scholarly journals Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements

2020 ◽  
Vol 12 (6) ◽  
pp. 2467 ◽  
Author(s):  
Fei Zhao ◽  
Yihan Gao ◽  
Tengyuan Wang ◽  
Jinsha Yuan ◽  
Xiaoxia Gao
Keyword(s):  
Wind Speed ◽  
Wind Shear ◽  
Wind Farm ◽  
Wind Farms ◽  
Field Measurements ◽  
Development Trend ◽  
Asymmetric Distribution ◽  
Lidar Measurements ◽  
Wind Characteristics ◽  
The Development Trend

To study the wake development characteristics of wind farms in complex terrains, two different types of Light Detection and Ranging (LiDAR) were used to conduct the field measurements in a mountain wind farm in Hebei Province, China. Under two different incoming wake conditions, the influence of wind shear, terrain and incoming wind characteristics on the development trend of wake was analyzed. The results showed that the existence of wind shear effect causes asymmetric distribution of wind speed in the wake region. The relief of the terrain behind the turbine indicated a subsidence of the wake centerline, which had a linear relationship with the topography altitudes. The wake recovery rates were calculated, which comprehensively validated the conclusion that the wake recovery rate is determined by both the incoming wind turbulence intensity in the wake and the magnitude of the wind speed.

scholarly journals Field Measurements of Wind Characteristics Using LiDAR on a Wind Farm with Downwind Turbines Installed in a Complex Terrain Region

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5135
Author(s):  
Tetsuya Kogaki ◽  
Kenichi Sakurai ◽  
Susumu Shimada ◽  
Hirokazu Kawabata ◽  
Yusuke Otake ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Field Experiments ◽  
Wind Farms ◽  
Field Measurements ◽  
Horizontal Wind ◽  
Flow Distortion ◽  
Horizontal Wind Speed ◽  
Vertical Profiling ◽  
Wind Characteristics

Downwind turbines have favorable characteristics such as effective energy capture in up-flow wind conditions over complex terrains. They also have reduced risk of severe accidents in the event of disruptions to electrical networks during strong storms due to the free-yaw effect of downwind turbines. These favorable characteristics have been confirmed by wind-towing tank experiments and computational fluid dynamics (CFD) simulations. However, these advantages have not been fully demonstrated in field experiments on actual wind farms. In this study—although the final objective was to demonstrate the potential advantages of downwind turbines through field experiments—field measurements were performed using a vertical-profiling light detection and ranging (LiDAR) system on a wind farm with downwind turbines installed in complex terrains. To deduce the horizontal wind speed, vertical-profiling LiDARs assume that the flow of air is uniform in space and time. However, in complex terrains and/or in wind farms where terrain and/or wind turbines cause flow distortion or disturbances in time and space, this assumption is not valid, resulting in erroneous wind speed estimates. The magnitude of this error was evaluated by comparing LiDAR measurements with those obtained using a cup anemometer mounted on a meteorological mast and detailed analysis of line-of-sight wind speeds. A factor that expresses the nonuniformity of wind speed in the horizontal measurement plane of vertical-profiling LiDAR is proposed to estimate the errors in wind speed. The possibility of measuring and evaluating various wind characteristics such as flow inclination angles, turbulence intensities, wind shear and wind veer, which are important for wind turbine design and for wind farm operation is demonstrated. However, additional evidence of actual field measurements on wind farms in areas with complex terrains is required in order to obtain more universal and objective evaluations.

scholarly journals Wind Characteristics of Three Meteorological Stations in China

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yang Yang ◽  
Yao Gang ◽  
Wang Rong ◽  
Wang Hengyu
Keyword(s):  
Economic Development ◽  
Wind Speed ◽  
Wind Energy ◽  
Turbulence Intensity ◽  
Wind Shear ◽  
Wind Farm ◽  
Wind Generation ◽  
Extreme Wind ◽  
Wind Characteristics ◽  
Extreme Wind Speed

With rapid economic development of China, demand for energy is growing rapidly. Many experts have begun to pay attention on exploiting wind energy. Wind characteristics of three meteorological stations in China were analyzed to find out if or not it is possible to build a wind farm in this paper. First of all, studies about the wind characteristics and potential wind energy were summarized. Then ways of collecting and manipulating wind data were introduced. Wind-generation potential was assessed by the method of Weibull distribution. Wind shear exponent, extreme wind speed in 50 years, and turbulence intensity were calculated. The wind characteristics were summarized and assessment of wind-generation potential was given. At last, the wind was simulated with autoregressive method by Matlab software.

scholarly journals Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2319
Author(s):  
Hyun-Goo Kim ◽  
Jin-Young Kim
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Free Stream ◽  
Turbine Blades ◽  
Wind Farms ◽  
Lidar Measurement ◽  
Wind Turbine Blades ◽  
Wake Effect

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

scholarly journals Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4291
Author(s):  
Paxis Marques João Roque ◽  
Shyama Pada Chowdhury ◽  
Zhongjie Huan
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Operating Conditions ◽  
Wake Effect ◽  
Wind Generators ◽  
Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

scholarly journals Analysis of Wind Speed Shear and Turbulence LiDAR Measurements to Support Offshore Wind in the Northeast United States

2018 ◽  
Author(s):  
Anthony Viselli ◽  
Nathan Faessler ◽  
Matthew Filippelli
Keyword(s):  
United States ◽  
Wind Speed ◽  
Wind Farm ◽  
Mesoscale Model ◽  
American Petroleum Institute ◽  
Offshore Wind ◽  
Wind Speed Profile ◽  
Northeast United States ◽  
Lidar Measurements ◽  
Wind Shears

This paper presents wind speed measurements collected at 40m to 200m above sea-level to support the New England Aqua Ventus I 12 MW Floating Offshore Wind Farm to be located 17km offshore the Northeast United States. The high-altitude wind speed data are unique and represent some of the first measurements made offshore in this part of the country which is actively being developed for offshore wind. Multiple LiDAR measurements were made using a DeepCLiDAR floating buoy and LiDARs located on land on a nearby island. The LiDARs compared favorably thereby confirming the LiDAR buoy measurements. Wind speed shear profiles are presented. The measurements are compared against industry standard mesoscale model outputs and offshore design codes including the American Bureau of Shipping, American Petroleum Institute, and DNV-GL guides. Significant variation in the vertical wind speed profile occurs throughout the year. This variation is not currently addressed in offshore wind design standards which typically recommend the use of only a few values for wind shear in operational and extreme conditions. The mean wind shears recorded were also higher than industry recommended values. Additionally, turbulence measurements made from the LiDAR, although not widely accepted in the scientific community, are presented and compared against industry guidelines.

Effect of Air Density on Output Power of Wind Turbine

2013 ◽  
Vol 336-338 ◽  
pp. 1114-1117 ◽  
Author(s):  
Ying Zhi Liu ◽  
Wen Xia Liu
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Output Power ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Air Density ◽  
Wind Farm Design ◽  
Farm Design

This paper elaborates the effect of wind speed on the output power of the wind farms at different locations. It also describes the correction of the power curve and shows the comparison chart of the standard power curve and the power curve after correction. In China's inland areas, wind farms altitude are generally higher, the air density is much different from the standard air density. The effect of air density on wind power output must be considered during the wind farm design.

The atmospheric turbulence characteristics in a diurnal cycle

2020 ◽  
Vol 34 (14n16) ◽  
pp. 2040109
Author(s):  
Yi-Lei Song ◽  
Lin-Lin Tian ◽  
Ning Zhao
Keyword(s):  
Wind Speed ◽  
Diurnal Cycle ◽  
Wind Shear ◽  
Atmospheric Stability ◽  
Atmospheric Flow ◽  
Eddy Simulation ◽  
Mean Wind Speed ◽  
Engineering Models ◽  
Wind Characteristics ◽  
Stability Classification

During a whole-day period, profiles of mean wind speed, wind shear and turbulence level shows great variability due to continuously varying atmospheric stability. Clearly understanding the spatial and temporal behaviors of the atmospheric wind flow is of great importance for science purposes. Large-eddy simulation (LES) technique is employed here to reproduce the evolution of atmospheric flow during a diurnal cycle. With the obtained LES results, wind characteristics in terms of wind speed, wind shear, turbulence intensity and turbulent kinetic energy can be examined referring to the stability classification. Besides, wind profiles obtained using currently available engineering models are also included for comparison. Disparities between the model predictions and the LES results illustrate that the standard engineering models cannot well capture the wind characteristics driven by the varying atmospheric stability solely, and a further improvement in models is highly needed.

scholarly journals Method for Determining the Maximum Allowable Capacity of Wind Farm Based on Box Set Robust Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Lihui Guo ◽  
Hao Bai
Keyword(s):  
Wind Speed ◽  
Robust Optimization ◽  
Wind Power ◽  
Optimization Model ◽  
Wind Farm ◽  
Optimization Theory ◽  
Wind Farms ◽  
Bilevel Optimization ◽  
Grid Connection ◽  
Node Voltage

With the increasing penetration of wind power, the randomness and volatility of wind power output would have a greater impact on safety and steady operation of power system. In allusion to the uncertainty of wind speed and load demand, this paper applied box set robust optimization theory in determining the maximum allowable installed capacity of wind farm, while constraints of node voltage and line capacity are considered. Optimized duality theory is used to simplify the model and convert uncertainty quantities in constraints into certainty quantities. Under the condition of multi wind farms, a bilevel optimization model to calculate penetration capacity is proposed. The result of IEEE 30-bus system shows that the robust optimization model proposed in the paper is correct and effective and indicates that the fluctuation range of wind speed and load and the importance degree of grid connection point of wind farm and load point have impact on the allowable capacity of wind farm.

Ensemble-Based Data Assimilation for Wind Forecasting – Application to Wind Farm

2020 ◽  
Author(s):  
Yang-Ming Fan
Keyword(s):  
Wind Speed ◽  
Natural Gas ◽  
Data Assimilation ◽  
Wind Energy ◽  
Wind Farm ◽  
Initial Conditions ◽  
Wind Farms ◽  
Weather Conditions ◽  
Energy Output ◽  
Combining Data

<p>The purpose of this study is to develop an ensemble-based data assimilation method to accurately predict wind speed in wind farm and provide it for the use of wind energy intelligent forecasting platform. As Taiwan government aimed to increase the share of renewable energy generation to 20% by 2025, among them, the uncertain wind energy output will cause electricity company has to reserve a considerable reserve capacity when dispatching power, and it is usually high cost natural gas power generation. In view of this, we will develop wind energy intelligent forecasting platform with an error of 10% within 72 hours and expect to save hundred millions of dollars of unnecessary natural gas generators investment. Once the wind energy can be predicted more accurately, the electricity company can fully utilize the robustness and economy of smart grid supply. Therefore, the mastery of the change of wind speed is one of the key factors that can reduce the minimum error of wind energy intelligent forecasting.</p><p>There are many uncertainties in the numerical meteorological models, including errors in the initial conditions or defects in the model, which may affect the accuracy of the prediction. Since the deterministic prediction cannot fully grasp the uncertainty in the prediction process, so it is difficult to obtain all possible wind field changes. The development of ensemble-based data assimilation prediction is to make up for the weakness of deterministic prediction. With the prediction of 20 wind fields as ensemble members, it is expected to include the uncertainty of prediction, quantify the uncertainty, and integrate the wind speed observations of wind farms as well to provide the optimal prediction of wind speed for the next 72 hours. The results show that the prediction error of wind speed within 72 hours is 6% under different weather conditions (excluding typhoons), which proves that the accuracy of wind speed prediction by combining data assimilation technology and ensemble approach is better.</p>

scholarly journals Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model

2012 ◽  
Vol 140 (9) ◽  
pp. 3017-3038 ◽  
Author(s):  
Anna C. Fitch ◽  
Joseph B. Olson ◽  
Julie K. Lundquist ◽  
Jimy Dudhia ◽  
Alok K. Gupta ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Kinetic Energy ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Farms ◽  
Mean Flow ◽  
Thrust Coefficient ◽  
Near Surface ◽  
Neutral Boundary Layer

Abstract A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the Weather Research and Forecasting model (WRF). The effects of wind turbines are represented by imposing a momentum sink on the mean flow; transferring kinetic energy into electricity and turbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy. Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with a long wake of 60-km e-folding distance. Within the farm the wind speed deficit reached a maximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.

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