Wind speed generation for dynamic analysis

A simple model to generate large band wind speed time sequences, especially easy to implement with a very reduced number of parameters, is presented. It is based on the calculation of a low‐frequency and a high‐frequency components. Low‐frequency component with 1 h sample time is obtained from a random process based on a conditional probability density function. Using real data from two different wind farms in two different months of the year, it has been found that Weibull distribution centered in the current hourly mean value seems to represent well the 1 h conditional PDF in all cases, and the standard deviation of this conditional Weibull is more or less in the range 1–1.3 m s^?1 independently of the season of the year or the location. Regarding to high‐frequency component, low‐frequency samples are used as initial and final values and, between them, the turbulence component values are inserted. For that, it has been used a stochastic process based on a Beta probability function and a simple rescaling procedure with two non‐linear parameters, calculated in a recursive way. Unlike the usual modelling procedures presented in the literature, spectral power density functions are not used. This simplifies the implementation significantly. Ten second sample‐time real speed wind data from two different wind farms have been used to validate the proposed high‐frequency model, obtaining excellent results. A thorough revision of the main models found in the literature to produce wind speed time sequences for dynamic analysis is performed in the paper. Copyright © 2017 John Wiley & Sons, Ltd.     

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.     

基于卷积和共享权系数长短时记忆网络时融合机制在风速预测上的应用研究

针对对于风能规划和应用都具有重大影响的风速存在强随机性问题,该文提出结合卷积神经网络（CNN）和共享权重长短期记忆网络（SWLSTM）的空时融合模型（CSWLSTM）,充分提取风速序列中蕴含的空域和时域信息,以提升预测精度。此外,为了获得可靠的风速概率预测结果,提出一种新的结合CNN、SWLSTM和高斯过程回归（GPR）的混合模型,称为 CSWLSTM-GPR。将CSWLSTM-GPR应用于中国内蒙古风速预测案例,从点预测精度、区间预测适用性和概率预测综合性能3个方面与相同结构的CNN和SWLSTM模型的风速预测方法进行比较。CSWLSTM-GPR的可靠性测试保证了预测结果的可靠性和说服力。实验结果表明,CSWLSTM-GPR在风速预测问题上能获得高精度的点预测、合适的预测区间和可靠的概率预测结果,也充分展现了该研究所提出CSWLSTM在风速预测方面具有较好的应用潜力。     

The system performance of autonomous photovoltaic–wind hybrid energy systems using synthetically generated weather data

The yearly system performance of autonomous photovoltaic–wind hybrid energy systems with battery storage is the subject of this article. The yearly system performance is simulated using synthetically generated solar radiation and wind speed data and compared to that simulated using measured hour-by-hour data. Two different synthetic weather data sets are generated: 3-day month and 4-day month, in which 3 and 4 days represent a month, resulting in a total of 36 and 48 days for a year. The hourly varying solar radiation data are synthesised from the clearness index value for each month. The daily constant wind speed data are synthesised using the Weibull wind speed distribution model, on a monthly basis. Using two different synthetic weather data sets, the effect of number of synthetic days on the system performance estimation is studied. Different sequences of synthetic solar and wind days lead to 36 and 576 combinations for 3- and 4-day months, respectively. Three predetermined combinations for both the 3- and 4-day months are chosen and the system performance of an autonomous photovoltaic–wind hybrid energy system with battery storage is simulated using these predetermined combinations. It is shown that the yearly system performance predicted from the 3- and 4-day synthetic data closely agrees with that obtained from the measured data, varying only slightly for different combinations.     

An M‐Rice wind speed frequency distribution

Several known statistical distributions can describe wind speed data, the most commonly used being the Weibull family. In this paper, a new law, called ‘M‐Rice’, is proposed for modeling wind speed frequency distributions. Inspired by recent empirical findings that suggest the existence of some cascading process in the mesoscale range, we consider that wind speed can be described by a seasonal AutoRegressive Moving Average (ARMA) model where the noise term is ‘multifractal’, i.e. associated with a random cascade. This leads to the distribution of wind speeds according to the M‐Rice probability distribution function, i.e. a Rice distribution multiplicatively convolved with a normal law. A comparison based on the estimation of the mean wind speed and power density values as well as on the different goodness‐of‐fit tests (the Kolmogorov–Smirnov test, the Kuiper test and the quantile–quantile plot) was made between this new distribution and the Weibull distribution for 35 data sets of wind speed from the Netherlands and Corsica (France) sites. Accordingly, the M‐Rice and Weibull distributions provided comparable performances; however, the quantile–quantile plots suggest that the M‐Rice distribution provides a better fit of extreme wind speed data. Beyond these good results, our approach allows one to interpret the observed values of Weibull parameters. Copyright © 2011 John Wiley & Sons, Ltd.     

Wind turbine concept market penetration over 10 years (1995–2004)

The aim of this article is to investigate the market penetration and share of different wind turbine concepts during the years 1995–2004, a period that represents the maturational era of the modern wind power industry. A detailed overview is given based on suppliers' market data and concept evaluation for each individual wind turbine type sold by the Top Ten suppliers over the selected decade. The investigation is processing information on approximately 160 wind turbine types from 22 different manufacturers that have featured in the Top Ten list of wind turbine suppliers during 1995–2004. The analysis is based on comprehensive data covering approximately 97% of the cumulative wind power installed worldwide at the end of 2004. The article also provides an overall perspective on contemporary wind turbine concepts, classified with respect to both their speed control ability and power control type. Current and future trends for wind turbine concepts are discussed. Copyright © 2006 John Wiley &Sons, Ltd.     

风速频率分布混合模型的研究

报道了一种有实际意义的风速频率分布四参数混合模型，与著名的Ｗｅｉｂｕｌｌ模型相比较，它不仅解决了在零风速时的概率密度不为零的问题，也改善了在低风速段理论与实测存在较大差距的问题，计算结果表明，其拟合精度远高于Ｗｅｉｂｕｌｌ分布。     

考虑风速风向联合概率分布的风电塔筒结构风致疲劳寿命评估

针对风向对风力机塔筒疲劳产生影响的问题,基于实测数据对考虑风速风向联合概率分布的风电塔筒结构的风致疲劳寿命展开研究。首先结合甘肃安西地区37 a的实测风速风向数据,给出风速风向联合概率分布。然后利用主S-N曲线法分别对不同风向和不同风速下风力机塔架结构法兰及门洞区域的响应规律进行分析。最后考虑风速风向联合概率分布,对风电塔筒结构风致疲劳寿命展开研究。结果表明：门洞朝向与风轮朝向的夹角变化和风速的改变均对风电塔筒的风致疲劳寿命有一定影响,其中门洞朝向与风轮朝向夹角为225°时疲劳寿命最长,风速为10~14 m/s时疲劳寿命变化幅度最大;考虑风速风向联合概率分布能更准确地计算风力机结构的风致疲劳寿命,且以此为依据对门洞朝向进行调整可延长其疲劳寿命,因此建议对风电塔架进行设计时,应考虑风电场所在地区的风速风向联合概率分布。     

Evaluation of a wind speed estimator for effective hub‐height and shear components

Estimates of the effective wind speed disturbances acting on a wind turbine are useful in a variety of control applications. With some simplifications, it is shown that for zero yaw error, any wind field interacting with a turbine can be equivalently described using a hub‐height (uniform) component as well as linear horizontal and vertical shear components. A Kalman filter‐based wind speed estimator is presented for estimation of these effective hub‐height and shear components. The wind speed estimator is evaluated in the frequency domain using the FAST aeroelastic simulator with the National Renewable Energy Laboratory's 5 MW reference wind turbine model and realistic hub‐height and shear disturbances. In addition, the impact of the inflow model, used to simulate the rotor aerodynamics, on the Kalman filter performance is investigated. It is found that the estimator accuracy strongly depends on the inflow model used. In general, the estimator performs well up to a bandwidth of 1 Hz when the inflow model used for simulation matches the model used to create the linear Kalman filter model and blade pitch angle remains close to the linearization operating point. However, inaccuracies in the linear model of the turbine when dynamic inflow is used for simulation as well as nonlinearities in the turbine dynamics due to blade pitch actuation cause performance to degrade. Finally, the improvement gained by employing a non‐causal wind speed estimator is assessed, showing a minor increase in performance. Copyright © 2014 John Wiley & Sons, Ltd.     

A novel method based on Weibull distribution for short-term wind speed prediction

Wind speed prediction (WSP) is essential in order to predict and analyze efficiency and performance of wind-based electricity generation systems. More accurate WSP may provide better opportunities to design and build more efficient and robust wind energy systems. Precious short-term prediction is difficult to achieve; therefore several methods have been developed so far. We notice that the statistics of the alterations, which occur between sequential values of the predicted wind speed data, may differ significantly from observed wind statistics. In this study, we investigate these alterations and compare them and, accordingly, propose a novel method based on Weibull and Gaussian probability distribution functions (PDF) for short-term WSP. The proposed method stands on an algorithm, which examines comparison of the statistical features of the observed and generated wind speed in order to achieve more accurate estimation. We have examined this method on the wind speed data set observed and recorded in Ankara in 2013 and in 2014. The obtained results show that the new algorithm provides better wind speed prediction with an enhanced wind speed model.     

De‐trending of wind speed variance based on first‐order and second‐order statistical moments only

The lack of efficient methods for de‐trending of wind speed resource data may lead to erroneous wind turbine fatigue and ultimate load predictions. The present paper presents two models, which quantify the effect of an assumed linear trend on wind speed standard deviations as based on available statistical data only. The first model is a pure time series analysis approach, which quantifies the effect of non‐stationary characteristics of ensemble mean wind speeds on the estimated wind speed standard deviations as based on mean wind speed statistics only. This model is applicable to statistics of arbitrary types of time series. The second model uses the full set of information and includes thus additionally observed wind speed standard deviations to estimate the effect of ensemble mean non‐stationarities on wind speed standard deviations. This model takes advantage of a simple physical relationship between first‐order and second‐order statistical moments of wind speeds in the atmospheric boundary layer and is therefore dedicated to wind speed time series but is not applicable to time series in general. The capabilities of the proposed models are discussed by comparing model predictions with conventionally de‐trended characteristics of measured wind speeds using data where high sampled time series are available, and a traditional de‐trending procedure therefore can be applied. This analysis shows that the second model performs significantly better than the first model, and thus in turn that the model constraint, introduced by the physical link between the first and second statistical moments, proves very efficient in the present context. © 2013 The Authors. Wind Energy Published by John Wiley & Sons Ltd.     

Limitations and breakdown of Monin–Obukhov similarity theory for wind profile extrapolation under stable stratification

The intent of this study is to investigate the limitations of the Monin–Obukhov similarity theory (MOST) for wind profile extrapolation—particularly its breakdown in stable stratification—and to explore several modifications intended to circumvent aspects of this breakdown. Using 10years of 10min averaged data from the 213m Cabauw meteorological tower in the Netherlands, we first demonstrate the sensitivity of the logarithmic wind speed model to highly uncertain estimates of the roughness length, z₀, and the associated limitations of applying the model in horizontally inhomogeneous conditions. We then demonstrate that these limitations can be mitigated by avoiding the use of z₀ in the logarithmic wind speed model. Rather, by using a lower boundary above z₀ (e.g. 10m) and a ‘bulk’ Obukhov length measured between two near‐surface altitudes, substantial improvements in wind speed extrapolation accuracy are found. Next, we demonstrate the limitations in applying the logarithmic wind speed model above the surface layer (SL), specifically the divergence of different forms of the MOST stability function, the role of the Coriolis force and the decoupling of surface winds from those aloft. Finally, we explore similarity‐based modifications to the logarithmic wind speed model that are intended to improve its accuracy above the SL, but we find that such modifications cannot circumvent the limitations described earlier. Given that modern hub heights and altitudes swept out by a wind turbine blade extend well beyond the range of applicability of MOST under conditions of stable stratification, new extrapolation models are required that are more applicable at these altitudes. Copyright © 2015 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.     

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.     

基于SVM方法的风电场短期风速预测

针对基于支持向量机的风电场短期风速预测进行研究．选择了不同的输入向量（历史风速时间序列,历史风速和温度．历史风速、温度和风向,历史风速、温度和时间）作为输入进行误差对比分析。实测数据及分析结果表明,采用历史风度和温度的二输入模型,预测效果最佳,为风速的短期预测和发电量预测提供了较好的参考价值。     

Prediction of the extreme wind speed in the mixed climate region by using Monte Carlo simulation and measure‐correlate‐predict method

The extreme wind speed at an offshore location was predicted using Monte Carlo simulation (MCS) and measure‐correlate‐predict (MCP) method. The Gumbel distribution could successfully express the annual maximum wind speed of extratropical cyclone. On the other hand, the estimated extreme wind speed of tropical cyclones by analytical probability distribution shows larger uncertainty. In the mixed climate like Japan, the extreme wind speed estimated from the combined probability distribution obtained by MCP and MCS methods agrees well with the observed data as compared with the combined probability distribution obtained by the MCP method only. The uncertainty of extreme wind speed due to limited observation period of wind speed and pressure was also evaluated by the Gumbel theory and Monte Carlo simulation. As a result, it was found that the uncertainty of 50 year recurrence wind speed obtained by MCS method is considerably smaller than that obtained by MCP method in the mixed climate. Copyright © 2014 John Wiley & Sons, Ltd.     

Wind speed time series characterization by Hilbert transform

Predictions of wind energy potential in a given region are based on on‐location observations. The time series of these observations would later be analysed and modelled either by a probability density function (pdf) such as a Weibull curve to represent the data or recently by soft computing techniques, such as neural networks (NNs). In this paper, discrete Hilbert transform has been applied to characterize the wind sample data measured on ?zmir Institute of Technology campus area which is located in Urla, ?zmir, Turkey, in March 2001 and 2002. By applying discrete Hilbert transform filter, the instantaneous amplitude, phase and frequency are found, and characterization of wind speed is accomplished. Authors have also tried to estimate the hourly wind data using daily sequence by Hilbert transform technique. Results are varying. Copyright © 2005 John Wiley & Sons, Ltd.     

内蒙古地区风资源评估与风场特征风速的推导

对内蒙古二十四个地区的风能资源进行评估,得到风谱图.首先提出了利用WAsP软件对1998年至2008年期间内蒙古二十四地区的风资源资料中的基础进行分析;然后利用风速威布尔分布函数和风力发电机组输出功率的威布尔的概率密度函数,求两个函数的极值,推导出切入风速和额定风速的公式.最后以内蒙古六个地区为例,计算不同风资源条件下的切入风速和额定风速.     

Comparison of potential power production at on‐ and offshore sites

This article presents analyses of the potential power production from turbines located in the near‐shore and offshore environment relative to an onshore location, using half‐hourly average wind speed data from four sites in the Danish wind monitoring network. These measurement sites are located in a relatively high wind speed environment, and data from these sites indicate a high degree of spatial coherence. For these sites and representative turbine specifications (rated power 1·3–2 W) the fraction of time with power output in excess of 500 kW is twice as high for the offshore location as for the land site. Also, the fraction of time with negligible power production (defined as <100 kW output from the turbines described herein) is less than 20% for the offshore site and twice as high at the land‐based location. Capacity factors are higher for coastal sites than for the land site, and the annual capacity factor for the offshore location is twice that of the land site. Potential power output at the offshore site exhibits approximately the same seasonal variation as at the land site but little diurnal variation. Copyright © 2001 John Wiley & Sons, Ltd.