scholarly journals Can Satellite Sampling of Offshore Wind Speeds Realistically Represent Wind Speed Distributions? Part II: Quantifying Uncertainties Associated with Distribution Fitting Methods

2004 ◽  
Vol 43 (5) ◽  
pp. 739-750 ◽  
Author(s):  
S. C. Pryor ◽  
M. Nielsen ◽  
R. J. Barthelmie ◽  
J. Mann
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Remotely Sensed ◽  
Offshore Wind ◽  
Data Series ◽  
Parameter Estimates ◽  
Distribution Fitting ◽  
Wind Speeds ◽  
Distribution Parameters ◽  
Independent Observations

Abstract Remote sensing tools represent an attractive proposition for measuring wind speeds over the oceans because, in principle, they also offer a mechanism for determining the spatial variability of flow. Presented here is the continuation of research focused on the uncertainties and biases currently present in these data and quantification of the number of independent observations (scenes) required to characterize various parameters of the probability distribution of wind speeds. Theoretical and empirical estimates are derived of the critical number of independent observations (wind speeds derived from analysis of remotely sensed scenes) required to obtain probability distribution parameters with an uncertainty of ±10% and a confidence level of 90% under the assumption of independent samples, and it is found that approximately 250 independent observations are required to fit the Weibull distribution parameters. Also presented is an evaluation of Weibull fitting methods and determination of the fitting method based on the first and third moments to exhibit the “best” performance for pure Weibull distributions. Further examined is the ability to generalize parameter uncertainty bounds presented previously by Barthelmie and Pryor for distribution parameter estimates from sparse datasets; these were found to be robust and hence generally applicable to remotely sensed wind speed data series.

scholarly journals Dutch Offshore Wind Atlas Validation against Cabauw Meteomast Wind Measurements

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6558
Author(s):  
Steven Knoop ◽  
Pooja Ramakrishnan ◽  
Ine Wijnant
Keyword(s):  
Wind Speed ◽  
Offshore Wind ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
Weather Model ◽  
Significant Difference ◽  
Wind Measurements ◽  
Sea Wind ◽  
Global Datasets ◽  
Meteorological Institute

The Dutch Offshore Wind Atlas (DOWA) is validated against wind speed and direction measurements from the Cabauw meteorological mast for a 10-year period and at heights between 10 m and 200 m. The validation results are compared to the Royal Netherlands Meteorological Institute (KNMI) North Sea Wind (KNW) atlas. It is found that the average difference (bias) between DOWA wind speeds and those measured at Cabauw varies for the different heights between −0.1 m/s to 0.3 m/s. Significant differences between DOWA and KNW are only found at altitudes of 10 m and 20 m, where KNW performs better. For heights above 20 m, there is no significant difference between DOWA and KNW with respect to the 10-year averaged wind speed bias. The diurnal cycle is better captured by DOWA compared to KNW, and the hourly correlation is slightly improved. In addition, a comparison with the global European Center for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and ERA5 reanalyses (used for KNW and DOWA, respectively) is made, highlighting the added skill provided by downscaling those global datasets with the weather model HARMONIE.

On the wind resource in Algeria: Probability distributions evaluation

2020 ◽  
pp. 095765092097588
Author(s):  
Houdayfa Ounis ◽  
Nawel Aries
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Probability Distributions ◽  
Resource Assessment ◽  
Descriptive Statistics ◽  
The Other ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
The Mean ◽  
Wind Resource

The present study aims to present a contribution to the wind resource assessment in Algeria using ERA-Interim reanalysis. Firstly, the ERA-Interim reanalysis 10 m wind speed data are considered for the elaboration of the mean annual 10 m wind speed map for a period starting from 01-01-2000 to 31-12-2017. Moreover, the present study intends to highlight the importance of the descriptive statistics other than the mean in wind resource assessment. On the other hand, this study aims also to select the proper probability distribution for the wind resource assessment in Algeria. Therefore, nine probability distributions were considered, namely: Weibull, Gamma, Inverse Gaussian, Log Normal, Gumbel, Generalized Extreme Value (GEV), Nakagami, Generalized Logistic and Pearson III. Furthermore, in combination with the distribution, three parameter estimation methods were considered, namely, Method of Moment, Maximum Likelihood Method and L-Moment Method. The study showed that Algeria has several wind behaviours due to the diversified topographic, geographic and climatic properties. Moreover, the annual mean 10 m wind speed map showed that the wind speed varies from 2.3 to 5.3 m/s, where 73% of the wind speeds are above 3 m/s. The map also showed that the Algerian Sahara is windiest region, while, the northern fringe envelopes the lowest wind speeds. In addition, it has been shown that the study of the mean wind speeds for the evaluation of the wind potential alone is not enough, and other descriptive statistics must be considered. On the other hand, among the nine considered distribution, it appears that the GEV is the most appropriate probability distribution. Whereas, the Weibull distribution showed its performance only in regions with high wind speeds, which, implies that this probability distribution should not be generalized in the study of the wind speed in Algeria.

scholarly journals New methods to improve the vertical extrapolation of near-surface offshore wind speeds

2021 ◽  
Author(s):  
Mike Optis ◽  
Nicola Bodini ◽  
Mithu Debnath ◽  
Paula Doubrawa
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Model Performance ◽  
Learning Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Near Surface ◽  
Wind Speeds ◽  
Machine Learning Model ◽  
Surface Measurements

Abstract. Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in U.S. Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air-sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10-m wind speeds from spatially resolved satellite-based wind atlases.

scholarly journals MISALIGNMENT AND LAG TIME OF WIND AND WAVE OCCURRENCE BASED ON 10 YEARS MEASUREMENTS IN THE NORTH SEA NEAR THE GERMAN COAST

2018 ◽  
pp. 13
Author(s):  
Arndt Hildebrandt ◽  
Remo Cossu
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
North Sea ◽  
Offshore Wind ◽  
Wave Loads ◽  
Wind Speeds ◽  
The North ◽  
The North Sea ◽  
The Impact ◽  
The Waves

There are several intentions to analyze the correlation of wind and wave data, especially in the North Sea. Fatigue damage is intensified by wind and wave loads acting from different directions, due to the misaligned aerodynamic damping of the rotor regarding the wave loads from lateral directions. Furthermore, construction time and costs are mainly driven by the operational times of the working vessels, which strongly depend on the wind and wave occurrence and correlation. Turbulent wind can rapidly change its direction and intensity, while the inert water waves react slowly in relation to the wind profile. Tuerk (2008) investigates the impact of wind and turbulence on offshore wind turbines by analyzing data of four years. The study shows that the wave height is increasing with higher wind speeds but when the wind speed drops the reaction of the waves is postponed. The dependence of the wave height on the wind speed is varying because of the atmospheric stability and different wind directions. Fischer et al. (2011) estimated absolute values of misalignment between wind and waves located in the Dutch North Sea. The study presents decreasing misalignment for increasing wind speeds, ranging up to 90 degrees for wind speeds below 12 m/s and up to 30 degrees for wind speeds above 20 m/s. Bredmose et al. (2013) present a method of offshore wind and wave simulation by using metocean data. The study describes characteristics of the wind and wave climate for the North and Baltic Sea as well as the directional distribution of wind and waves. Güner et al. (2013) cover the development of a statistical wave model for the Karaburun coastal zone located at the southwest coast of the Black Sea with the help of wind and wave measurements and showed that the height of the waves is directly correlating with the duration of the wind for the last four hours.

German Bight Storminess over the Last Century

2020 ◽  
Author(s):  
Daniel Krieger ◽  
Oliver Krueger ◽  
Frauke Feser ◽  
Ralf Weisse ◽  
Birger Tinz ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
German Bight ◽  
Geostrophic Wind ◽  
Air Pressure ◽  
Offshore Wind ◽  
Pressure Data ◽  
Storm Activity ◽  
Wind Speeds ◽  
North East

<p>Assessing past storm activity provides valuable knowledge for economic and ecological sectors, such as the renewable energy sector, insurances, or health and safety. However, long time series of wind speed measurements are often not available as they are usually hampered by inhomogeneities due to changes in the surroundings of a measurement site, station relocations, and changes in the instrumentation. On the contrary, air pressure measurements provide mostly homogeneous time series as the air pressure is usually unaffected by such factors.</p><p>Therefore, we perform statistical analyses on historical pressure data measured at several locations within the German Bight (southeastern North Sea) between 1897 and 2018. We calculate geostrophic wind speeds from triplets of mean sea level pressure observations that form triangles over the German Bight. We then investigate the evolution of German Bight storminess from 1897 to 2018 through analyzing upper quantiles of geostrophic wind speeds, which act as a proxy for past storm activity. The derivation of storm activity is achieved by enhancing the established triangle proxy method via combining and merging storminess time series from numerous partially overlapping triangles in an ensemble-like manner. The utilized approach allows for the construction of robust, long-term and subdaily German Bight storminess time series. Further, the method provides insights into the underlying uncertainty of the time series.</p><p>The results show that storm activity over the German Bight is subject to multidecadal variability. The latest decades are characterized by an increase in activity from the 1960s to the 1990s, followed by a decline lasting into the 2000s and below-average activity up until present. The results are backed through a comparison with reanalysis products from four datasets, which provide high-resolution wind and pressure data starting in 1979 and offshore wind speed measurements taken from the FINO-WIND project. This study also finds that German Bight storminess positively correlates with storminess in the North-East Atlantic in general. In certain years, however, notably different levels of storm activity in the two regions can be found, which likely result from shifted large-scale circulation patterns.</p>

scholarly journals Doppler Lidar Investigations of Wind Turbine Near-Wakes and LES Modeling with New Porous Disc Approach

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2101
Author(s):  
Takanori Uchida ◽  
Tadasuke Yoshida ◽  
Masaki Inui ◽  
Yoshihiro Taniyama
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Wind Speeds ◽  
Wind Speed Distribution ◽  
Wake Model ◽  
Turbine Wake ◽  
Swept Area

Many bottom-mounted offshore wind farms are currently planned for the coastal areas of Japan, in which wind speeds of 6.0–10.0 m/s are extremely common. The impact of such wind speeds is very relevant for the realization of bottom-mounted offshore wind farms. In evaluating the feasibility of these wind farms, therefore, strict evaluation at wind speeds of 6.0–10.0 m/s is important. In the present study, the airflow characteristics of 2 MW-class downwind wind turbine wake flows were first investigated using a vertically profiling remote sensing wind measurement device (lidar). The wind turbines used in this study are installed at the point where the sea is just in front of the wind turbines. A ground-based continuous-wave (CW) conically scanning wind lidar system (“ZephIR ZX300”) was used. Focusing on the wind turbine near-wakes, the detailed behaviors were considered. We found that the influence of the wind turbine wake, that is, the wake loss (wind velocity deficit), is extremely large in the wind speed range of 6.0–10.0 m/s, and that the wake loss was almost constant at such wind speeds (6.0–10.0 m/s). It was additionally shown that these results correspond to the distribution of the thrust coefficient of the wind turbine. We proposed a computational fluid dynamics (CFD) porous disk (PD) wake model as an intermediate method between engineering wake models and CFD wake models. Based on the above observations, the wind speed range for reproducing the behavior of the wind turbine wakes with the CFD PD wake model we developed was set to 6.0–10.0 m/s. Targeting the vertical wind speed distribution in the near-wake region acquired in the “ZephIR ZX300”, we tuned the parameters of the CFD PD wake model (CRC = 2.5). We found that in practice, when evaluating the mean wind velocity deficit due to wind turbine wakes, applying the CFD PD wake model in the wind turbine swept area was very effective. That is, the CFD PD wake model can reproduce the mean average wind speed distribution in the wind turbine swept area.

Empirical Models of the Probability Distribution of Sea Surface Wind Speeds

2007 ◽  
Vol 20 (23) ◽  
pp. 5798-5814 ◽  
Author(s):  
Adam Hugh Monahan
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Maximum Entropy ◽  
Surface Wind ◽  
Empirical Models ◽  
Sea Surface ◽  
Wind Speeds ◽  
Sea Surface Wind ◽  
Non Gaussian ◽  
Vector Wind

Abstract This study considers the probability distribution of sea surface wind speeds, which have historically been modeled using the Weibull distribution. First, non-Weibull structure in the observed sea surface wind speeds (from SeaWinds observations) is characterized using relative entropy, a natural information theoretic measure of the difference between probability distributions. Second, empirical models of the probability distribution of sea surface wind speeds, parameterized in terms of the parameters of the vector wind probability distribution, are developed. It is shown that Gaussian fluctuations in the vector wind cannot account for the observed features of the sea surface wind speed distribution, even if anisotropy in the fluctuations is accounted for. Four different non-Gaussian models of the vector wind distribution are then considered: the bi-Gaussian, the centered gamma, the Gram–Charlier, and the constrained maximum entropy. It is shown that so long as the relationship between the skewness and kurtosis of the along-mean sea surface wind component characteristic of observations is accounted for in the modeled probability distribution, then all four vector wind distributions are able to simulate the observed mean, standard deviation, and skewness of the sea surface wind speeds with an accuracy much higher than is possible if non-Gaussian structure in the vector winds is neglected. The constrained maximum entropy distribution is found to lead to the best simulation of the wind speed probability distribution. The significance of these results for the parameterization of air/sea fluxes in general circulation models is discussed.

scholarly journals New methods to improve the vertical extrapolation of near-surface offshore wind speeds

2021 ◽  
Vol 6 (3) ◽  
pp. 935-948
Author(s):  
Mike Optis ◽  
Nicola Bodini ◽  
Mithu Debnath ◽  
Paula Doubrawa
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Model Performance ◽  
Learning Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Near Surface ◽  
Wind Speeds ◽  
Machine Learning Model ◽  
Surface Measurements

Abstract. Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in US Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air–sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10 m wind speeds from spatially resolved satellite-based wind atlases.

scholarly journals Power fluctuations in high-installation- density offshore wind fleets

2021 ◽  
Vol 6 (2) ◽  
pp. 461-476
Author(s):  
Juan Pablo Murcia Leon ◽  
Matti Juhani Koivisto ◽  
Poul Sørensen ◽  
Philippe Magnant
Keyword(s):  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Offshore Wind ◽  
Weather Data ◽  
Storm Events ◽  
High Wind ◽  
Wind Speeds ◽  
Power Fluctuations ◽  
The Impact

Abstract. Detailed simulation of wind generation as driven by weather patterns is required to quantify the impact on the electrical grid of the power fluctuations in offshore wind power fleets. This paper focuses on studying the power fluctuations of high-installation-density offshore fleets since they present a growing challenge to the operation and planning of power systems in Europe. The Belgian offshore fleet is studied because it has the highest density of installation in Europe by 2020, and a new extension is expected to be fully operational by 2028. Different stages of the future installed capacity, turbine technology, and turbine storm shutdown technologies are examined and compared. This paper analyzes the distribution of power fluctuations both overall and during high wind speeds. The simulations presented in this paper use a new Student t-distributed wind speed fluctuation model that captures the missing spectra from the weather reanalysis simulations. An updated plant storm shutdown model captures the plant behavior of modern high-wind-speed turbine operation. Detailed wake modeling is carried out using a calibrated engineering wake model to capture the Belgium offshore fleet and its tight farm-to-farm spacing. Long generation time series based on 37 years of historical weather data in 5 min resolution are simulated to quantify the extreme fleet-level power fluctuations. The model validation with respect to the operational data of the 2018 fleet shows that the methodology presented in this paper can capture the distribution of wind power and its spatiotemporal characteristics. The results show that the standardized generation ramps are expected to be reduced towards the 4.4 GW of installations due to the larger distances between plants. The most extreme power fluctuations occur during high wind speeds, with large ramp-downs occurring in extreme storm events. Extreme ramp-downs are mitigated using modern turbine storm shutdown technologies, while extreme ramp-ups can be mitigated by the system operator. Extreme ramping events also occur at below-rated wind speeds, but mitigation of such ramping events remains a challenge for transmission system operators.

scholarly journals Power Fluctuations In High Installation Density Offshore Wind Fleets

2020 ◽  
Author(s):  
Juan Pablo Murcia Leon ◽  
Matti Juhani Koivisto ◽  
Poul Sørensen ◽  
Philippe Magnant
Keyword(s):  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Offshore Wind ◽  
Weather Data ◽  
Storm Events ◽  
High Wind ◽  
Wind Speeds ◽  
Power Fluctuations ◽  
The Impact

Abstract. Detailed simulation of wind generation as driven by weather patterns is required to quantify the impact on the electrical grid of the power fluctuations in offshore wind power fleets. This article focuses on studying the power fluctuations of high installation density offshore fleets since they present a growing challenge to the operation and planning of power systems in Europe. The Belgian offshore fleet is studied because it has the highest density of installation in Europe by 2020 and a new extension is expected to start operations by 2028. Different stages of the future installed capacity, turbine technology and turbine storm shutdown technologies are examined and compared. This paper analyzes the distribution of power fluctuations both overall and during high wind speeds. The simulations presented in this article use a new t-student distributed wind speed fluctuations model that captures the missing spectra from the weather reanalysis-simulations. An updated plant storm shutdown model captures the plant behavior of modern high wind speed turbine operation. Detailed wake modeling is carried out using a calibrated engineering wake model in order to capture the Belgium offshore fleet and its tight farm to farm spacing. Long generation time series based on 37 years of historical weather data in 5 min resolution are simulated in order to quantify the extreme fleet-level power fluctuations. The model validation with respect the operational data of the 2018 fleet shows that the methodology presented in this article is able to capture the distribution of wind power and its spatio-temporal characteristics. The results show that the standardized generation ramps are expected to be reduced towards the 4.4 GW of installations due to the larger distances between plants. The most extreme power fluctuations occur during high wind speeds, with large down-ramps occurring in extreme storm events. Extreme down-ramps are mitigated using modern turbine storm shutdown technologies; while extreme up-ramps can be mitigated by the system operator. Extreme ramping events also occur at bellow rated wind speeds, but mitigation of such ramping events remains a challenge for transmission system operators.

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