Lidar arc scan uncertainty reduction through scanning geometry optimization

Abstract. Doppler lidars are frequently operated in a mode referred to as arc scans, wherein the lidar beam scans across a sector with a fixed elevation angle and the resulting measurements are used to derive an estimate of the n minute horizontal mean wind velocity (speed and direction). Previous studies have shown that the uncertainty in the measured wind speed originates from turbulent wind fluctuations and depends on the scan geometry (the arc span and the arc orientation). This paper is designed to provide guidance on optimal scan geometries for two key applications in the wind energy industry: wind turbine power performance analysis and annual energy production. We present a quantitative analysis of the retrieved wind speed uncertainty derived using a theoretical model with the assumption of isotropic and frozen turbulence, and observations from three sites that are onshore with flat terrain, onshore with complex terrain and offshore, respectively. The results from both the theoretical model and observations show that the uncertainty is scaled with the turbulence intensity such that the relative standard error on the 10 min mean wind speed is about 30 % of the turbulence intensity. The uncertainty in both retrieved wind speeds and derived wind energy production estimates can be reduced by aligning lidar beams with the dominant wind direction, increasing the arc span and lowering the number of beams per arc scan. Large arc spans should be used at sites with high turbulence intensity and/or large wind direction variation when arc scans are used for wind resource assessment.

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Lidar arc scan uncertainty reduction through scanning geometry optimization

Atmospheric Measurement Techniques ◽

10.5194/amt-9-1653-2016 ◽

2016 ◽

Vol 9 (4) ◽

pp. 1653-1669 ◽

Cited By ~ 7

Author(s):

Hui Wang ◽

Rebecca J. Barthelmie ◽

Sara C. Pryor ◽

Gareth. Brown

Keyword(s):

Theoretical Model ◽

Wind Speed ◽

Wind Energy ◽

Turbulence Intensity ◽

Wind Direction ◽

Energy Production ◽

Elevation Angle ◽

Relative Standard Error ◽

Power Performance ◽

Relative Standard

Abstract. Doppler lidars are frequently operated in a mode referred to as arc scans, wherein the lidar beam scans across a sector with a fixed elevation angle and the resulting measurements are used to derive an estimate of the n minute horizontal mean wind velocity (speed and direction). Previous studies have shown that the uncertainty in the measured wind speed originates from turbulent wind fluctuations and depends on the scan geometry (the arc span and the arc orientation). This paper is designed to provide guidance on optimal scan geometries for two key applications in the wind energy industry: wind turbine power performance analysis and annual energy production prediction. We present a quantitative analysis of the retrieved wind speed uncertainty derived using a theoretical model with the assumption of isotropic and frozen turbulence, and observations from three sites that are onshore with flat terrain, onshore with complex terrain and offshore, respectively. The results from both the theoretical model and observations show that the uncertainty is scaled with the turbulence intensity such that the relative standard error on the 10 min mean wind speed is about 30 % of the turbulence intensity. The uncertainty in both retrieved wind speeds and derived wind energy production estimates can be reduced by aligning lidar beams with the dominant wind direction, increasing the arc span and lowering the number of beams per arc scan. Large arc spans should be used at sites with high turbulence intensity and/or large wind direction variation.

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Wind resource assessment and wind farm modeling in Mossobo-Harena area, North Ethiopia

Wind Engineering ◽

10.1177/0309524x20925409 ◽

2020 ◽

pp. 0309524X2092540

Author(s):

Addisu Dagne Zegeye

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Wind Turbine ◽

Power Density ◽

Energy Production ◽

Wind Farm ◽

Ground Level ◽

Energy Potential ◽

Mean Power ◽

Wind Energy Potential

Although Ethiopia does not have significant fossil fuel resource, it is endowed with a huge amount of renewable energy resources such as hydro, wind, geothermal, and solar power. However, only a small portion of these resources has been utilized so far and less than 30% of the nation’s population has access to electricity. The wind energy potential of the country is estimated to be up to 10 GW. Yet less than 5% of this potential is developed so far. One of the reasons for this low utilization of wind energy in Ethiopia is the absence of a reliable and accurate wind atlas and resource maps. Development of reliable and accurate wind atlas and resource maps helps to identify candidate sites for wind energy applications and facilitates the planning and implementation of wind energy projects. The main purpose of this research is to assess the wind energy potential and model wind farm in the Mossobo-Harena site of North Ethiopia. In this research, wind data collected for 2 years from Mossobo-Harena site meteorological station were analyzed using different statistical software to evaluate the wind energy potential of the area. Average wind speed and power density, distribution of the wind, prevailing direction, turbulence intensity, and wind shear profile of the site were determined. Wind Atlas Analysis and Application Program was used to generate the generalized wind climate of the area and develop resource maps. Wind farm layout and preliminary turbine micro-sitting were done by taking various factors into consideration. The IEC wind turbine class of the site was determined and an appropriate wind turbine for the study area wind climate was selected and the net annual energy production and capacity factor of the wind farm were determined. The measured data analysis conducted indicates that the mean wind speed at 10 and 40 m above the ground level is 5.12 and 6.41 m/s, respectively, at measuring site. The measuring site’s mean power density was determined to be 138.55 and 276.52 W/m2 at 10 and 40 m above the ground level, respectively. The prevailing wind direction in the site is from east to south east where about 60% of the wind was recorded. The resource grid maps developed by Wind Atlas Analysis and Application Program on a 10 km × 10 km area at 50 m above the ground level indicate that the selected study area has a mean wind speed of 5.58 m/s and a mean power density of 146 W/m2. The average turbulence intensity of the site was found to be 0.136 at 40 m which indicates that the site has a moderate turbulence level. According to the resource assessment done, the area is classified as a wind Class IIIB site. A 2-MW rated power ENERCON E-82 E2 wind turbine which is an IEC Class IIB turbine with 82 m rotor diameter and 98 m hub height was selected for estimation of annual energy production on the proposed wind farm. 88 ENERCON E-82 E2 wind turbines were properly sited in the wind farm with recommended spacing between the turbines so as to reduce the wake loss. The rated power of the wind farm is 180.4 MW and the net annual energy production and capacity factor of the proposed wind farm were determined to be 434.315 GWh and 27.48% after considering various losses in the wind farm.

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Wake and Turbulence Analysis for Wind Turbine Layouts in an Island

E3S Web of Conferences ◽

10.1051/e3sconf/20186406010 ◽

2018 ◽

Vol 64 ◽

pp. 06010

Author(s):

Bachhal Amrender Singh ◽

Vogstad Klaus ◽

Lal Kolhe Mohan ◽

Chougule Abhijit ◽

Beyer Hans George

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Wind Turbine ◽

Wind Turbines ◽

Energy Production ◽

Wind Farm ◽

Full Length Paper ◽

Turbulence Analysis ◽

Wake Model ◽

Wind Resources

There is a big wind energy potential in supplying the power in an island and most of the islands are off-grid. Due to the limited area in island(s), there is need to find appropriate layout / location for wind turbines suited to the local wind conditions. In this paper, we have considered the wind resources data of an island in Trøndelag region of the Northern Norway, situated on the coastal line. The wind resources data of this island have been analysed for wake losses and turbulence on wind turbines for determining appropriate locations of wind turbines in this island. These analyses are very important for understanding the fatigue and mechanical stress on the wind turbines. In this work, semi empirical wake model has been used for wake losses analysis with wind speed and turbine spacings. The Jensen wake model used for the wake loss analysis due to its high degree of accuracy and the Frandsen model for characterizing the turbulent loading. The variations of the losses in the wind energy production of the down-wind turbine relative to the up-wind turbine and, the down-stream turbulence have been analysed for various turbine distances. The special emphasis has been taken for the case of wind turbine spacing, leading to the turbulence conditions for satisfying the IEC 61400-1 conditions to find the wind turbine layout in this island. The energy production of down-wind turbines has been decreased from 2 to 20% due to the lower wind speeds as they are located behind up-wind turbine, resulting in decreasing the overall energy production of the wind farm. Also, the higher wake losses have contributed to the effective turbulence, which has reduced the overall energy production from the wind farm. In this case study, the required distance for wind turbines have been changed to 6 rotor diameters for increasing the energy gain. From the results, it has been estimated that the marginal change in wake losses by moving the down-stream wind turbine by one rotor diameter distance has been in the range of 0.5 to 1% only and it is insignificant. In the full-length paper, the wake effects with wind speed variations and the wind turbine locations will be reported for reducing the wake losses on the down-stream wind turbine. The Frandsen model has been used for analysing turbulence loading on the down-stream wind turbine as per IEC 61400-1 criteria. In larger wind farms, the high turbulence from the up-stream wind turbines increases the fatigues on the turbines of the wind farm. In this work, we have used the effective turbulence criteria at a certain distance between up-stream and down-stream turbines for minimizing the fatigue load level. The sensitivity analysis on wake and turbulence analysis will be reported in the full-length paper. Results from this work will be useful for finding wind farm layouts in an island for utilizing effectively the wind energy resources and electrification using wind power plants.

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Wind potential estimation and proposed energy production in Southern Punjab using Weibull probability density function and surface measured data

Energy Exploration & Exploitation ◽

10.1177/0144598720920748 ◽

2020 ◽

pp. 014459872092074 ◽

Cited By ~ 2

Author(s):

Muhammad Sumair ◽

Tauseef Aized ◽

Syed Asad Raza Gardezi ◽

Syed Ubaid Ur Rehman ◽

Syed Muhammad Sohail Rehman

Keyword(s):

Wind Speed ◽

Energy Density ◽

Wind Energy ◽

Wind Power ◽

Power Density ◽

Energy Production ◽

Economic Assessment ◽

Weibull Model ◽

Wind Power Density ◽

Wind Potential

Current work focusses on the wind potential assessment in South Punjab. Eleven locations from South Punjab have been analyzed using two-parameter Weibull model (with Energy Pattern Factor Method to estimate Weibull parameters) and five years (2014–2018) hourly wind data measured at 50 m height and collected from Pakistan Meteorological Department. Techno-economic analysis of energy production using six different turbine models was carried out with the purpose of presenting a clear picture about the importance of turbine selection at particular location. The analysis showed that Rahim Yar Khan carries the highest wind speed, highest wind power density, and wind energy density with values 4.40 ms−1, 77.2 W/m2 and 677.76 kWh/m2/year, respectively. On the other extreme, Bahawalnagar observes the least wind speed i.e. 3.60 ms−1 while Layyah observes the minimum wind power density and wind energy density as 38.96 W/m2 and 352.24 kWh/m2/year, respectively. According to National Renewable Energy Laboratory standards, wind potential ranging from 0 to 200 W/m2 is considered poor. Economic assessment was carried out to find feasibility of the location for energy harvesting. Finally, Polar diagrams drawn to show the optimum wind blowing directions shows that optimum wind direction in the region is southwest.

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A case study of climate variability effects on wind resources in South Africa

Journal of Energy in Southern Africa ◽

10.17159/2413-3051/2014/v25i3a2652 ◽

2014 ◽

Vol 25 (3) ◽

pp. 2-10 ◽

Cited By ~ 1

Author(s):

Lynette Herbst ◽

Jörg Lalk

Keyword(s):

Climate Change ◽

Wind Speed ◽

Wind Energy ◽

Wind Turbine ◽

Wind Power ◽

Energy Production ◽

Energy Sector ◽

Speed Increase ◽

Wind Resources ◽

Wind Speed Increase

The wind energy sector is one of the most prominent sectors of the renewable energy industry. However, its dependence on meteorological factors subjects it to climate change. Studies analysing the impact of climate change on wind resources usually only model changes in wind speed. Two elements that have to be calculated in addition to wind speed changes are Annual Energy Production (AEP) and Power Density (PD). This is not only because of the inherent variability between wind speed and wind power generated, but also because of the relative magnitudes of change in energy potentially generated at different areas under varied wind climates. In this study, it was assumed that two separate locations would experience a 10% wind speed increase after McInnes et al. (2010). Given the two locations’ different wind speed distributions, a wind speed increase equal in magnitude is not equivalent to similar magnitudes of change in potential energy production in these areas. This paper demonstrates this fact for each of the case studies. It is of general interest to the energy field and is of value since very little literature exists in the Southern African context on climate change- or variability-effects on the (wind) energy sector. Energy output is therefore dependent not only on wind speed, but also wind turbine characteristics. The importance of including wind power curves and wind turbine generator capacity in wind resource analysis is emphasised.

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Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization

10.5194/wes-2017-56 ◽

2018 ◽

Cited By ~ 3

Author(s):

Andrés Santiago Padrón ◽

Jared Thomas ◽

Andrew P. J. Stanley ◽

Juan J. Alonso ◽

Andrew Ning

Keyword(s):

Wind Speed ◽

Wind Direction ◽

Energy Production ◽

Wind Farm ◽

Polynomial Chaos ◽

Optimization Problems ◽

Probability Distributions ◽

Regression Result ◽

Computationally Efficient ◽

Wind Farm Optimization

Abstract. In this paper, we develop computationally-efficient techniques to calculate statistics used in wind farm optimization with the goal of enabling the use of higher-fidelity models and larger wind farm optimization problems. We apply these techniques to maximize the Annual Energy Production (AEP) of a wind farm by optimizing the position of the individual wind turbines. The AEP (a statistic itself) is the expected power produced by the wind farm over a period of one year subject to uncertainties in the wind conditions (wind direction and wind speed) that are described with empirically-determined probability distributions. To compute the AEP of the wind farm, we use a wake model to simulate the power at different input conditions composed of wind direction and wind speed pairs. We use polynomial chaos (PC), an uncertainty quantification method, to construct a polynomial approximation of the power over the entire stochastic space and to efficiently (using as few simulations as possible) compute the expected power (AEP). We explore both regression and quadrature approaches to compute the PC coefficients. PC based on regression is significantly more efficient than the rectangle rule (the method most commonly used to compute the expected power). With PC based on regression, we have reduced by as much as an order of magnitude the number of simulations required to accurately compute the AEP, thus enabling the use of more expensive, higher-fidelity models or larger wind farm optimizations. We perform a large suite of gradient-based optimizations with different initial turbine locations and with different numbers of samples to compute the AEP. The optimizations with PC based on regression result in optimized layouts that produce the same AEP as the optimized layouts found with the rectangle rule but using only one-third of the samples. Furthermore, for the same number of samples, the AEP of the optimal layouts found with PC is 1 % higher than the AEP of the layouts found with the rectangle rule.

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Design and Experiment of Assistive Mechanism for Adjustment of Transrectal Ultrasound Probe

Applied Bionics and Biomechanics ◽

10.1155/2020/8846073 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Jin-Gang Jiang ◽

Hui Zuo ◽

Yong-De Zhang ◽

Zhi-Yuan Huang ◽

Xiao-Wei Guo ◽

...

Keyword(s):

Elevation Angle ◽

Transrectal Ultrasound ◽

Maximum Error ◽

Relative Standard Error ◽

Ultrasound Probe ◽

Ultrasonic Probe ◽

Center Point ◽

Relative Standard ◽

Point Motion ◽

Posture Adjustment

Transrectal ultrasound prostate biopsy is the most commonly used method for the diagnosis of prostate cancer. During the operation, the doctor needs to manually adjust the ultrasound probe for repeated adjustments, which is difficult to ensure the efficiency, accuracy, and safety of the operation. This paper presents a passive posture adjusting mechanism of transrectal ultrasound probe. The overall mechanism has 7 degrees of freedom, consisting of a position adjustment module, a posture adjustment module, and an ultrasonic probe rotation and feed module. In order to achieve the centering function, the posture adjustment module is designed based on the double parallelogram. Centering performance is verified based on SimMechanics, and remote center point error of physical prototypes is evaluated. The maximum error of the azimuth remote center point motion and the maximum error of the remote center point motion of the ultrasonic probe are 4 mm and 3.4 mm, respectively, which are less than the anus that can withstand 6 mm. Meanwhile, the analysis of measurement error shows that the random error correlation is weak in different directions, the systematic error confidence intervals of azimuth and elevation angle are less than 2.5 mm, and the maximum relative fixed point error and the maximum relative standard error are 14.73% and 14.98%, respectively. The simulation and testing results have shown the effectiveness and reliability of the propose mechanism.

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Can LiDARs Replace Meteorological Masts in Wind Energy?

Energies ◽

10.3390/en12193680 ◽

2019 ◽

Vol 12 (19) ◽

pp. 3680 ◽

Cited By ~ 3

Author(s):

Jay Prakash Goit ◽

Susumu Shimada ◽

Tetsuya Kogaki

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Turbulence Intensity ◽

Regression Line ◽

Sonic Anemometer ◽

Bending Moments ◽

Wind Speeds ◽

Acceptable Accuracy ◽

Sonic Anemometers ◽

Measured Wind Speed

This paper discusses whether profiling LiDARs can replace meteorological tower-based wind speed measurement for wind energy applications without severely compromising accuracy. To this end, the accuracy of LiDAR is evaluated in a moderately complex terrain by comparing long-term wind data measured by a profiling LiDAR against those obtained from tower-mounted cup and sonic anemometers. The LiDAR-measured wind speeds show good agreement with those measured using the sonic anemometer, with the slope of regression line being 1.0 and R 2 > 0.99 . Furthermore, the turbulence intensity obtained from the LiDAR has better agreement with that from the sonic anemometer compared to the cup anemometer which showed the lowest turbulence intensities among the three devices. A comparison of the turbulence intensity obtained from the 90th percentile of the standard deviation distribution shows that the LiDAR-measured turbulence intensities are mostly larger (by 2% or less) than those measured by the sonic anemometer. The gust factors obtained from both devices roughly converged to 1.9, showing that LiDAR is able to measure peak wind speed with acceptable accuracy. The accuracy of the wind speed and power distributions measured using the profiling LiDAR are then evaluated by comparing them against the corresponding distributions obtained from the sonic anemometer. Furthermore, the annual capacity factor—for the NREL 5-MW wind turbine—from the LiDAR-measured wind speed is 2% higher than that obtained from the sonic anemometer-measured wind speed. Numerical simulations are performed using OpenFAST in order to compute fatigue loads for the wind speed and turbulence distributions for the LiDAR and the sonic anemometer measurements. It is found that the 20 years lifetime Damage Equivalent Loads (DELs) computed for the LiDAR wind speed were higher than those for the sonic anemometer wind speeds, by 2%–6% for the blade root bending moments and by 11%–13% for the tower base bending moments. This study shows that even with some shortcomings, profiling LiDARs can measure wind speeds and turbulence intensities with acceptable accuracy. Therefore, they can be used to analyze wind resource and wind power potential of prospective sites, and to evaluate whether those sites are suitable for wind energy development.

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Statistical and Spectral Analysis of Wind Characteristics Relevant to Wind Energy Assessment Using Tower Measurements in Complex Terrain

Journal of Wind Energy ◽

10.1155/2013/739162 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 16

Author(s):

Radian Belu ◽

Darko Koracin

Keyword(s):

Spectral Analysis ◽

Wind Speed ◽

Wind Energy ◽

Turbulence Intensity ◽

Complex Terrain ◽

Operation Management ◽

Small Range ◽

Grid Integration ◽

Energy Assessment ◽

Average Wind

The main objective of the study was to investigate spatial and temporal characteristics of the wind speed and direction in complex terrain that are relevant to wind energy assessment and development, as well as to wind energy system operation, management, and grid integration. Wind data from five tall meteorological towers located in Western Nevada, USA, operated from August 2003 to March 2008, used in the analysis. The multiannual average wind speeds did not show significant increased trend with increasing elevation, while the turbulence intensity slowly decreased with an increase were the average wind speed. The wind speed and direction were modeled using the Weibull and the von Mises distribution functions. The correlations show a strong coherence between the wind speed and direction with slowly decreasing amplitude of the multiday periodicity with increasing lag periods. The spectral analysis shows significant annual periodicity with similar characteristics at all locations. The relatively high correlations between the towers and small range of the computed turbulence intensity indicate that wind variability is dominated by the regional synoptic processes. Knowledge and information about daily, seasonal, and annual wind periodicities are very important for wind energy resource assessment, wind power plant operation, management, and grid integration.

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