Hub-height Wind Speed Research Articles

Effects of the thrust force induced by wind turbine rotors on the incoming wind field: A wind LiDAR experiment

A field experiment was conducted to investigate the effects of the thrust force induced by utility-scale wind turbines on the incoming wind field. Five wind profiling LiDARs and a scanning Doppler pulsed wind LiDAR were deployed in the proximity of a row of four wind turbines located over relatively flat terrain, both before and after the construction of the wind farm. The analysis of the LiDAR data collected during the pre-construction phase enables quantifying the wind map of the site, which is then leveraged to correct the post-construction LiDAR data and isolate rotor-induced effects on the incoming wind field. The analysis of the profiling LiDAR data allows for the identification of the induction zone upstream of the turbine rotors, with an increasing velocity deficit moving from the top tip towards the bottom tip of the rotor. The largest wind speed reduction (about 5%) is observed for convective conditions and incoming hub-height wind speed between cut-in and rated wind speeds. The scanning LiDAR data indicate the presence of speedup regions within the gaps between adjacent turbine rotors. Speedup increases with reducing the transverse distance between the rotors, atmospheric instability (maximum 15%), while a longer streamwise extent of the speedup region is observed under stable atmospheric conditions.

Large Eddy Simulation of wind turbine fatigue loading and yaw dynamics induced by wake turbulence

A coupled Large Eddy Simulation (LES) and aeroelastic code was used to evaluate control responses and fatigue loading of a four-inline wind turbine array. Neutral and unstably stratified atmospheric boundary layers with hub-height wind speeds of 7 and 15 m/s were used for wind farm inflows. These cases operate in different control regions. It was found that for both incoming wind speeds, atmospheric stability has no significant impact on the fatigue loads of the front-row wind turbines. However, stability affected wake characteristics which caused differences in control response and fatigue experienced by downstream turbines. The most distinctive difference was observed at a downstream turbine in the above-rated condition where the shaft torsional load in neutral stability condition was up to 50% higher than the unstable case. A baseline active yaw controller was implemented in the below-rated condition, which caused higher fatigue on turbines in the wake compared to the fixed yaw turbine case, without any power output gain.

Evaluation of coupled wind / wave model simulations of offshore winds in the Mid-Atlantic Bight using lidar-equipped buoys.

Abstract From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long term measurements of hub-height wind speed in the marine environment. We performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) model, to see if simulated hub height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a standalone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled-Ocean-Atmosphere-Wave-Sediment-Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 08 May 2015 and the cold frontal case of 25 Mar 2016, but not the nor-easter of 18 Jan 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 Mar 2016 but worsening them for 08 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the VA buoy, largely due to greater self-consistency between wind and wave fields.

Impacts of offshore wind farms on the atmospheric environment over Taiwan Strait during an extreme weather typhoon event

Wind is one of the cleanest renewable energy resources. Through the “Thousand Wind Turbines Project”, Taiwan is planning to increase the proportion of power generation from renewable energy and has set a target of 5.7 GW for offshore wind by 2025. The effects of future offshore wind farms (OWFs) over the Taiwan Strait on the atmospheric environment have not been evaluated. This study examined the potential effects of proposed OWFs on the atmospheric environment if the OWFs had existed during Tropical Storm Haitang (2017) by using Weather Research and Forecasting (WRF) model. A small set of ensemble simulations was conducted for studying the sensitivity of the ambient conditions in the region to the wind farm locations, the number and density of the turbines, and the initial time of simulations. Following the landfall and northward movement of Tropical Storm Haitang, a series of complex interactions between the typhoon circulation and the wind farm emerged, including small time slots of wake effect and mountain blocking effect. The combination of these rapidly changing OWFs-related effects contributed to a weak reduction in precipitation (− 1.08 mm) and hub-height wind speed (− 0.25 m s−1), as well as minimal warming near the surface (+ 0.13 °C) over southern Taiwan.

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.

Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble

Abstract. To accurately plan and manage wind power plants, not only does the time-varying wind resource at the site of interest need to be assessed but also the uncertainty connected to this estimate. Numerical weather prediction (NWP) models at the mesoscale represent a valuable way to characterize the wind resource offshore, given the challenges connected with measuring hub-height wind speed. The boundary condition and parametric uncertainty associated with modeled wind speed is often estimated by running a model ensemble. However, creating an NWP ensemble of long-term wind resource data over a large region represents a computational challenge. Here, we propose two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup in which a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired long-term period (20 year). We quantify hub-height wind speed boundary condition and parametric uncertainty from the short-term model ensemble as its normalized across-ensemble standard deviation. Then, we develop and apply a gradient-boosting model and an analog ensemble approach to temporally extrapolate such uncertainty to the full 20-year period, for which only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both of the proposed approaches provide accurate estimates of the long-term wind speed boundary condition and parametric uncertainty across the region (R2>0.75), with the gradient-boosting model slightly outperforming the analog ensemble in terms of bias and centered root-mean-square error. At the three offshore wind energy lease areas in the region, we find a long-term median hourly uncertainty between 10 % and 14 % of the mean hub-height wind speed values. Finally, we assess the physical variability in the uncertainty estimates. In general, we find that the wind speed uncertainty increases closer to land. Also, neutral conditions have smaller uncertainty than the stable and unstable cases, and the modeled wind speed in winter has less boundary condition and parametric sensitivity than summer.

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.

Quantifying the Impacts of Land Surface Modeling on Hub-Height Wind Speed under Different Soil Conditions

AbstractWe investigate the impact of three land surface models (LSMs) on simulating hub-height wind speed under three different soil regimes (dry, wet, and frozen) to improve understanding of the physics of wind energy forecasts using the Weather Research and Forecasting (WRF) model. A six-day representative period is selected for each soil condition. The simulated wind speed, surface energy budget and soil properties are compared with the observations collected from the second Wind Forecast Improvement Project (WFIP2). For the selected cases, our simulation results suggest that the impact of LSMs on hub-height wind speed are sensitive to the soil states but not so much to the choice of LSM. The simulated hub-height wind speed is in much better agreement with the observations for the dry soil case than the wet and frozen soil cases. Over the dry soil, there is a strong physical connection between the land surface and hub-height wind speed through near-surface turbulent mixing. Over the wet soil, the simulated hub-height wind speed is less impacted by the land surface due to weaker surface fluxes and large-scale synoptic disturbances. Over the frozen soil, the LSM seems to have limited impact on hub-height wind speed variability due to the decoupling of the land surface with the overlying atmosphere. Two main sources of modeling uncertainties are proposed. The first is the insufficient model physics representing the surface energy budget, especially the ground heat flux, and the second is the inaccurate initial soil states such as soil temperature and soil moisture.

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.

Calibration of wind speed ensemble forecasts for power generation

In the last decades, wind power became the second largest energy source in the EU covering 16% of its electricity demand. However, due to its volatility, accurate short range wind power predictions are required for successful integration of wind energy into the electrical grid. Accurate predictions of wind power require accurate hub height wind speed forecasts, where the state-of-the-art method is the probabilistic approach based on ensemble forecasts obtained from multiple runs of numerical weather prediction models. Nonetheless, ensemble forecasts are often uncalibrated and might also be biased, thus require some form of post-processing to improve their predictive performance. We propose a novel flexible machine learning approach for calibrating wind speed ensemble forecasts, which results in a truncated normal predictive distribution. In a case study based on 100m wind speed forecasts produced by the operational ensemble prediction system of the Hungarian Meteorological Service, the forecast skill of this method is compared with the predictive performance of three different ensemble model output statistics approaches and the raw ensemble forecasts. We show that compared with the raw ensemble, post-processing always improves the calibration of probabilistic and accuracy of point forecasts, and from the four competing methods, the novel machine learning based approach results in the best overall performance.

Large Eddy Simulation of Wind Turbine Fatigue Loading and Yaw Dynamics Induced by Wake Turbulence

A coupled Large Eddy Simulation (LES) and aeroelastic code was used to evaluate control responses and fatigue loading of a four-inline wind turbine array. Neutral and unstably stratified atmospheric boundary layers with hub-height wind speeds of 7 and 15 m/s were used for wind farm inflows. These cases operate in different control regions. It was found that for both incoming wind speeds, atmospheric stability has no significant impact on the fatigue loads of the front-row wind turbines. However, stability affected wake characteristics which caused differences in control response and fatigue experienced by downstream turbines. The most distinctive difference was observed at a downstream turbine in the above-rated condition where the shaft torsional load in neutral stability condition was up to 50% higher than the unstable case. A baseline active yaw controller was implemented in the below-rated condition, which caused higher fatigue on turbines in the wake compared to the fixed yaw turbine case, without any power output gain.

Performance Assessment of a Wind Turbine Using SCADA based Gaussian Process Model

Loss of wind turbine power production identified through performance assessment is a useful tool for effective condition monitoring of a wind turbine. Power curves describe the nonlinear relationship between power generation and hub height wind speed and play a significant role in analyzing the performance of a turbine.Performance assessment using nonparametric models is gaining popularity. A Gaussian Process is a nonlinear, non-parametric probabilistic approach widely used for fitting models and forecasting applications due to its flexibility and mathematical simplicity. Its applications extended to both classification and regression related problems. Despite promising results, Gaussian Process application in wind turbine condition monitoring is limited.In this paper, a model based on a Gaussian Process developed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then was compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a standard issue with a wind turbine, which causes underperformance. Hence it is used as case study to test and validate the algorithm effectiveness.

Wind Ramp Events Validation in NWP Forecast Models during the Second Wind Forecast Improvement Project (WFIP2) Using the Ramp Tool and Metric (RT&M)

AbstractThe second Wind Forecast Improvement Project (WFIP2) is a multiagency field campaign held in the Columbia Gorge area (October 2015–March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011–August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-height) wind speeds measured by 19 sodars and three lidars, and to forecasts from the High-Resolution Rapid Refresh (HRRR), 3-km, and from the High-Resolution Rapid Refresh Nest (HRRRNEST), 750-m horizontal grid spacing, models. The diurnal and seasonal distribution of ramp events are analyzed, finding a noticeable diurnal variability for spring and summer but less for fall and especially winter. Also, winter has fewer ramps compared to the other seasons. The model skill at forecasting ramp events, including the impact of the modification to the model physical parameterizations, was finally investigated.

Validating simulated mountain wave impacts on hub-height wind speed using SoDAR observations

The ascent of stably stratified air over a mountain barrier can trigger the generation of mountain waves. Mountain waves occur frequently over the Columbia River Gorge in western North America and can impact wind power generation over the area. Therefore, predicting the details of mountain waves events (e.g., dominant wavelength, timing, and duration) can be very valuable for the wind energy community. In this study, the ability of the Weather Research and Forecasting (WRF) model to simulate mountain waves and their impact on hub-height wind speed is investigated. Our results suggest that the WRF model has moderate skill in simulating observed mountain wave. Further, given WRF predictions of wavelength range and wave period, the Fast Fourier Transform can calculate the simulated mountain wave impact on hub-height wind speed. The resulting wind speeds agree well with SoDAR observations in terms of both magnitude and pattern. Finally, for the simulated cases, WRF consistently predicts impacts of significant mountain wave events about an hour earlier than the actual observations. The sensitivities as well as uncertainties associated with our methodology are discussed in detail.

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.

Climatology of wind variability for the Shagaya region in Kuwait

Electrical system operators utilizing wind energy production need accurate wind power forecasts to prepare for changes in power production. To understand the forecast problem and sources of forecast uncertainty, a climatology of the region of interest is needed. For Shagaya Renewable Energy Park in Kuwait, seasonal and diurnal wind patterns and the atmospheric phenomena that cause them are identified using observations from meteorological towers, surface weather stations, and wind turbines. A setup conducive to shamals increases hub-height wind speed by up to 3 m s-1 from May to August and thereby increases power production of the Shagaya wind turbines by 24%, in a season with higher energy demand for cooling homes and businesses. Near sunset, wind speed ramps up and remains faster throughout the night due to the prevalent nocturnal low-level jet. Wind speed ramps back down after sunrise when the nocturnal boundary layer is eroded by convective turbulence, which leads to more short-term fluctuations in wind speed and wind power during the day. Given knowledge on these seasonal and diurnal cycles of short-term and long-term wind power variability, wind power has clear potential to meet a significant portion of Kuwait's energy needs.

Improving site-dependent power curve prediction accuracy using regression trees

The accurate prediction of the power production of a wind turbine at a particular site is important in both the planning and operation phases; however, the standard power curve binning method is not specific to the atmospheric conditions at the site. In this work, the application of machine learning for improving the accuracy of site-specific power predictions by taking into account turbulence intensity and shear is investigated, by creating a set of 8,000 ten-minute long aero-servo-elastic simulations of the NREL 5MW reference wind turbine at a random combination of hub-height wind speeds, turbulence intensities and shear factors using cloud computing with the software ASHES. A regression tree with maximum depth of eight and optimised with Adaptive Boosting is trained using a random selection of half of the data. For a set of 50 random test cases, the Root Mean Square Error of the predicted power compared to the simulated power is found to be three times smaller than for the standard power curve method of binning. OEMs could use this method to train a model for the dependency of power production on wind speed, turbulence intensity and shear in the certification phase of a given wind turbine type, which could then be applied by the wind farm planner or operator at any given site for which measurements of the atmospheric conditions are available. Similar success is observed for out-of-plane tip deflections. This result is highly dependent on the quality and distribution of the input data, and therefore this work is being used as a basis for the analysis of real measurement data, as well as for comparisons to other methods such as Artificial Neural Networks.

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.

Sensitivity of Turbine-Height Wind Speeds to Parameters in the Planetary Boundary-Layer Parametrization Used in the Weather Research and Forecasting Model: Extension to Wintertime Conditions

We extend the model sensitivity analysis of Yang et al. (Boundary-Layer Meteorol 162: 117–142, 2017) to include results for February 2011, in addition to May of the same year. We investigate the sensitivity of simulated hub-height wind speeds to the selection of 12 parameters applied in the Mellor–Yamada–Nakanishi–Niino planetary boundary-layer parametrization in the Weather Research and Forecasting model, including parameters used to represent the dissipation of turbulence kinetic energy (TKE), Prandtl number (Pr), and turbulence length scales. Differences in the sensitivity of the ensemble of simulated wind speed to the various parameters can largely be explained by changes in the static stability. The largest monthly differences are found during the day, while the sensitivity to many of the parameters during the night is similar regardless of the month. This finding is consistent with an increased frequency of daytime stable conditions in February compared to May. The spatial variability of the sensitivity to TKE dissipation and Pr can also be attributed to variability in the static stability across the domain at any point in time.

Incorporating air density into a Gaussian process wind turbine power curve model for improving fitting accuracy

AbstractA power curve conventionally represents the relationship between hub height wind speed and wind turbine power output. Power curves facilitate the prediction of power production at a site and are also useful in identifying the significant changes in turbine performance which can be vital for condition monitoring. However, their accuracy is significantly influenced by changes in air density, mainly when the turbine is operating below rated power. A Gaussian process (GP) is a nonparametric machine learning approach useful for power curve fitting. Critical analysis of temperature correction is essential for improving the accuracy of wind turbine power curves. The conventional approach is to correct the data for air density before it is binned to provide a power curve, as described in the IEC standard. In this paper, four different possible approaches of air density correction and its effect on GP power curve fitting model accuracy are explored to identify whether the traditional IEC approach used for air density correction is most effective when estimating power curves using a GP. Finding the most accurate air density compensation approach is necessary to minimize GP model uncertainty.