aerodynamics characteristic on the wind turbine. Using SCADA and offshore LiDAR observation data of the Anholt offshore wind farm, it is investigated whether the rotor equivalent wind speed (REWS) introduced in IEC61400-12-1 can contribute to the improvement of power output forecasting accuracy. The weighted value by separated sector area and vertical wind shear effect by difference between heights can explain the role of energy flux and atmospheric stability on the exact wind energy calculation. The commercial CFD model WindSim is used to calculate power production according to the HHWS and the REWS, and to compare them with the actual AEP of the local wind farm. The classification of atmospheric stability is carried out by Richardson number, which well represents the thermal and physical properties of the atmosphere below the atmospheric boundary layer, along with the wind shear coefficient and turbulence intensity. When atmospheric stability was classified by each stability index, the REWS-based predicted power output was sometimes more accurate than HHWS, but sometimes inferior. However, in most cases, using the REWS, it was possible to calculate an estimate closer to the actual power output. Through the results of this study, it is possible to provide a rationale for which method, REWS or HHWS, can more accurately calculate the expected power output and effectively derive the economic feasibility of the project by identifying the characteristics of local atmospheric stability before the wind farm project.

Evaluation of a Wind Field Parametrisation Methodology for Lidar-Measured Wind Turbine Inflow

There are different methods for the reconstruction of wind fields from lidar measurements, either expressing the wind condition as a set of global parameters, or by 2D or 3D wind vector reconstruction. In this work, we propose a four-parameter wind field parametrisation model as a further development of the three-parameter model introduced by Kapp and Kühn (2014, 2017), which includes a rotor-equivalent wind speed, the horizontal inclination angle of the wind with respect to the measurement plane, a wind shear exponent and a turbulence parameter. In our approach, the turbulence parameter is based on the spatial variations with respect to the rotor-equivalent wind speed over the measurement plane. The model is evaluated by applying it on a spatially and temporally highly resolved SpinnerLidar data set, measured from the nacelle of a wind turbine situated in flat terrain, with a met mast data set for comparison. First results show a good correlation for the rotor-equivalent wind speed and the horizontal inclination, however, a poor fit of the exponential shear. The turbulence parameter follows a physically different definition than the common definition of turbulence intensity, however, the analysis shows that they are weakly correlated with each other. Overall, the model shows potential, but must be further evaluated, e.g. with additional data or simulated lidar measurements within an LES wind field.

A refined wind farm parameterization for the weather research and forecasting model

In the original wind farm parameterization of the Weather Research and Forecasting model, the standard constant air density is a source of errors in power output estimation, and the hub-height wind speed is inappropriate to represent the wind speed through the whole rotor area. To overcome these issues, a refined model incorporating variable air density is developed, and the hub-height wind speed is replaced with the rotor equivalent wind speed (REWS) in this study. To keep consistency, the power coefficients corresponding to the REWS under variable air density conditions are modified. The influences of atmospheric stability are investigated to evaluate the performance of wind power estimation between the original and the current refined models. The refined wind farm parameterization has few impacts on the flow field, however, the average power generation becomes more scattered and is notably decreased by nearly 7% due to the reduced variable air density. Meanwhile, the REWS slightly increases the power estimation by around 1.1%. Both REWS and variable air density can decrease the estimation uncertainty of the average power induced by complex atmospheric stability. Moreover, atmospheric stability plays a more significant role in power outputs, and the refined model can capture a stronger correlation between the atmospheric condition and wind power generation.

Improvement of wind power prediction from meteorological characterization with machine learning models

To mitigate uncertainties in wind resource assessments and to improve the estimation of energy production of a wind project, this work uses a decision tree machine learning model to assess the effectiveness of hub-height wind speed, rotor-equivalent wind speed, and lapse rate as variables in power prediction. Atmospheric data is used to train regression trees and correlate the power outputs to wind profiles and meteorological characteristics to be able to predict power responses according to physical patterns. The decision tree model was trained for four vertical wind profile classifications to showcase the need for multiple calculations of wind speed at various levels of the rotor layer. Results indicate that when compared to traditional power curve methods, the decision tree combining rotor-equivalent wind speed and lapse rate improves prediction accuracy by 22% for the given data-set, while also proving to be the most effective method in power prediction for all classified vertical wind profile types. Models incorporating lapse rate into predictions performed better than those without it, showing the importance of considering atmospheric criteria in wind power prediction analyses.

Experimental investigation on power performance testing using nacelle lidar measurements over excavated terrain

Abstract In order to find out the usability of the nacelle mounted light detection and ranging (lidar) for power performance testing under the condition of excavated terrain, an experimental investigation was carried out at the DBK wind farm on Jeju Island, South Korea. A nacelle lidar installed on a 2 MW test wind turbine was used with an 80 m tall met mast and a ground-based lidar for measuring wind conditions. The measurement sector was calculated for the site, which was reduced to the analysed sector for finding out the influence of the excavated landfill. Since the excavated landfill was located near the test wind turbine, the excavated terrain was evaluated according to International Electrotechnical Commission (IEC) 61400-12-1, 2nd edition and the power curve was drawn using met mast wind speeds at the hub height, which was compared with that from the turbine manufacturer. The excavated terrain was further analysed using the Computational Fluid Dynamics (CFD) technique to determine whether it has an influence on wind flow. Then, the wind speed data measured by cup anemometers on the met mast, the ground-based lidar and the nacelle mounted lidar were compared through the linear regression method. The rotor equivalent wind speed (REWS) was calculated using met mast and ground lidar wind speeds according to the IEC 61400-12-1, 2nd edition. The power curves were obtained by applying the REWS and the nacelle lidar measurements. In addition, the relative errors of the power outputs were estimated and further annual energy productions (AEPs) were analysed by applying the Rayleigh wind speed distribution to the power curves. As a result, the effect of the excavated terrain on the power curve was very small. The power curves using the nacelle lidar measurements were mostly the same as the power curve using the REWS.

Rotor equivalent wind speed calculation method based on equivalent power considering wind shear and tower shadow

Wind speed spatial variation in the whole wind wheel rotation plane caused by wind shear and tower shadow effect becomes more obvious with the increase of rotor diameter and tower height, thus the hub height wind speed is not sufficient to represent the actual wind speed effect and the rotor equivalent wind speed should be used. However, the existing rotor equivalent wind speed calculation models do not consider wind turbine operation characteristics, which cannot match the actual power control effect, thus ignoring the power loss caused by actual power control deviation. To address this problem, a rotor equivalent wind speed calculation method based on equivalent power is proposed in this paper, which comprehensively considers wind shear, tower shadow and wind turbine operation characteristics. The rationality of the proposed model was qualitatively evaluated from different power control stages of wind turbine and the validity was quantitatively verified from the aspect of theoretical power calculation. The results demonstrated that the proposed model can effectively reflect the variation law of rotor equivalent wind speed under the actual control effect of different power control stages, which contains the power loss caused by actual power control deviation, thus improving the theoretical power calculation accuracy.

Characterization of offshore vertical wind shear conditions in Southern New England

AbstractVertical wind shears could have a significant effect on the energy produced by a wind turbine and on its loads. Although the development of several wind farms has been planned on the East Coast of the United States, there are no studies that characterize the vertical wind shear over this area. This study focuses on characterizing wind shears in the marine boundary layer in Southern New England and along the East Coast of the United States. The analysis looks at the statistical distribution of vertical wind shear values and at their associated meteorological conditions. The analysis relies on remote‐sensing wind measurements and other meteorological data recorded at the Woods Hole Oceanographic Institution Air–Sea Interaction Tower located 3 km to the South of Martha's Vineyard, together with buoy measurements and ERA5 reanalysis data. This work shows that large vertical wind shear values (>0.05 m/s/m) calculated using wind measurements at 60 and 53 m were often observed (≈25.3% of all the valid wind profiles analyzed) for South‐Westerly winds within a range of positive bulk Richardson numbers 0–0.1. These large‐shear values are the result of the presence of a strong high‐pressure system (Bermuda‐Azores High) over the North Atlantic basin and low pressures over land, which result in warm Southerly winds flowing over the cold waters of the Labrador current. The power density computed considering the vertical wind shear by means of the rotor equivalent wind speed is 5.5% smaller than that considering wind speed measurements at 110 m only.

How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

Abstract. Most megawatt-scale wind turbines align themselves into the wind as defined by the wind speed at or near the center of the rotor (hub height). However, both wind speed and wind direction can change with height across the area swept by the turbine blades. A turbine aligned to hub-height winds might experience suboptimal or superoptimal power production, depending on the changes in the vertical profile of wind, also known as shear. Using observed winds and power production over 6 months at a site in the high plains of North America, we quantify the sensitivity of a wind turbine's power production to wind speed shear and directional veer as well as atmospheric stability. We measure shear using metrics such as α (the log-law wind shear exponent), βbulk (a measure of bulk rotor-disk-layer veer), βtotal (a measure of total rotor-disk-layer veer), and rotor-equivalent wind speed (REWS; a measure of actual momentum encountered by the turbine by accounting for shear). We also consider the REWS with the inclusion of directional veer, REWSθ, although statistically significant differences in power production do not occur between REWS and REWSθ at our site. When REWS differs from the hub-height wind speed (as measured by either the lidar or a transfer function-corrected nacelle anemometer), the turbine power generation also differs from the mean power curve in a statistically significant way. This change in power can be more than 70 kW or up to 5 % of the rated power for a single 1.5 MW utility-scale turbine. Over a theoretical 100-turbine wind farm, these changes could lead to instantaneous power prediction gains or losses equivalent to the addition or loss of multiple utility-scale turbines. At this site, REWS is the most useful metric for segregating the turbine's power curve into high and low cases of power production when compared to the other shear or stability metrics. Therefore, REWS enables improved forecasts of power production.

Comparison of wind turbine power curves using cup anemometer and pulsed doppler light detection and ranging systems

To clarify the difference in the wind turbine power curves obtained by cup anemometer and light detection and ranging (LIDAR) measurements, an investigation was experimentally performed in the Haengwon wind farm on Jeju Island, South Korea. A LIDAR mounted on the nacelle of a 1.5-MW test wind turbine was used with a met mast and a ground LIDAR positioned at a distance of 2.5 times the rotor diameter from the turbine. The wind speed data obtained by each instrument were compared through linear regression analysis. The rotor equivalent wind speed (REWS) was derived from a cup anemometer and ground LIDAR measurements in accordance with the International Electrotechnical Commission (TEC) standard 61400-12-1, 2nd edition. The scatter plots were drawn using the wind data measured by each instrument and compared in terms of the standard deviation. The power curves drawn from the REWS and nacelle LIDAR measurements were compared with that from the cup anemometer measurements according to IEC 61400-12-1, 1st edition. To quantitatively identify the difference in the power curves, the relative error was calculated using the cup anemometer power curve as a reference. Consequently, the relative error for the power output in the bin interval of 0.5 m/s before the rated wind speed was high, whereas that after the rated was close to 0 %. The relative errors for the power curve from the REWS and the nacelle LIDAR measurements were −0.37 % and 3.01 % on average, respectively.

Do we really need rotor equivalent wind speed?

AbstractThe use of the rotor equivalent wind speed for determination of power curves and annual energy production for wind turbines is advocated in the second edition of the IEC 61400‐12‐1 standard. This requires the measurements of wind speeds at different heights, for which remote sensing equipment is recommended in addition to meteorological masts. In this paper, we present a theoretical analysis that shows that the relevance of the rotor equivalent wind speed method depends on turbine dimensions and wind shear regime. For situations where the ratio of rotor diameter and hub height is smaller than 1.8, the rotor equivalent wind speed method is not needed if the wind shear coefficient at the location of the wind turbine has a constant value between −0.05 and 0.4: in these cases, the rotor equivalent wind speed and the wind speed at hub height are within 1%. For complex terrains with high wind shear deviations are larger. The effect of non‐constant wind shear exponent, ie, different wind shear coefficients for lower and upper half of the rotor swept area especially at offshore conditions is limited to also about 1%.

Incorporation of the Rotor-Equivalent Wind Speed into the Weather Research and Forecasting Model’s Wind Farm Parameterization

Abstract Wind power installations have been increasing in recent years. Because wind turbines can influence local wind speeds, temperatures, and surface fluxes, weather forecasting models should consider their effects. Wind farm parameterizations do currently exist for numerical weather prediction models. They generally consider two turbine impacts: elevated drag in the region of the wind turbine rotor disk and increased turbulent kinetic energy production. The wind farm parameterization available in the Weather Research and Forecasting (WRF) Model calculates this drag and TKE as a function of hub-height wind speed. However, recent work has suggested that integrating momentum over the entire rotor disk [via a rotor-equivalent wind speed (REWS)] is more appropriate, especially for cases with high wind shear. In this study, we implement the REWS in the WRF wind farm parameterization and evaluate its impacts in an idealized environment, with varying amounts of wind speed shear and wind directional veer. Specifically, we evaluate three separate cases: neutral stability with low wind shear, high stability with high wind shear, and high stability with nonlinear wind shear. For most situations, use of the REWS with the wind farm parameterization has marginal impacts on model forecasts. However, for scenarios with highly nonlinear wind shear, the REWS can significantly affect results.

Wind Profile and Power Performance Measurements Using a Nine-beam Nacelle Lidar

Light detection and ranging (lidar) is a remote sensing method that can measure the average wind speed of larger volumes compared with conventional wind sensors like cup anemometers. Two-beam nacelle lidar is the most representative remote sensor for wind inflow measurement. The remote sensors and conventional wind sensors cannot measure the wind profile it influences the power generation output. Wind data were measured for a 300-kW wind turbine using both a nacelle anemometer installed in it and a nine-beam nacelle lidar, and then compared. The authors were able to obtain a wind profile by using a nine-beam nacelle lidar, which can irradiate the laser in nine directions and observe the inside of the target volume at a higher resolution than the two-beam version. Unlike nacelle lidars released in the past, the recent nine-beam version allowed also wind profile observations. Since the power output of the turbine was influenced by the wind profile, its relationship with the rotor equivalent wind speed (REWS) was clarified.

Classifying rotor‐layer wind to reduce offshore available power uncertainty

AbstractRotor‐layer wind resource and turbine available power uncertainties prior to wind farm construction may contribute to significant increases in project risk and costs. Such uncertainties exist in part due to limited offshore wind measurements between 40 and 250 m and the lack of empirical methods to describe wind profiles that deviate from a priori, expected power law conditions. In this article, we introduce a novel wind profile classification algorithm that accounts for nonstandard, unexpected profiles that deviate from near power law conditions. Using this algorithm, offshore Doppler wind lidar measurements in the Mid‐Atlantic Bight are classified based on goodness‐of‐fit to several mathematical expressions and relative speed criteria. Results elucidate the limitations of using power law extrapolation methods to approximate average wind profile shape/shear conditions, as only approximately 18% of profiles fit well with this expression, while most consist of unexpected wind shear. Further, results demonstrate a relationship between classified profile variability and coastal meteorological features, including stability and offshore fetch. Power law profiles persist during unstable conditions and relatively weaker northeasterly flow from water (large fetch), whereas unexpected classified profiles are prevalent during stable conditions and stronger southwesterly flow from land (small fetch). Finally, the magnitude of the discrepancy between hub‐height wind speed and rotor equivalent wind speed available power estimates varies by classified wind‐profile type. During unexpected classified profiles, both a significant overprediction and underprediction of hub‐height wind available power is possible, illustrating the importance of accounting for site‐specific rotor‐layer wind shear when predicting available power.

Study on methods to determine rotor equivalent wind speed to increase prediction accuracy of wind turbine performance under wake condition

A downstream wind turbine located within the reach of the wake region of an upstream wind turbine experiences a decrease in power output due to wake effects. For this reason, when designing a wind farm, various engineering wake models are used to predict the power deficit and wind farm layout is designed in the optimal way to minimize the wake losses. Generally, in the process of calculating the loss of wind farm AEP, in most cases the single point-measured wind speed is used. However, this results in an error when predicting the loss of AEP under wake conditions. When predicting the AEP of a wind turbines affected by wakes, the rotor equivalent wind speed (REWS), which considers the effect of wake wind shear, should be applied. This research examined REWSpower converted from the power output of a wind turbine to demonstrate the need of rotor equivalent wind speed under upstream turbine's wake condition and furthermore suggested a method to calculate REWSspws using the nacelle-measured wind speed. By analyzing 48months collected data of Supervisory Control and Data Acquisition (SCADA) system from a wind farm, error percentages among REWSpower, REWSspws, and the nacelle-measured wind speed were compared.

A methodology for the design and testing of atmospheric boundary layer models for wind energy applications

Abstract. The GEWEX Atmospheric Boundary Layer Studies (GABLS) 1, 2 and 3 are used to develop a methodology for the design and testing of Reynolds-averaged Navier–Stokes (RANS) atmospheric boundary layer (ABL) models for wind energy applications. The first two GABLS cases are based on idealized boundary conditions and are suitable for verification purposes by comparing with results from higher-fidelity models based on large-eddy simulation. Results from three single-column RANS models, of 1st, 1.5th and 2nd turbulence closure order, show high consistency in predicting the mean flow. The third GABLS case is suitable for the study of these ABL models under realistic forcing such that validation versus observations from the Cabauw meteorological tower are possible. The case consists on a diurnal cycle that leads to a nocturnal low-level jet and addresses fundamental questions related to the definition of the large-scale forcing, the interaction of the ABL with the surface and the evaluation of model results with observations. The simulations are evaluated in terms of surface-layer fluxes and wind energy quantities of interest: rotor equivalent wind speed, hub-height wind direction, wind speed shear and wind direction veer. The characterization of mesoscale forcing is based on spatially and temporally averaged momentum budget terms from Weather Research and Forecasting (WRF) simulations. These mesoscale tendencies are used to drive single-column models, which were verified previously in the first two GABLS cases, to first demonstrate that they can produce similar wind profile characteristics to the WRF simulations even though the physics are more simplified. The added value of incorporating different forcing mechanisms into microscale models is quantified by systematically removing forcing terms in the momentum and heat equations. This mesoscale-to-microscale modeling approach is affected, to a large extent, by the input uncertainties of the mesoscale tendencies. Deviations from the profile observations are reduced by introducing observational nudging based on measurements that are typically available from wind energy campaigns. This allows the discussion of the added value of using remote sensing instruments versus tower measurements in the assessment of wind profiles for tall wind turbines reaching heights of 200 m.

Improving the Accuracy of Wind Turbine Power Curve Validation by the Rotor Equivalent Wind Speed Concept

The measurement of the wind speed at hub height is part of the current IEC standard procedure for the power curve validation of wind turbines. The inherent assumption is thereby made that this measured hub height wind speed sufficiently represents the wind speed across the entire rotor area. It is very questionable, however, whether the hub height wind speed (HHWS) method is appropriate for rotor sizes of commercial state-of-the-art wind turbines. The rotor equivalent wind speed (REWS) concept, in which the wind velocities are measured at several different heights across the rotor area, is deemed to be better suited to represent the wind speed in power curve measurements and thus results in more accurate predictions of the annual energy production (AEP) of the turbine. The present paper compares the estimated AEP, based on HHWS power curves, of two different commercial wind turbines to the AEP that is based on REWS power curves. The REWS was determined by LiDAR measurements of the wind velocities at ten different heights across the rotor area. It is shown that a REWS power curve can, depending on the wind shear profile, result in higher, equal or lower AEP estimations compared to the AEP predicted by a HHWS power curve.

Demonstrating the effect of vertical and directional shear for resource mapping of wind power

AbstractThe use of wind energy is growing around the world, and its growth is set to continue into the foreseeable future. Estimates of the wind speed and power are helpful to assess the potential of new sites for development and to facilitate electric grid integration studies. In the present paper, wind speed and power resource mapping analyses are performed. These resource mappings are produced on a 13 km, hourly model grid over the entire continental USA for the years of 2006–2014. The effects of the rotor equivalent wind speed (REWS) along with directional shear are investigated. The total dataset (wind speed and power) contains ≈152,000 model grid points, with each location containing ≈78,000 hourly time steps. The resource mapping and dataset are created from analysis fields, which are output from an advanced weather assimilation model. Two different methods were used to estimate the wind speed over the rotor swept area (with rotor diameter of 100 m). First, using a single wind speed at hub height (80 m) and, second, the REWS with directional shear. The demonstration study shows that in most locations the incorporation of the REWS reduces the average available wind power. In addition, the REWS technique estimates more wind power production at night and less production in the day compared with the hub height technique; potentially critical for siting new wind turbines and plants. However, the wind power estimate differences are dependent on seasonality, diurnal cycle and geographic location. More research is warranted into these effects to determine the level at which these features are observed at actual wind plants.© 2015 The Authors. Wind Energy published by John Wiley & Sons, Ltd.