Evaluation of four numerical wind flow models for wind resource mapping

A wide range of numerical wind flow models are available to simulate atmospheric flows. For wind resource mapping, the traditional approach has been to rely on linear Jackson–Hunt type wind flow models. Mesoscale numerical weather prediction (NWP) models coupled to linear wind flow models have been in use since the end of the 1990s. In the last few years, computational fluid dynamics (CFD) methods, in particular Reynolds‐averaged Navier–Stokes (RANS) models, have entered the mainstream, whereas more advanced CFD models such as large‐eddy simulations (LES) have been explored in research but remain computationally intensive. The present study aims to evaluate the ability of four numerical models to predict the variation in mean wind speed across sites with a wide range of terrain complexities, surface characteristics and wind climates. The four are (1) Jackson–Hunt type model, (2) CFD/RANS model, (3) coupled NWP and mass‐consistent model and (4) coupled NWP and LES model. The wind flow model predictions are compared against high‐quality observations from a total of 26 meteorological masts in four project areas. The coupled NWP model and NWP‐LES model produced the lowest root mean square error (RMSE) as measured between the predicted and observed mean wind speeds. The RMSE for the linear Jackson‐Hunt type model was 29% greater than the coupled NWP models and for the RANS model 58% greater than the coupled NWP models. The key advantage of the coupled NWP models appears to be their ability to simulate the unsteadiness of the flow as well as phenomena due to atmospheric stability and other thermal effects. Copyright © 2012 John Wiley & Sons, Ltd.     

Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique. In this study, a MOS based on multiple linear regression is proposed: the model screens the most relevant NWP forecast variables and selects the best predictors to fit a regression equation that minimizes the forecast errors, utilizing wind farm power output measurements as input. The performance of the method is evaluated in two wind farms, located in different topographical areas and with different NWP grid spacing. Because of the high seasonal variability of NWP forecasts, it was considered appropriate to implement monthly stratified MOS. In both wind farms, the first predictors were always wind speeds (at different heights) or friction velocity. When friction velocity is the first predictor, the proposed MOS forecasts resulted to be highly dependent on the friction velocity–wind speed correlation. Negligible improvements were encountered when including more than two predictors in the regression equation. The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained by the implementation of a more refined MOS stratification, e.g. fitting specific equations in different synoptic situations. Copyright © 2012 John Wiley & Sons, Ltd.     

基于Attention-GRU风速修正和Stacking的短期风电功率预测

针对使用数值天气预报（NWP）数据进行风电功率预测时,NWP风速与实际风速存在偏差导致预测精度欠佳,提出一种基于注意力机制（Attenion）门控逻辑单元（GRU）数值天气预报风速修正和Stacking多算法融合的短期风电功率预测模型。首先,分析NWP预报风速和实际风速的皮尔逊相关系数,建立Attention-GRU风速修正模型,提高预报风速精度。其次,考虑风向、温度、湿度、气压、空气密度等气象因素,基于Stacking框架,提出融合XGBoost、LSTM、SVR、LASSO的多算法风电功率预测模型,同时采用网格搜索与交叉验证优化模型参数。最后,选取西北和东北两个典型风电场数据进行验证,算例结果表明,所提出模型能改善NWP风速精度并提升风电功率预测效果。     

Validation of boundary layer parameterization schemes in the weather research and forecasting model under the aspect of offshore wind energy applications— Part I: Average wind speed and wind shear

Five different planetary boundary layer (PBL) schemes in the weather research and forecasting model have been tested with respect to their capability to model boundary layer parameters relevant for offshore wind deployments. For the year 2005 model simulations based on the Yonsei University, asymmetric convection model version 2, quasi‐normal scale elimination, Mellor–Yamada–Janjic and Mellor–Yamada–Nakanishi–Niino PBL schemes with weather research and forecasting have been performed for the North Sea and validated against measurements of the Forschungsplattformen in Nord‐ und Ostsee Nr.1 platform. The investigations have been focused on the key parameters 100 m mean wind speed and wind shear expressed by the power law exponent α. All PBL‐schemes are doing well in reproducing averages and average annual statistics of the 100 m wind speed. However, two of the schemes (Yonsei University and Mellor–Yamada–Nakanishi–Niino) overestimate the wind speed above 15 m s^?1 systematically. The results for the power law wind profile show a large variability between the models and the observations for different atmospheric stability conditions and also differ a lot from the industry standards. Overall, the Mellor–Yamada–Janjic scheme performs slightly better than the others and is suggested as first choice for marine atmospheric boundary layer simulations without apriori information of atmospheric stability in the region of interest. Copyright © 2014 John Wiley & Sons, Ltd.     

Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting

Wind power forecasting for projection times of 0–48 h can have a particular value in facilitating the integration of wind power into power systems. Accurate observations of the wind speed received by wind turbines are important inputs for some of the most useful methods for making such forecasts. In particular, they are used to derive power curves relating wind speeds to wind power production. By using power curve modeling, this paper compares two types of wind speed observations typically available at wind farms: the wind speed and wind direction measurements at the nacelles of the wind turbines and those at one or more on‐site meteorological masts (met masts). For the three Australian wind farms studied in this project, the results favor the nacelle‐based observations despite the inherent interference from the nacelle and the blades and despite calibration corrections to the met mast observations. This trend was found to be stronger for wind farm sites with more complex terrain. In addition, a numerical weather prediction (NWP) system was used to show that, for the wind farms studied, smaller single time‐series forecast errors can be achieved with the average wind speed from the nacelle‐based observations. This suggests that the nacelle‐average observations are more representative of the wind behavior predicted by an NWP system than the met mast observations. Also, when using an NWP system to predict wind farm power production, it suggests the use of a wind farm power curve based on nacelle‐average observations instead of met mast observations. Further, it suggests that historical and real‐time nacelle‐average observations should be calculated for large wind farms and used in wind power forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

The influence of non‐logarithmic wind speed profiles on potential power output at Danish offshore sites

Detailed knowledge of mean wind speed profiles is essential for properly assessing the power output of a potential wind farm. Since atmospheric stratification plays a crucial role in affecting wind speed profiles, obtaining a detailed picture of the climatology of stability conditions at a given site is very important. In the present study, long time series from offshore measurement sites around Denmark are analysed, with the aim of quantifying the role of atmospheric stability in wind speed profiles and in our ability to model them. A simple method for evaluating stability is applied, and the resulting statistics of the atmospheric stratification is thoroughly studied. A significant improvement in the mean wind speed profile prediction is obtained by applying a stability correction to the logarithmic profiles suitable for neutral conditions. These results are finally used to estimate power densities at different heights. Copyright © 2004 John Wiley & Sons, Ltd.     

Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm

Here, we quantify relationships between wind farm efficiency and wind speed, direction, turbulence and atmospheric stability using power output from the large offshore wind farm at Nysted in Denmark. Wake losses are, as expected, most strongly related to wind speed variations through the turbine thrust coefficient; with direction, atmospheric stability and turbulence as important second order effects. While the wind farm efficiency is highly dependent on the distribution of wind speeds and wind direction, it is shown that the impact of turbine spacing on wake losses and turbine efficiency can be quantified, albeit with relatively large uncertainty due to stochastic effects in the data. There is evidence of the ‘deep array effect’ in that wake losses in the centre of the wind farm are under‐estimated by the wind farm model WAsP, although overall efficiency of the wind farm is well predicted due to compensating edge effects. Copyright © 2010 John Wiley & Sons, Ltd.     

Assessing atmospheric stability and its impacts on rotor‐disk wind characteristics at an onshore wind farm

As the average hub height and blade diameter of new wind turbine installations continue to increase, turbines typically encounter higher wind speeds, which enable them to extract large amounts of energy, but they also face challenges due to the complex nature of wind flow and turbulence in the planetary boundary layer (PBL). Wind speed and turbulence can vary greatly across a turbine's rotor disk; this variability is partially due to whether the PBL is stable, neutral or convective. To assess the influence of stability on these wind characteristics, we utilize a unique data set including observations from two meteorological towers, a surface flux tower and high‐resolution remote‐sensing sound detection and ranging (SODAR) instrument. We compare several approaches to defining atmospheric stability to the Obukhov length (L). Typical wind farm observations only allow for the calculation of a wind shear exponent (α) or horizontal turbulence intensity (I_U) from cup anemometers, whereas SODAR gives measurements at multiple heights in the rotor disk of turbulence intensity (I) in the latitudinal (I_u), longitudinal (I_v) and vertical (I_w) directions and turbulence kinetic energy (TKE). Two methods for calculating horizontal Ifrom SODAR data are discussed. SODAR stability parameters are in high agreement with the more physically robust L,with TKE exhibiting the best agreement, and show promise for accurate characterizations of stability. Vertical profiles of wind speed and turbulence, which likely affect turbine power performance, are highly correlated with stability regime. At this wind farm, disregarding stability leads to over‐assessments of the wind resource during convective conditions and under‐assessments during stable conditions. Copyright © 2011 John Wiley & Sons, Ltd.     

Convertible wind energy based on predicted wind speed at hub-height

Support vector machine is proposed to find wind speed at higher heights using measurements at lower heights. The mean absolute percentage error between measured and the estimated wind speed at height 40 m is found to be satisfactory. After validation at 40 m, the model was used to calculate the wind speed at hub heights up to 100 m. Annual energy yield was found to be increasing with hub height and, hence, accurate estimation of wind speed at heights becomes essential for realistic wind energy assessment. Furthermore, the plant capacity factor was found to be increasing approximately 1% for each 10-m increase in hub height.     

LiDAR‐based characterization of mid‐altitude wind conditions for airborne wind energy systems

Engineers and researchers working on the development of airborne wind energy systems (AWES) still rely on oversimplified wind speed approximations and coarsely sampled reanalysis data because of a lack of high‐resolution wind data at altitudes above 200 m. Ten‐minute average wind speed LiDAR measurements up to an altitude of 1100 m and data from nearby weather stations were investigated with regard to wind energy generation and impact on LiDAR measurements. Data were gathered by a long‐range pulsed Doppler LiDAR device installed on flat terrain. Because of the low overall carrier‐to‐noise ratio, a custom‐filtering technique was applied. Our analyses show that diurnal variation and atmospheric stability significantly affect wind conditions aloft which cause a wide range of wind speeds and a multimodal probability distribution that cannot be represented by a simple Weibull distribution fit. A better representation of the actual wind conditions can be achieved by fitting Weibull distributions separately to stable and unstable conditions. Splitting and clustering the data by simulated surface heat flux reveals substate stratification responsible for the multimodality. We classify different wind conditions based on these substates, which result in different wind energy potential. We assess optimal traction power and optimal operating altitudes statistically as well as for specific days based on a simplified AWES model. Using measured wind speed standard deviation, we estimate average turbulence intensity and show its variation with altitude and time. Selected short‐term data sets illustrate temporal changes in wind conditions and atmospheric stratification with a high temporal and vertical resolution.     

低空急流条件下水平轴风力机风轮气动特性的研究

为阐明低空急流条件下风力机风轮的气动特性,基于工程化的边界层风速模型和Von Karman谱模型建立不同来流的脉动风场,对比研究低空急流条件下NREL 5 MW风力机风轮的输出功率和气动载荷的变化规律。结果表明:如果仅以轮毂高度处的风速作为风力机变桨控制的依据,与均匀来流和剪切来流相比较,低空急流条件下,虽然来流风功率明显增大,但风轮的输出功率在较高风速时反而减小;风轮所受的不平衡气动载荷,包括横向力、纵向力、偏航力矩和倾覆力矩在较高风速时小于剪切来流的结果;且仅以轮毂高度处的风速预测得到的风轮输出功率高于实际结果,其最大相对误差为89.4%。因此,低空急流条件下,为提高风能利用率和风轮输出功率的预测精度,应考虑不同高度位置处的风速大小对风力机进行变桨控制和功率预测。     

Prediction of local wind climatology from Met Office models: Virtual Met Mast techniques

The Met Office has developed the Virtual Met Mast? (VMM) tool for assessing the feasibility of potential wind farm sites. It provides site‐specific climatological wind information for both onshore and offshore locations. The VMM relies on existing data from past forecasts from regional‐scale numerical weather prediction (NWP) models, to which corrections are applied to account for local site complexity. The techniques include corrections to account for the enhanced roughness lengths used in NWP models to represent drag due to sub‐grid orography and downscaling methods that predict local wind acceleration over small‐scale terrain. The corrected NWP data are extended to cover long periods (decades) using a technique in which the data are related to alternative long‐term datasets. For locations in the UK, the VMM currently relies on operational mesoscale model forecast data at 4 km horizontal resolution. Predictions have been verified against observations made at typical wind turbine hub heights at over 80 sites across the UK. In general, the predictions compare well with the observations. The techniques provide an efficient method for screening potential wind resource sites. Examples of how the VMM techniques can be used to produce local wind maps are also presented. © 2016 Crown copyright. Wind Energy © 2016 John Wiley & Sons, Ltd     

Analysing wind power ramp events and improving very short-term wind power predictions by including wind speed observations

Though wind power predictions have been consistently improved in the last decade, persistent reasons for remaining uncertainties are sudden large changes in wind speed, so-called ramps. Here, we analyse the occurrence of ramp events in a wind farm in Eastern Germany and the performance of a wind power prediction tool in forecasting these events for forecasting horizons of 15 and 30 min. Results on the seasonality of ramp events and their diurnal cycle are presented for multiple ramp definition thresholds. Ramps were found to be most frequent in March and April and least frequent in November and December. For the analysis, the wind power prediction tool is fed by different wind velocity forecast products, for example, numerical weather prediction (NWP) model and measurement data. It is shown that including observational wind speed data for very short-term wind power forecasts improves the performance of the power prediction tool compared to the NWP reference, both in terms of ramp detection and in decreasing the mean absolute error between predicted and generated wind power. This improvement is enhanced during ramp events, highlighting the importance of wind observations for very short-term wind power prediction.     

Probabilistic maximum‐value wind prediction for offshore environments

High wind speeds can pose a great risk to structures and operations conducted in offshore environments. When forecasting wind speeds, most models focus on the average wind speeds over a given period, but this value alone represents only a small part of the true wind conditions. We present statistical models to predict the full distribution of the maximum‐value wind speeds in a 3 h interval. We take a detailed look at the performance of linear models, generalized additive models and multivariate adaptive regression splines models using meteorological covariates such as gust speed, wind speed, convective available potential energy, Charnock, mean sea‐level pressure and temperature, as given by the European Center for Medium‐Range Weather Forecasts forecasts. The models are trained to predict the mean value of maximum wind speed, and the residuals from training the models are used to develop the full probabilistic distribution of maximum wind speed. Knowledge of the maximum wind speed for an offshore location within a given period can inform decision‐making regarding turbine operations, planned maintenance operations and power grid scheduling in order to improve safety and reliability, and probabilistic forecasts result in greater value to the end‐user. The models outperform traditional baseline forecast methods and achieve low predictive errors on the order of 1–2 m s^?1. We show the results of their predictive accuracy for different lead times and different training methodologies. Copyright © 2014 John Wiley & Sons, Ltd.     

基于风速误差校正和ALO-LSSVM的风电功率预测

风资源的随机波动性引起的相位滞后性问题,导致风电功率预测精度不高,尤其是风速变化较快时,滞后性引起的预测误差较大。考虑到风速波动与风功率变化密切相关,提出一种非参数核密度估计和数值天气预报(NWP)相结合的方法,并对预测风速误差进行校正,改善了预测风速的相位滞后性;然后将校正后的风速和风功率作为输入数据进行风电功率预测;采用蚁狮算法(ALO)优化最小二乘支持向量机(LSSVM)参数,从而建立基于风速误差校正和ALO-LSSVM组合的风电功率预测模型。算例结果表明,所提方法风功率预测精度更高。     

LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

Wind measurements were performed with the UTD mobile LiDAR station for an onshore wind farm located in Texas with the aim of characterizing evolution of wind‐turbine wakes for different hub‐height wind speeds and regimes of the static atmospheric stability. The wind velocity field was measured by means of a scanning Doppler wind LiDAR, while atmospheric boundary layer and turbine parameters were monitored through a met‐tower and SCADA, respectively. The wake measurements are clustered and their ensemble statistics retrieved as functions of the hub‐height wind speed and the atmospheric stability regime, which is characterized either with the Bulk Richardson number or wind turbulence intensity at hub height. The cluster analysis of the LiDAR measurements has singled out that the turbine thrust coefficient is the main parameter driving the variability of the velocity deficit in the near wake. In contrast, atmospheric stability has negligible influence on the near‐wake velocity field, while it affects noticeably the far‐wake evolution and recovery. A secondary effect on wake‐recovery rate is observed as a function of the rotor thrust coefficient. For higher thrust coefficients, the enhanced wake‐generated turbulence fosters wake recovery. A semi‐empirical model is formulated to predict the maximum wake velocity deficit as a function of the downstream distance using the rotor thrust coefficient and the incoming turbulence intensity at hub height as input. The cluster analysis of the LiDAR measurements and the ensemble statistics calculated through the Barnes scheme have enabled to generate a valuable dataset for development and assessment of wind farm models.     

风切变指数在风电场风资源评估中的应用

以内蒙古地区3座70m高测风塔连续2年的实测数据来分析风切变指数的变化,结果表明:1)不同高度梯度的风切变指数受地面粗糙度及周围地形地貌的影响较大。2)计算相邻高度的风速时,采用相邻高度间的风切变指数计算得到的结果较好;计算相差较大的高度间风速时,采用拟合曲线得到的风切变指数计算得到的结果较好。3)利用3～25m/s的风切变指数计算各月风速及年均风速结果都与实测值最接近;而利用全部风速数据的风切变指数计算统计各月风速往往比实测值偏大;利用3～25m/s拟合曲线得到的风切变指数统计各月风速比实测值偏小。     

On the application of the Jensen wake model using a turbulence‐dependent wake decay coefficient: the Sexbierum case

We present a methodology to process wind turbine wake simulations, which are closely related to the nature of wake observations and the processing of these to generate the so‐called wake cases. The method involves averaging a large number of wake simulations over a range of wind directions and partly accounts for the uncertainty in the wind direction assuming that the same follows a Gaussian distribution. Simulations of the single and double wake measurements at the Sexbierum onshore wind farm are performed using a fast engineering wind farm wake model based on the Jensen wake model, a linearized computational fluid dynamics wake model by Fuga and a nonlinear computational fluid dynamics wake model that solves the Reynolds‐averaged Navier–Stokes equations with a modified k‐ε turbulence model. The best agreement between models and measurements is found using the Jensen‐based wake model with the suggested post‐processing. We show that the wake decay coefficient of the Jensen wake model must be decreased from the commonly used onshore value of 0.075 to 0.038, when applied to the Sexbierum cases, as wake decay is related to the height, roughness and atmospheric stability and, thus, to turbulence intensity. Based on surface layer relations and assumptions between turbulence intensity and atmospheric stability, we find that at Sexbierum, the atmosphere was probably close to stable, although the stability was not observed. We support these assumptions using detailed meteorological observations from the Høvsøre site in Denmark, which is topographically similar to the Sexbierum region. © 2015 The Authors. Wind Energy published by John Wiley & Sons Ltd.     

Atmospheric stability‐dependent infinite wind‐farm models and the wake‐decay coefficient

We extend the infinite wind‐farm boundary‐layer (IWFBL) model of Frandsen to take into account atmospheric static stability effects. This extended model is compared with the IWFBL model of Emeis and to the Park wake model used in Wind Atlas Analysis and Application Program (WAsP), which is computed for an infinite wind farm. The models show similar behavior for the wind‐speed reduction when accounting for a number of surface roughness lengths, turbine to turbine separations and wind speeds under neutral conditions. For a wide range of atmospheric stability and surface roughness length values, the extended IWFBL model of Frandsen shows a much higher wind‐speed reduction dependency on atmospheric stability than on roughness length (roughness has been generally thought to have a major effect on the wind‐speed reduction). We further adjust the wake‐decay coefficient of the Park wake model for an infinite wind farm to match the wind‐speed reduction estimated by the extended IWFBL model of Frandsen for different roughness lengths, turbine to turbine separations and atmospheric stability conditions. It is found that the WAsP‐recommended values for the wake‐decay coefficient of the Park wake model are (i) larger than the adjusted values for a wide range of neutral to stable atmospheric stability conditions, a number of roughness lengths and turbine separations lower than ～ 10 rotor diameters and (ii) too large compared with those obtained by a semiempirical formulation (relating the ratio of the friction to the hub‐height free velocity) for all types of roughness and atmospheric stability conditions. © 2013 The Authors. Wind Energy published by John Wiley & Sons, Ltd.     

A general method to estimate wind farm power using artificial neural networks

An artificial neural network (ANN) is trained and validated using a large dataset of observations of wind speed, direction, and power generated at an offshore wind farm (Lillgrund in Sweden). In its traditional form, the ANN is used to generate a new two‐dimensional power curve, which predicts with high accuracy (bias ～?0.5% and absolute error ～2%) the power of the entire Lillgrund wind farm based on wind speed and direction. By contrast, manufacturers only provide one‐dimensional power curves (i.e., power as a function of wind speed) for a single turbine. The second innovative application of the ANN is the use of a geometric model (GM) to calculate two simple geometric properties to replace wind direction in the ANN. The resulting GM‐ANN has the powerful feature of being applicable to any wind farm, not just Lillgrund. A validation at an onshore wind farm (Nørrekær in Denmark) demonstrates the high accuracy (bias ～?0.7% and absolute error ～6%) and transfer‐learning ability of the GM‐ANN.