Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements

This study investigates how short-term lidar measurements can be used in combination with a mast measurement to improve vertical extrapolation of wind speed. Several methods are developed and analyzed for their performance in estimating the mean wind speed, the wind speed distribution, and the energy yield of an idealized wind turbine at the target height of the extrapolation. These methods range from directly using the wind shear of the short-term measurement to a classification approach based on commonly available environmental parameters using linear regression. The extrapolation strategies are assessed using data of ten wind profiles up to 200 m measured at different sites in Germany. Different mast heights and extrapolation distances are investigated. The results show that, using an appropriate extrapolation strategy, even a very short-term lidar measurement can significantly reduce the uncertainty in the vertical extrapolation of wind speed. This observation was made for short as well as for very large extrapolation distances. Among the investigated methods, the linear regression approach yielded better results than the other methods. Integrating environmental variables into the extrapolation procedure further increased the performance of the linear regression approach. Overall, the extrapolation error in (theoretical) energy yield was decreased by around 50% to 70% on average for a lidar measurement of approximately one to two months depending on the extrapolation height and distance. The analysis of seasonal patterns revealed that appropriate extrapolation strategies can also significantly reduce the seasonal bias that is connected to the season during which the short-term measurement is performed.

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The Potential Wind Power Resource in Central-South of the Constanta County

Mathematical Modelling in Civil Engineering ◽

10.2478/mmce-2019-0007 ◽

2019 ◽

Vol 15 (3) ◽

pp. 1-12

Author(s):

Emilian Boboc

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Extreme Values ◽

Reanalysis Data ◽

Value Theory ◽

Physical Models ◽

Structural Safety ◽

Energy Yield ◽

Wind Speeds ◽

Mean Wind Speed

Abstract Usually, wind turbine generator’s structures or radio masts are located in wind exposed sites. The paper aims to investigate the wind conditions in the nearby area of Cobadin Commune, Constanta County, Romania at heights of 150-200m above the surface using global reanalysis data sets CFSR, ERA 5, ERA I and MERRA 2. Using the extreme value theory and the physical models of the datasets, the research focuses on the assessment of the maximum values that are expected for the wind speeds, but the wind statistics created can be used for a further wind or energy yield calculation. Without reaching the survival wind speed for wind turbine generators, with mean wind speed values higher than 7 m/s and considering the cut-in and cut-out wind speeds of 3 m/s, respectively 25 m/s, the site can be exploited in more than 90% of the time to generate electricity, thus, the paper is addressed to the investors in the energy of renewable sources. At the same time, the insights of the wind characteristics and the knowledge of the extreme values of the wind speed can be useful, not just for the designers, in the rational assessment of the structural safety of wind turbines, but also those evaluating the insured losses.

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Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Range, Ecuador

Renewable Energy ◽

10.1016/j.renene.2021.10.070 ◽

2021 ◽

Author(s):

Germánico López ◽

Pablo Arboleya

Keyword(s):

Wind Speed ◽

Linear Regression ◽

Complex Terrain ◽

Regression Models ◽

Linear Regression Models ◽

Short Term ◽

Wind Speed Forecasting ◽

The Andes

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Investigation of Marine Wind Veer Characteristics Using Wind Lidar Measurements

Atmosphere ◽

10.3390/atmos11111178 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1178

Author(s):

Zhenru Shu ◽

Qiusheng Li ◽

Yuncheng He ◽

Pak Wai Chan

Keyword(s):

Wind Speed ◽

Atmospheric Stability ◽

Offshore Wind ◽

Hilly Terrain ◽

Open Sea ◽

Wide Range ◽

Lidar Measurements ◽

Mean Wind Speed ◽

Wind Characteristics ◽

Wind Lidar

A proper understanding of marine wind characteristics is of essential importance across a wide range of engineering applications. While the offshore wind speed and turbulence characteristics have been examined extensively, the knowledge of wind veer (i.e., turning of wind with height) is much less understood and discussed. This paper presents an investigation of marine wind field with particular emphasis on wind veer characteristics. Extensive observations from a light detection and ranging (Lidar) system at an offshore platform in Hong Kong were examined to characterize the wind veer profiles up to a height of 180 m. The results underscored the occurrence of marine wind veer, with a well-defined two-fold vertical structure. The observed maximum wind veer angle exhibits a reverse correlation with mean wind speed, which decreases from 2.47° to 0.59° for open-sea terrain, and from 7.45° to 1.92° for hilly terrain. In addition, seasonal variability of wind veer is apparent, which is most pronounced during spring and winter due to the frequent occurrence of the low-level jet. The dependence of wind veer on atmospheric stability is evident, particularly during winter and spring. In general, neutral stratification reveals larger values of wind veer angle as compared to those in stable and unstable stratification conditions.

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Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

Applied Energy ◽

10.1016/j.apenergy.2013.08.025 ◽

2014 ◽

Vol 113 ◽

pp. 690-705 ◽

Cited By ~ 146

Author(s):

Kuilin Chen ◽

Jie Yu

Keyword(s):

Kalman Filter ◽

Wind Speed ◽

State Space ◽

Support Vector Regression ◽

Unscented Kalman Filter ◽

Support Vector ◽

Short Term ◽

Wind Speed Prediction ◽

Regression Approach ◽

Speed Prediction

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Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements

Wind Energy Science ◽

10.5194/wes-5-1449-2020 ◽

2020 ◽

Vol 5 (4) ◽

pp. 1449-1468

Author(s):

Frauke Theuer ◽

Marijn Floris van Dooren ◽

Lueder von Bremen ◽

Martin Kühn

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Time Synchronization ◽

Stock Exchange ◽

Scanning Speed ◽

Energy System ◽

Offshore Wind ◽

Doppler Lidar ◽

Short Term ◽

Lidar Measurements

Abstract. Decreasing gate closure times on the electricity stock exchange market and the rising share of renewables in today's energy system causes an increasing demand for very short-term power forecasts. While the potential of dual-Doppler radar data for that purpose was recently shown, the utilization of single-Doppler lidar measurements needs to be explored further to make remote-sensing-based very short-term forecasts more feasible for offshore sites. The aim of this work was to develop a lidar-based forecasting methodology, which addresses a lidar's comparatively low scanning speed. We developed a lidar-based forecast methodology using horizontal plan position indicator (PPI) lidar scans. It comprises a filtering methodology to recover data at far ranges, a wind field reconstruction, a time synchronization to account for time shifts within the lidar scans and a wind speed extrapolation to hub height. Applying the methodology to seven free-flow turbines in the offshore wind farm Global Tech I revealed the model's ability to outperform the benchmark persistence during unstable stratification, in terms of deterministic as well as probabilistic scores. The performance during stable and neutral situations was significantly lower, which we attribute mainly to errors in the extrapolation of wind speed to hub height.

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Evaluation of Wind Energy Potential for Power Generation in some Coastal Cities in Akwa Ibom State, Nigeria

ELEKTRIKA- Journal of Electrical Engineering ◽

10.11113/elektrika.v17n3.83 ◽

2018 ◽

Vol 17 (3) ◽

pp. 1-9

Author(s):

Ye-Obong Udoakah ◽

Ubong Ikafia

Keyword(s):

Wind Speed ◽

Empirical Method ◽

Energy Potential ◽

Mean Power ◽

Coastal Cities ◽

Wind Potential ◽

Distribution Parameters ◽

Mean Wind Speed ◽

Wind Energy Potential ◽

Using Data

The wind potential of three coastal sites, Ibeno (Latitude 4° 33' 54.2"N & Longitude 8° 4' 21.3"E), Oron (Latitude 4°48'26.53"N & Longitude 8°14'15.74"E) and Okobo (Latitude 4°41'48.48"N & Longitude 8°15'23.23"E) in Akwa Ibom State, Nigeria was investigated. Using data obtained from the Nigeria Meteorological Agency (NIMET) and the Maritime Academy of Nigeria, Oron for Ibeno, Okobo and Oron respectively for a period of five years (2012-2016), a statistical analysis was performed. Using empirical method, the overall average Weibull distribution parameters in terms of k and c, mean power density and mean wind speed were determined for the three locations to be 3.29, 4.74 & 4.11, 7.72, 6.54 & 6.40 m/s, 344.68, 236.10 & 185.18 w/m2 and 8.24, 7.26 & 6.70 m/s respectively for Ibeno, Oron and Okobo. The results indicate that all the locations had a wind speed range higher than the 5-6 m/s recommended for electricity generation and a wind density value greater than 100 W/m2. Resulting from the analysis, the overall annual energy production was projected to be 15.23, 10.45 & 8.19 GW for Ibeno, Oron and Okobo respectively, leading to the conclusion that the three cities located along the coastal lines of Akwa Ibom, would be viable for commercial wind power production.

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A Basic Wind Speed Map for Oman

The Journal of Engineering Research [TJER] ◽

10.24200/tjer.vol11iss2pp64-78 ◽

2014 ◽

Vol 11 (2) ◽

pp. 64

Author(s):

A.S. Alnuaimi ◽

M.A. Mohsin ◽

K.H. Al-Riyami

Keyword(s):

Wind Speed ◽

Return Period ◽

Structural Design ◽

Short Term ◽

Wind Speed Data ◽

Wind Speeds ◽

Mean Wind Speed

The aim of this research was to develop the first basic wind speed map for Oman. Hourly mean wind speed records from 40 metrological stations were used in the calculation. The period of continuous records ranged from 4–37 years. The maximum monthly hourly mean and the maxima annual hourly mean wind speed data were analysed using the Gumbel and Gringorten methods. Both methods gave close results in determining basic wind speeds, with the Gumbel method giving slightly higher values. Due to a lack of long-term records in some regions of Oman, basic wind speeds were extrapolated for some stations with only short-term records, which were defined as those with only 4– 8 years of continuous records; in these cases, monthly maxima were used to predict the long-term basic wind speeds. Accordingly, a basic wind speed map was developed for a 50-year return period. This map was based on basic wind speeds calculated from actual annual maxima records of 29 stations with at least 9 continuous years of records as well as predicted annual maxima wind speeds for 11 short-term record stations. The basic wind speed values ranged from 16 meters/second (m/s) to 31 m/s. The basic wind speed map developed in this research is recommended for use as a guide for structural design in Oman.

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Aero-elastic load validation in wake conditions using nacelle-mounted lidar measurements

10.5194/wes-2020-8 ◽

2020 ◽

Author(s):

Davide Conti ◽

Nikolay Dimitrov ◽

Alfredo Peña

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Turbulence Intensity ◽

Sensor Data ◽

Wave System ◽

Horizontal Shear ◽

Reference Case ◽

Elastic Load ◽

Lidar Measurements ◽

Mean Wind Speed

Abstract. We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed and a continuous wave system, were installed on the nacelle of a 2.3 MW wind turbine operating in free-, partial- and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters and wake disturbances are present for two opposite wind direction sectors. We account for wake-induced effects by means of wind field parameters commonly used as inputs for load simulations, which are reconstructed using lidar measurements. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error and turbulence-spectra parameters. The uncertainty and bias of aero-elastic load predictions are quantified against wind turbine on-board sensor data. We consider mast-based load assessments in free wind as a reference case and assess the uncertainty in lidar-based power and load predictions when the turbine is operating in partial- and full-wake. Compared to the reference case, the simulations in wake conditions lead to an increase of the relative error as low as 4 %. It is demonstrated that the mean wind speed, turbulence intensity and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a rotor equivalent wind speed model are the most challenging aspects of load validation in wake conditions.

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