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.     

Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes

The existence of vertical wind shear in the atmosphere close to the ground requires that wind resource assessment and prediction with numerical weather prediction (NWP) models use wind forecasts at levels within the full rotor span of modern large wind turbines. The performance of NWP models regarding wind energy at these levels partly depends on the formulation and implementation of planetary boundary layer (PBL) parameterizations in these models. This study evaluates wind speeds and vertical wind shears simulated by the Weather Research and Forecasting model using seven sets of simulations with different PBL parameterizations at one coastal site over western Denmark. The evaluation focuses on determining which PBL parameterization performs best for wind energy forecasting, and presenting a validation methodology that takes into account wind speed at different heights. Winds speeds at heights ranging from 10 to 160 m, wind shears, temperatures and surface turbulent fluxes from seven sets of hindcasts are evaluated against observations at Høvsøre, Denmark. The ability of these hindcast sets to simulate mean wind speeds, wind shear, and their time variability strongly depends on atmospheric static stability. Wind speed hindcasts using the Yonsei University PBL scheme compared best with observations during unstable atmospheric conditions, whereas the Asymmetric Convective Model version 2 PBL scheme did so during near‐stable and neutral conditions, and the Mellor–Yamada–Janjic PBL scheme prevailed during stable and very stable conditions. The evaluation of the simulated wind speed errors and how these vary with height clearly indicates that for wind power forecasting and wind resource assessment, validation against 10 m wind speeds alone is not sufficient. Copyright © 2012 John Wiley & Sons, Ltd.     

Wind energy potential assessment for the offshore areas of Taiwan west coast and Penghu Archipelago

The average wind speed and wind power density of Taiwan had been evaluated at 10 m, 30 m and 50 m by simulation of mesoscale numerical weather prediction model (MM5). The results showed that wind energy potential of this area is excellent. Taiwan has offered funds to encourage the founding of offshore wind farms in this area. The purpose of this study is to make a high resolution wind energy assessment for the offshore area of Taiwan west coast and Penghu archipelago by using WAsP. The result of this study has been used to the relative financial planning of offshore wind farm projects in Taiwan. The basic inputs of WAsP include wind weather data and terrain data. The wind weather data was from a monitoring station located on a remote island, Tongi, because that all of weather stations in the area of Taiwan west coast are affected by urbanization. SRTM was selected to be used as terrain data and downloaded from CGIAR-CSI for voids problem. The coverage of considered terrain area in this assessment work is about 300 km × 400 km that made some difficulties to run wind energy assessment of the whole area with a high resolution of 100 m. So the interested area of this study is divided into 19 areas for the wind energy assessment and mapping. The assessment results show the Changhua area has best wind energy potential in the area of Taiwan west coast which power density is above 1000 W/m² height and the areas of Penghu archipelago are above 1300 W. These results are higher than the expected from NWP. 180 of 3 MW wind turbines were used in the study of micro sitting in the Changhua area.The type and number of the wind turbines and the layout of the wind farm is similar to the prior study of Taipower Company for demonstrating the reliability of this study. The assessment result of average net annual energy production (AEP) of the wind farm is about 11.3 GWh that is very close to the prior study. The terrain effect is also studied. The average net annual energy production will decrease about 0.7 GWh if the wind turbines were moved eastward 3600 m closer to the coast because of terrain effect. As the same reason, the average net annual energy production would be increased to 11.392 GWh if the wind farm is moved westward 3600 m away from the coast.     

Wind energy analysis based on turbine and developed site power curves: A case-study of Darling City

The observed wind at a given site varies continuously as a function of time and season, increasing hub heights, topography of the terrain, prevailing weather condition etc. The quality of wind resource is one of the important site factors to be considered when assessing the wind potential of any location for any energy project. In this study, two wind energy analysis techniques are presented: the use of direct technique where the electrical power outputs of the wind turbines at a time t are estimated using the turbine power curve(s) and the use of statistical-based technique where the power outputs are estimated based on the developed site power curve(s). The wind resource assessment at Darling site is conducted using a 5-min time series weather data collected on a 10 m height over a period of 24 months. Because of the non-linearity of the site's wind speed and its corresponding power output, the wind resources are modeled and the developed site power curve(s) are used to estimate the long term energy outputs of the wind turbines for changing weather conditions. Three wind turbines rating of 1.3 MW, 1.3 MW and 1.0 MW were selected for the energy generation based on the gauged wind resource(s) at 50, 60 and 70 m heights, respectively. The energy outputs at 50 m height using the 1.3 MW WT were compared to the energy outputs at 60 m to determine the standard height for utility scale energy generation at this site. An additional energy generation of 190.71 MWh was available by deploying the same rated turbine at a 60 m height. Furthermore, comparisons were made between the use of turbine and site power curve for wind energy analysis at the considered heights. The results show that the analysis of the energy outputs of the WTs based on the site power curve is an accurate technique for wind energy analysis as compared to the turbine power curve. Conclusions are drawn on the suitability of this site for utility scale generation based on the wind resources evaluation at different heights.     

Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment

Wind energy resource assessment applications require accurate wind measurements. Most of the published studies used data from existing weather station network operated by meteorological departments. Due to relatively high cost of weather stations the resolution of the weather station network is coarse for wind energy applications. Typically, meteorological departments install weather stations at specific locations such as airports, ports and areas with high density population. Typically, these locations are avoided during wind farms siting. According to WMO regulations, weather stations provide measurements for different weather elements at specific altitudes such as 2 m for air temperature and 10 m for wind measurements. For wind energy resource assessment applications, minimum of one year of wind measurements is required to build wind climatology for a certain site. Therefore data collected from a certain site cannot be used before one year of operation. Due to these limitations, wind energy resource assessment application needs to use data from different sources. Recently, wind assessment studies were conducted using data generated by Numerical Weather Prediction models. This paper reviews the use of the Numerical Weather Prediction data for wind energy resource assessment. It gives a general overview of NWP models and how they overcome the limitations in the classical wind measurements.     

Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function

The wind resource is a crucial step in planning a wind energy project and detailed knowledge of the wind characteristic at a site is needed to estimate the performance of a wind energy project. In this paper, with the help of 2-parameter Weibull distribution, the assessment of wind energy potentiality at Kudat and Labuan in 2006-2008 was carried out. “WRPLOT” software has been used to show the wind direction and resultant of the wind speed direction. The monthly and yearly highest mean wind speeds were 4.76 m/s at Kudat and 3.39 m/s at Labuan respectively. The annual highest values of the Weibull shape parameter (k) and scale parameter (c) were 1.86 and 3.81 m/s respectively. The maximum wind power density was found to be 67.40 W/m² at Kudat for the year 2008. The maximum wind energy density was found to be 590.40 kWh/m²/year at Kudat in 2008. The highest most probable wind speed and wind speed carrying maximum energy were estimated 2.44 m/s at Labuan in 2007 and 6.02 m/s at Kudat in 2007. The maximum deviation, at wind speed more than 2 m/s, between observed and Weibull frequency distribution was about 5%. The most probable wind directions (blowing from) were 190° and 269° at Kudat and Labuan through the study years. From this study, it is concluded that these sites are unsuitable for the large-scale wind energy generation. However, small-scale wind energy can be generated at the turbine height of 100 m.     

Resource potential for wind-hydrogen power in Ukraine

The paper provides an assessment of the current wind energy potential in Ukraine, and discusses developmental prospects for wind-hydrogen power generation in the country. Hydrogen utilization is a highly promising option for Ukraine's energy system, environment, and business. In Ukraine, an optimal way towards clean zero-carbon energy production is through the development of the wind-hydrogen sector. In order to make it possible, the energy potential of industrial hydrogen production and use has to be studied thoroughly.Ukraine possesses huge resources for wind energy supply. At the beginning of 2020, the total installed capacity of Ukrainian wind farms was 1.17 GW. Wind power generation in Ukraine has significant advantages in comparison to the use of traditional sources such as thermal and nuclear energy.In this work, an assessment of the wind resource potential in Ukraine is made via the geographical approach suggested by the authors, and according to the «Methodical guidelines for the assessment of average annual power generation by a wind turbine based on the long-term wind speed observation data». The paper analyses the long-term dynamics of average annual wind speed at 40 Ukrainian weather stations that provide valid data. The parameter for the vertical wind profile model is calculated based on the data reanalysis for 10 m and 50 m altitudes. The capacity factor (CF) for modern wind turbine generators is determined. The CF spatial distribution for an average 3 MW wind turbine and the power generation potential for the wind power plants across the territory of Ukraine are mapped.Based on the wind energy potential assessment, the equivalent possible production of water electrolysis-derived green hydrogen is estimated. The potential average annual production of green hydrogen across the territory of Ukraine is mapped.It is concluded that Ukraine can potentially establish wind power plants with a total capacity of 688 GW on its territory. The average annual electricity production of this system is supposed to reach up to 2174 bln kWh. Thus, it can provide an average annual production of 483 billion Nm³ (43 million tons) of green hydrogen by electrolysis. The social efficiency of investments in wind-hydrogen electricity is presented.     

Statistical post‐processing of turbulence‐resolving weather forecasts for offshore wind power forecasting

Accurate short‐term power forecasts are crucial for the reliable and efficient integration of wind energy in power systems and electricity markets. Typically, forecasts for hours to days ahead are based on the output of numerical weather prediction models, and with the advance of computing power, the spatial and temporal resolutions of these models have increased substantially. However, high‐resolution forecasts often exhibit spatial and/or temporal displacement errors, and when regarding typical average performance metrics, they often perform worse than smoother forecasts from lower‐resolution models. Recent computational advances have enabled the use of large‐eddy simulations (LESs) in the context of operational weather forecasting, yielding turbulence‐resolving weather forecasts with a spatial resolution of 100 m or finer and a temporal resolution of 30 seconds or less. This paper is a proof‐of‐concept study on the prospect of leveraging these ultra high‐resolution weather models for operational forecasting at Horns Rev I in Denmark. It is shown that temporal smoothing of the forecasts clearly improves their skill, even for the benchmark resolution forecast, although potentially valuable high‐frequency information is lost. Therefore, a statistical post‐processing approach is explored on the basis of smoothing and feature engineering from the high‐frequency signal. The results indicate that for wind farm forecasting, using information content from both the standard and LES resolution models improves the forecast accuracy, especially with a feature selection stage, compared with using the information content solely from either source.     

Potential of power gain with improved yaw alignment

One of the primary criteria for extracting energy from the wind using horizontal axis upwind wind turbines is the ability to align the rotor axis with the dominating wind direction. The conventional way of estimating the direction of the incoming flow is by using transducers placed atop the nacelle and downwind of the rotor. Recent studies have suggested methods based on advanced upwind measurement technologies for estimating the inflow direction and improving the yaw alignment. In this study, the potential of increased power output with improved yaw alignment is investigated by assessing the performance of a current measurement and yaw control system. The performance is assessed by analyzing data containing upwind wind speed and direction measurements from a met mast, and yaw angle and power production measurements from an operating offshore wind turbine. The results of the analysis indicate that the turbine is operating with a wind speed‐dependent yaw error distribution. The theoretical annual energy production loss due to the yaw error distribution of the existing system is estimated to approximately 0.2%. Copyright © 2014 John Wiley & Sons, Ltd.     

Wind farm power prediction: a data‐mining approach

In this paper, models for short‐ and long‐term prediction of wind farm power are discussed. The models are built using weather forecasting data generated at different time scales and horizons. The maximum forecast length of the short‐term prediction model is 12 h, and the maximum forecast length of the long‐term prediction model is 84 h. The wind farm power prediction models are built with five different data mining algorithms. The accuracy of the generated models is analysed. The model generated by a neural network outperforms all other models for both short‐ and long‐term prediction. Two basic prediction methods are presented: the direct prediction model, whereby the power prediction is generated directly from the weather forecasting data, and the integrated prediction model, whereby the prediction of wind speed is generated with the weather data, and then the power is generated with the predicted wind speed. The direct prediction model offers better prediction performance than the integrated prediction model. The main source of the prediction error appears to be contributed by the weather forecasting data. Copyright © 2008 John Wiley & Sons, Ltd.     

Power curve measurement with a nacelle mounted lidar

Nacelle‐based lidars are an attractive alternative to conventional mast base reference wind instrumentation where the erection of a mast is expensive, for example offshore. In this paper, the use of this new technology for the specific application of wind turbine power performance measurement is tested. A pulsed lidar prototype, measuring horizontally, was installed on the nacelle of a multi‐megawatt wind turbine. A met mast with a top‐mounted cup anemometer standing at two rotor diameters in front of the turbine was used as a reference. After a data‐filtering step, the comparison of the 10 min mean wind speed measured by the lidar to that measured by the cup anemometer showed a deviation of about 1.4% on average. The power curve measured with the lidar was very similar to that measured with the cup anemometer although the lidar power curve was slightly distorted because of the deviation in wind speed measurements. A lower scatter in the power curve was observed for the lidar than for the mast. Since the lidar follows the turbine nacelle as it yaws, it always measures upwind. The wind measured by the lidar therefore shows a higher correlation with the turbine power fluctuations than the wind measured by the mast. Finally, the lidar is never in the wake of the turbine under test contrary to the cup anemometer; therefore, the wind sector usable for power curve measurement was larger than the sector for which the cup anemometer was not disturbed by any obstacle. The power curve obtained with the lidar for the wind sector in which the mast is in the wake of the turbine under test compared well with the power curve obtained on the standard sector. Copyright © 2013 John Wiley & Sons, Ltd.     

Wind energy potential estimation and micrositting on Izmir Institute of Technology Campus, Turkey

The aim of this study was to predict the wind energy content over the campus area of Izmir Institute of Technology. The wind data were collected at 10 and 30 m mast heights for a period of 16 months. Mean wind speeds were 7.03 and 8.14 m/s at 10 and 30 m mast heights, respectively. The ‘WAsP’ and ‘WindPRO’ softwares were used for the wind statistics and energy calculations. Suitable sites were selected according to the created wind power and energy maps. Wind turbines with nominal powers between 600 and 1500 kW were established for annual energy production calculations and best fitted ones were used for the micrositting.     

Wind forecasts for wind power generation using the Eta model

The goal of this article is to apply the regional atmospheric numerical weather prediction Eta model and describe its performance in validation of the wind forecasts for wind power plants. Wind power generation depends on wind speed. Wind speed is converted into power through characteristic curve of a wind turbine. The forecasting of wind speed and wind power has the same principle.Two sets of Eta model forecasts are made: one with a coarse resolution of 22 km, and another with a nested grid of 3.5 km, centered on the Nasudden power plants, (18.22°E, 57.07°N; 3 m) at island Gotland, Sweden. The coarse resolution forecasts were used for the boundary conditions of the nested runs. Verification is made for the nested grid model, for summers of 1996–1999, with a total number of 19 536 pairs of forecast and observed winds. The Eta model is compared against the wind observed at the nearest surface station and against the wind turbine tower 10 m wind. As a separate effort, the Eta model wind is compared against the wind from tower observations at a number of levels (38, 54, 75 and 96 m).Four common measures of accuracy relative to observations - mean difference (bias), mean absolute difference, root mean square difference and correlation coefficient are evaluated. In addition, scatter plots of the observed and predicted pairs at 10 and 96 m are generated. Average overall results of the Eta model 10 m wind fits to tower observations are: mean difference (bias) of 0.48 m/s, mean absolute difference of 1.14 m/s, root mean square difference of 1.38 m/s, and the correlation coefficient of 0.79. Average values for the upper tower observation levels are the mean difference (bias) of 0.40 m/s; mean absolute difference of 1.46 m/s; root mean square difference of 1.84 m/s and the correlation coefficient of 0.80.     

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.     

Evaluating the performance of wind turbines in selected locations in Oyo state,Nigeria

The wind speed distribution and wind energy potential are investigated in three selected locations in Oyo state using wind speed data that span between 12 and 20 years measured at 10 m height. In addition, the performance of selected small to medium size wind turbines in these sites were examined. The annual energy output and capacity factor for these turbines were determined. It was found that the monthly mean wind speeds in Oyo state ranges from 2.85 m/s to 5.20 m/s. While the monthly mean power density varies between 27.08 W/m² and 164.48 W/m², while the annual mean power density is in the range of 67.28 W/m² and 106.60 W/m². Based on annual energy output, wind turbines with cut-in wind speed of about 2.5 m/s and moderate rated wind speeds will be best suited for all the sites.     

A dynamical downscaling of ERA‐Interim in the North Sea using WRF with a 3 km grid—for wind resource applications

Large offshore wind energy projects are being planned and installed in the North Sea, and there is an urgent demand for high‐resolution atmospheric statistics to assess potential power production and revenue. Meteorological observations are too sparse to obtain those statistics, and global reanalyses like ERA‐Interim have a resolution too coarse in space and time to capture important small‐scale and terrain‐driven features of the atmospheric flow. We therefore dynamically downscale ERA‐Interim with the mesoscale model Weather Research and Forecasting to a 3 km grid to capture those unresolved features, for the period 1999–2008. The large‐scale flow is conditioned by spectral nudging, and we make use of observation nudging towards QuikSCAT near‐surface winds. The downscaling results in 100 m wind‐speed distributions and mean wind speeds, which are closer to the observations than ERA‐Interim, while the accuracy in terms of root‐mean‐square error decreases. The observation nudging partially counteracts this latter effect, improving the root‐mean‐square error of wind speed and direction by 0.5 m s^?1 and ~10°, respectively. We also introduce the power skill score, specifically designed to evaluate model performance within wind resource mapping. The power skill score confirms that the dynamical downscaling improves the distribution of wind speed in ranges where high accuracy is important for wind resource assessment. Copyright © 2016 John Wiley & Sons, Ltd.     

Uncertainty of power curve measurement with a two‐beam nacelle‐mounted lidar

Nacelle lidars are attractive for offshore measurements since they can provide measurements of the free wind speed in front of the turbine rotor without erecting a met mast, which significantly reduces the cost of the measurements. Nacelle‐mounted pulsed lidars with two lines of sight (LOS) have already been demonstrated to be suitable for use in power performance measurements. To be considered as a professional tool, however, power curve measurements performed using these instruments require traceable calibrated measurements and the quantification of the wind speed measurement uncertainty. Here we present and demonstrate a procedure fulfilling these needs. A nacelle lidar went through a comprehensive calibration procedure. This calibration took place in two stages. First with the lidar on the ground, the tilt and roll readings of the inclinometers in the nacelle lidar were calibrated. Then the lidar was installed on a 9m high platform in order to calibrate the wind speed measurement. The lidar's radial wind speed measurement along each LOS was compared with the wind speed measured by a calibrated cup anemometer, projected along the LOS direction. The various sources of uncertainty in the lidar wind speed measurement have been thoroughly determined: uncertainty of the reference anemometer, the horizontal and vertical positioning of the beam, the lack of homogeneity of the flow within the probe volume, lidar measurement mean deviation and standard uncertainty. The resulting uncertainty lies between 1 and 2% for the wind speed range between cut‐in and rated wind speed. Finally, the lidar was mounted on the nacelle of a wind turbine in order to perform a power curve measurement. The wind speed was simultaneously measured with a mast‐top mounted cup anemometer placed two rotor diameters upwind of the turbine. The wind speed uncertainty related to the lidar tilting was calculated based on the tilt angle uncertainty derived from the inclinometer calibration and the deviation of the measurement height from hub height. The resulting combined uncertainty in the power curve using the nacelle lidar was less than 10% larger on average than that obtained with the mast mounted cup anemometer. Copyright © 2015 John Wiley & Sons, Ltd.     

Wind resource evaluation in six selected high altitude locations in Nigeria

This paper presents an assessment of wind energy potentials of six selected high altitude locations within the North-West and North-East geopolitical regions, Nigeria, by using 36-year (1971–2007) wind speed data subjected to 2-parameter Weibull distribution functions. The results showed that the maximum mean wind speed is obtained in Katsina as 9.839 m/s while the minimum value of 3.397 m/s is got in Kaduna for all the locations considered. The annual wind power density and energy variation based on the Weibull analysis ranged from 368.92 W/m² and 3224.45 kWh/m²/year to 103.14 W/m² and 901.75 kWh/m²/year in Kano and Potiskum for the maximum and minimum values respectively. Furthermore, Katsina and Kano will be suitable for wind turbine installations while Gusau will only be appropriate for wind energy utilization using taller wind turbine towers whereas Kaduna, Bauchi and Potiskum will be considered marginal for wind power development based of their respective annual mean wind speeds and power densities.     

Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy

Increasing knowledge on wind shear models to strengthen their reliability appears as a crucial issue, markedly for energy investors to accurately predict the average wind speed at different turbine hub heights, and thus the expected wind energy output. This is particularly helpful during the feasibility study to abate the costs of a wind power project, thus avoiding installation of tall towers, or even more expensive devices such as LIDAR or SODAR.The power law (PL) was found to provide the finest representation of wind speed profiles and is hence the focus of the present study. Besides commonly used for vertical extrapolation of wind speed time series, the PL relationship between “instantaneous” wind profiles was demonstrated by Justus and Mikhail to be consistent with the height variation of Weibull distribution. Therefore, in this work a comparison is performed between these two different PL–based extrapolation approaches to assess wind resource to the turbine hub height: (i) extrapolation of wind speed time series, and (ii) extrapolation of Weibull wind speed distribution. The models developed by Smedman–Högström and Högström (SH), and Panofsky and Dutton (PD) were used to approach (i), while those from Justus and Mikhail (JM) and Spera and Richards (SR) to approach (ii). Models skill in estimating wind shear coefficient was also assessed and compared.PL extrapolation models have been tested over a flat and rough location in Apulia region (Southern Italy), where the role played by atmospheric stability and surface roughness, along with their variability with time and wind characteristics, has been also investigated. A 3-year (1998–2000) 1–h dataset, including wind measurements at 10 and 50 m, has been used. Based on 10–m wind speed observations, the computation of 50–m extrapolated wind resource, Weibull distribution and energy yield has been made. This work is aimed at proceeding the research issue addressed within a previous study, where PL extrapolation models were tested and compared in extrapolating wind resource and energy yield from 10 to 100 m over a complex–topography and smooth coastal site in Tuscany region (Central Italy). As a result, wind speed time series extrapolating models proved to be the most skilful, particularly PD, based on the similarity theory and thus addressing all stability conditions. However, comparable results are returned by the empirical JM Weibull distribution extrapolating model, which indeed proved to be preferable as being: (i) far easier to be used, as z₀–, stability–, and wind speed time series independent; (ii) more conservative, as wind energy is underpredicted rather than overpredicted.     

A spinner‐integrated wind lidar for enhanced wind turbine control

A field test with a continuous wave wind lidar (ZephIR) installed in the rotating spinner of a wind turbine for unimpeded preview measurements of the upwind approaching wind conditions is described. The experimental setup with the wind lidar on the tip of the rotating spinner of a large 80 m rotor diameter, 59 m hub height 2.3 MW wind turbine (Vestas NM80), located at Tjæreborg Enge in western Denmark is presented. Preview wind data at two selected upwind measurement distances, acquired during two measurement periods of different wind speed and atmospheric stability conditions, are analyzed. The lidar‐measured speed, shear and direction of the wind field previewed in front of the turbine are compared with reference measurements from an adjacent met mast and also with the speed and direction measurements on top of the nacelle behind the rotor plane used by the wind turbine itself. Yaw alignment of the wind turbine based on the spinner lidar measurements is compared with wind direction measurements from both the nearby reference met mast and the turbine's own yaw alignment wind vane. Furthermore, the ability to detect vertical wind shear and vertical direction veer in the inflow, through the analysis of the spinner lidar data, is investigated. Finally, the potential for enhancing turbine control and performance based on wind lidar preview measurements in combination with feed‐forward enabled turbine controllers is discussed. Copyright © 2012 John Wiley & Sons, Ltd.