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.

scholarly journals Analysis of offshore wind farm located on Baltic Sea

2019 ◽  
Vol 137 ◽  
pp. 01049
Author(s):  
Anna Sobotka ◽  
Kajetan Chmielewski ◽  
Marcin Rowicki ◽  
Justyna Dudzińska ◽  
Przemysław Janiak ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
The European Union ◽  
European Union Policy ◽  
Offshore Wind Farms ◽  
Wind Speeds ◽  
Wind Measurements ◽  
The Baltic

Poland is currently at the beginning of the energy transformation. Nowadays, most of the electricity generated in Poland comes from coal combustion. However, in accordance to the European Union policy of reducing the emission of carbon dioxide to the atmosphere, there are already plans to switch to low-emission energy sources in Poland, one of which are offshore wind farms. The article presents the current regulatory environment of the offshore wind energy in Poland, along with a reference to Polish and European decarbonisation plans. In the further part of the article, the methods of determining the kinetic energy of wind and the power curve of a wind turbine are discussed. Then, on the basis of historical data of wind speeds collected in the area of the Baltic Sea, calculations are carried out leading to obtain statistical distributions of power that could be generated by an exemplary wind farm with a power capacity of 400 MW, located at the place of wind measurements. On their basis, statistical differences in the wind power generation between years, months of the year and hours of the day are analysed.

scholarly journals Coastal Wind Measurements Using a Single Scanning LiDAR

2020 ◽  
Vol 12 (8) ◽  
pp. 1347 ◽  
Author(s):  
Susumu Shimada ◽  
Jay Prakash Goit ◽  
Teruo Ohsawa ◽  
Tetsuya Kogaki ◽  
Satoshi Nakamura
Keyword(s):  
Wind Speed ◽  
Goodness Of Fit ◽  
Sonic Anemometer ◽  
Data Availability ◽  
Horizontal Wind ◽  
Research Station ◽  
Lidar Measurement ◽  
Wind Speeds ◽  
Wind Measurements ◽  
Scanning Lidar

A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR was deployed on the landward end of the HORS pier. We compared the wind speed and direction data recorded by the scanning LiDAR to the observations obtained from a vertical profiling LiDAR installed at the opposite end of the pier, 400 m from the scanning LiDAR. The best practice for offshore wind measurements using a single scanning LiDAR was evaluated by comparing results from a total of nine experiments using several different scanning settings. A two-parameter velocity volume processing (VVP) method was employed to retrieve the horizontal wind speed and direction from the radial wind speed. Our experiment showed that, at the current offshore site with a negligibly small vertical wind speed component, the accuracy of the scanning LiDAR wind speeds and directions was sensitive to the azimuth angle setting, but not to the elevation angle setting. In addition to the validations for the 10-minute mean wind speeds and directions, the application of LiDARs for the measurement of the turbulence intensity (TI) was also discussed by comparing the results with observations obtained from a sonic anemometer, mounted at the seaward end of the HORS pier, 400 m from the scanning LiDAR. The standard deviation obtained from the scanning LiDAR measurement showed a greater fluctuation than that obtained from the sonic anemometer measurement. However, the difference between the scanning LiDAR and sonic measurements appeared to be within an acceptable range for the wind turbine design. We discuss the variations in data availability and accuracy based on an analysis of the carrier-to-noise ratio (CNR) distribution and the goodness of fit for curve fitting via the VVP method.

scholarly journals Wind sensing with drone-mounted wind lidars: proof of concept

2020 ◽  
Vol 13 (2) ◽  
pp. 521-536
Author(s):  
Nikola Vasiljević ◽  
Michael Harris ◽  
Anders Tegtmeier Pedersen ◽  
Gunhild Rolighed Thorsen ◽  
Mark Pitter ◽  
...  
Keyword(s):  
Wind Speed ◽  
Continuous Wave ◽  
Position Control ◽  
Horizontal Wind ◽  
Lidar Measurement ◽  
Proof Of Concept ◽  
Horizontal Wind Speed ◽  
Wind Speeds ◽  
Potential Applications ◽  
Wind Measurements

Abstract. The fusion of drone and wind lidar technology introduces the exciting possibility of performing high-quality wind measurements virtually anywhere. We present a proof-of-concept (POC) drone–lidar system and report results from several test campaigns that demonstrate its ability to measure accurate wind speeds. The POC system is based on a dual-telescope continuous-wave (CW) lidar, with drone-borne telescopes and ground-based optoelectronics. Commercially available drone and gimbal units are employed. The demonstration campaigns started with a series of comparisons of the wind speed measurements acquired by the POC system to simultaneous measurements performed by nearby mast-based sensors. On average, an agreement down to about 0.1 m s−1 between mast- and drone-based measurements of the horizontal wind speed is found. Subsequently, the extent of the flow disturbance caused by the drone downwash was investigated. These tests vindicated the somewhat conservative choice of lidar measurement ranges made for the initial wind speed comparisons. Overall, the excellent results obtained without any drone motion correction and with fairly primitive drone position control indicate the potential of drone–lidar systems in terms of accuracy and applications. The next steps in the development are outlined and several potential applications are discussed.

accuracy of wind measurements on towers or stacks1,2

1967 ◽  
Vol 48 (9) ◽  
pp. 665-675 ◽  
Author(s):  
Gerald C. Gill ◽  
Lars E. Olsson ◽  
Josef Sela ◽  
Motozo Suda
Keyword(s):  
Wind Speed ◽  
Scale Model ◽  
Wind Speeds ◽  
Lattice Type ◽  
Free Air ◽  
Scale Models ◽  
The Face ◽  
Wind Measurements ◽  
Wind Tunnel Data ◽  
Undisturbed Flow

Wind sensors mounted on towers and smokestacks do not always indicate the true free-air flow. To determine the probable errors in measurements of wind speed and direction around such structures, quarter-scale models have been tested in a large wind tunnel. Data on changes in wind speed and direction were obtained by using smoke, very small wind vanes, and a scale model propeller anemometer. Most emphasis has been placed on a relatively open lattice-type tower, but a solid tower and a stack were also studied. The analysis shows that in the wake of lattice-type towers disturbance is moderate to severe, and that in the wake of solid towers and stacks there is extreme turbulence, with reversal of flow. Recommendations for locating wind sensors in the wind field relative to the supporting structure are given for each of the three structures studied. Guidelines are suggested regarding probable errors in measurements of wind speed and direction around different supporting structures, as outlined below. For an open triangular tower with equal sides D, the wake is about 1-1/2D in width for a distance downwind of at least 6D. Sensors mounted 2 D out from the corner of such a tower will usually measure speeds within ± 10° of that of the undisturbed flow for an arc of about 330°. The disturbance by very dense towers and stacks is much greater. Wind sensors mounted 3 diameters out from the face of a stack will measure wind speeds within ± 10%, and directions within ± 10° of the undisturbed flow for an arc of about 180°.

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 IMPROVED FORMULAS FOR ESTIMATING OFFSHORE WINDS

1984 ◽  
Vol 1 (19) ◽  
pp. 149
Author(s):  
S.A. Hsu
Keyword(s):  
Continental Shelf ◽  
Coastal Waters ◽  
Offshore Wind ◽  
Wind Data ◽  
Wind Speeds ◽  
Coastal Marine ◽  
The World ◽  
Wind Measurements ◽  
The U.S

Differences in onshore and offshore wind speeds have long been known to exist [see, e.g., (2), (15), (16)]. Marine meteorologists in the weather services are required to forecast offshore winds. Many studies related to coastal marine sciences and engineering require wind data or estimates for offshore regions. Yet in situ measurements over water are often lacking. Traditionally, wind measurements over land, preferably near coasts, have been used to estimate offshore winds. However, because simultaneous onshore and offshore observations do not always exist, systematic studies such as simple comparisons between these two environments are also lacking. Only recently the U.S. National Oceanic and Atmospheric Administration (NOAA) deployed a network of buoys for longer term measurements over the continental shelf as well as farther offshore. All of these buoys are located in or near U.S. coastal waters. However, there are still vast regions in other parts of the world where such a network does not exist.

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>

Estimation of Weibull distribution for wind speeds along ship routes

2016 ◽  
Vol 231 (2) ◽  
pp. 464-480 ◽  
Author(s):  
Wengang Mao ◽  
Igor Rychlik
Keyword(s):  
Wind Speed ◽  
Weibull Distribution ◽  
North Atlantic ◽  
Gaussian Model ◽  
Energy Harvest ◽  
Wind Speeds ◽  
The North ◽  
Significant Difference ◽  
The North Atlantic ◽  
Wind Distribution

In order to evaluate potential benefits of new green shipping concepts that utilize wind power as auxiliary propulsion in ships or of offshore wind energy harvest, it is essential to have reliable wind speed statistics. A new method to find parameters in the Weibull distribution is given. It can be used either at a fixed offshore position or along arbitrary ship routes. The method employs a spatio-temporal transformed Gaussian model for wind speed variability. The model was fitted to 10 years’ ERA-Interim reanalysis data of wind speed. The proposed method to derive Weibull distribution is validated using wind speeds measured on-board by vessels sailing in the North Atlantic and the west region of the Mediterranean Sea. For the westbound voyages in the North Atlantic, the proposed method gives a good approximation of the observed wind distribution along those ship routes. For the eastbound voyages, significant difference is found between the observed wind distribution and that approximated by the proposed method. The suspected reason is attributed to the ship routing decisions of masters and software. Hence, models that consider only the wind climate description need to be supplemented with a method to take into account the effect of wind-aware routing plan.

scholarly journals Methods to Estimate Surface Roughness Length for Offshore Wind Energy

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Maryam Golbazi ◽  
Cristina L. Archer
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Statistical Method ◽  
Analytical Method ◽  
Roughness Length ◽  
Robust Statistics ◽  
Atmospheric Stability ◽  
Offshore Wind ◽  
Surface Roughness Length ◽  
Wind Measurements

The northeastern coast of the U.S. is projected to expand its offshore wind capacity from the existing 30 MW to over 22 GW in the next decade, yet, only a few wind measurements are available in the region and none at hub height (around 100 m today); thus, extrapolations are needed to estimate wind speed as a function of height. A common method is the log-law, which is based on surface roughness length (z0). No reliable estimates of z0 for the region have been presented in the literature. Here, we fill this knowledge gap using two field campaigns that were conducted in the Nantucket Sound at the Cape Wind (CW) platform: the 2003–2009 “CW Historical”, which collected wind measurements on a meteorological tower at three levels (20, 41, and 60 m AMSL) with sonic and cup/vane anemometers, and the 2013–2014 IMPOWR (Improving the Mapping and Prediction of Offshore Wind Resources), which collected high-frequency wind and flux measurements at 12 m AMSL. We tested three different methods to calculate z0: (1) analytical method, dependent on friction velocity u∗ and a stability function ψ; (2) the Charnock relationship between z0 and u∗; and (3) a statistical method based on wind speed observed at the three levels. The first two methods are physical, whereas the statistical method is purely mathematical. Comparing mean and median of z0, we find that the median is a more robust statistics because the mean varies by over four orders of magnitude across the three methods and the two campaigns. In general, the median z0 exhibits little seasonal variability and a weak dependency on atmospheric stability, which was predominantly unstable (54–67%). With the goal of providing the most accurate estimates of wind speed near the hub height of modern turbines, the statistical method, despite delivering unrealistic z0 values at times, gives the best estimates of 60 m winds, even when the 5 m wind speed from a nearby buoy is used as the reference. The unrealistic z0 values are caused by nonmonotonic wind speed profiles, occurring about 41% of the time, and should not be rejected because they produce realistic fits. Furthermore, the statistical method outperforms the other two even though it does not need any stability information. In summary, if wind speed data from multiple levels are available, as is the case with vertically pointing floating lidar and meteorological towers, the statistical method is recommended, regardless of the seemingly unrealistic z0 values at times. If multilevel wind speeds are not available but advanced sonic anemometry is available at one level, the analytical method is recommended over Charnock’s. Lastly, if a single, constant value of z0 is sought after to characterize the region, we recommend the median from the statistical method, i.e., 6.09×10−3 m, which is typical of rough seas.

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