Modeling of Wind Speeds Inside a Wind Farm With Application to Wind Farm Aggregate Modeling Considering LVRT Characteristic

2020 ◽  
Vol 35 (1) ◽  
pp. 508-519 ◽  
Author(s):  
Yuqing Jin ◽  
Daming Wu ◽  
Ping Ju ◽  
Christian Rehtanz ◽  
Feng Wu ◽  
...  
Keyword(s):  
Wind Farm ◽  
Wind Speeds ◽  
Aggregate Modeling

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 Comparison of Weibull Estimation Methods for Diverse Winds

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ferhat Bingöl
Keyword(s):  
Wind Farm ◽  
Study Data ◽  
Distribution Functions ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Weibull Function ◽  
Wind Speeds ◽  
Low Wind Speed ◽  
Low Wind Speeds ◽  
Wind Distribution

Wind farm siting relies on in situ measurements and statistical analysis of the wind distribution. The current statistical methods include distribution functions. The one that is known to provide the best fit to the nature of the wind is the Weibull distribution function. It is relatively straightforward to parameterize wind resources with the Weibull function if the distribution fits what the function represents but the estimation process gets complicated if the distribution of the wind is diverse in terms of speed and direction. In this study, data from a 101 m meteorological mast were used to test several estimation methods. The available data display seasonal variations, with low wind speeds in different seasons and effects of a moderately complex surrounding. The results show that the maximum likelihood method is much more successful than industry standard WAsP method when the diverse winds with high percentile of low wind speed occur.

Simulation Study on Dynamic Reactive Power Compensation of Grid-Connected Wind Farms

2013 ◽  
Vol 756-759 ◽  
pp. 4171-4174 ◽  
Author(s):  
Xiao Ming Wang ◽  
Xing Xing Mu
Keyword(s):  
Voltage Stability ◽  
Wind Farm ◽  
Reactive Power ◽  
Wind Farms ◽  
Reactive Power Compensation ◽  
Wind Speeds ◽  
Wind Generators ◽  
Reactive Compensation ◽  
Farm Model ◽  
Set Up

With the Asynchronous wind generators as research object, this paper analyzes the problems of the voltage stability and the generation mechanism of the reactive power compensation during the wind farms connected operation. For paralleling capacitor bank has shown obvious defects, therefore this paper employs dynamic reactive power compensation to improve reactive characteristics of grid-connected wind farms. With the influences of different wind disturbances and grid faults on wind farms, wind farm model is set up and dynamic reactive power compensation system and wind speeds are built in the Matlab/Simulink software, The simulation result shows that they can provide reactive power compensation to ensure the voltage stability of the wind farms. But STATCOM needs less reactive compensation capacity to make sure the voltage and active power approaching steady state before the faults more quickly, Therefore STATCOM is more suitable for wind farms connected dynamic reactive power compensation.

scholarly journals Inter-annual variability of wind climates and wind turbine annual energy production

2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Standard Deviation ◽  
Wind Energy ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Speeds ◽  
Annual Variability ◽  
Eastern Usa

Abstract. Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind turbine hub-heights is poorly constrained and maybe overestimated by the 6 % standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the long-term wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6 %. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90 % of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Calibration and validation of the Dynamic Wake Meandering model Part I: Bayesian estimation of model parameters using SpinnerLidar-derived wake characteristics

2021 ◽  
Author(s):  
Davide Conti ◽  
Nikolay Dimitrov ◽  
Alfredo Peña ◽  
Thomas Herges
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Flow Characteristics ◽  
Uncertainty Assessment ◽  
Wake Flow ◽  
Model Parameters ◽  
Wake Field ◽  
Wind Speeds ◽  
Standard Lead ◽  
Estimation Of Model Parameters

Abstract. In this first part of a two-part work, we study the calibration of the Dynamic Wake Meandering (DWM) model using high spatial and temporal resolution SpinnerLidar measurements of the wake field collected at the Scaled Wind Farm Technology (SWiFT) facility located in Lubbock, Texas, U.S.A. We derive two-dimensional wake flow characteristics including wake deficit, wake turbulence and wake meandering from the lidar observations under different atmospheric stability conditions, inflow wind speeds and downstream distances up to five rotor diameters. We then apply Bayesian inference to obtain a probabilistic calibration of the DWM model, where the resulting joint distribution of parameters allows both for model implementation and uncertainty assessment. We validate the resulting fully-resolved wake field predictions against the lidar measurements and discuss the most critical sources of uncertainty. The results indicate that the DWM model can accurately predict the mean wind velocity and turbulence fields in the far wake region beyond four rotor diameters, as long as properly-calibrated parameters are used and wake meandering time series are accurately replicated. We demonstrate that the current DWM-model parameters in the IEC standard lead to conservative wake deficit predictions. Finally, we provide practical recommendations for reliable calibration procedures.

scholarly journals Prediction of noise from wind turbines: theoretical and experimental study

2018 ◽  
pp. 34-41 ◽  
Author(s):  
Carlos Alberto Echeverri-Londoño ◽  
Alice Elizabeth González Fernández
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Sound Pressure ◽  
Prediction Method ◽  
Wide Band ◽  
Propagation Models ◽  
Low Frequencies ◽  
High Frequencies ◽  
Wind Speeds ◽  
Sound Pressure Levels

Several noise propagation models used to calculate the noise produced by wind turbines have been reported. However, these models do not accurately predict sound pressure levels. Most of them have been developed to estimate the noise produced by industries, in which wind speeds are less than 5 m/s, and conditions favor its spread. To date, very few models can be applied to evaluate the propagation of sound from wind turbines and most of these yield inaccurate results. This study presents a comparison between noise levels that were estimated using the prediction method established in ISO 9613 Part 2 and measured levels of noise from wind turbines that are part of a wind farm currently in operation. Differences of up to 56.5 dBZ, with a median of 29.6 dBZ, were found between the estimated sound pressure levels and measured levels. The residual sound pressure levels given by standard ISO 9613 Part 2 for the wind turbines is larger for high frequencies than those for low frequencies. When the wide band equivalent continuous sound pressure level is expressed in dBA, the residual varies between −4.4 dBA and 37.7 dBA, with a median of 20.5 dBA.

Research on Reliability Assessment of Power System with Wind Farms

2012 ◽  
Vol 608-609 ◽  
pp. 588-591
Author(s):  
Wen Jiang ◽  
Ye Xia Cheng ◽  
Ye Jian Cheng
Keyword(s):  
Power System ◽  
Wind Farm ◽  
Sequential Monte Carlo ◽  
Moving Average ◽  
Reliability Assessment ◽  
Arma Model ◽  
Wind Farms ◽  
Test System ◽  
Wind Speeds ◽  
State Models

Due to randomness and fluctuation of wind speed, reliability of power system will be affected severely with increasing wind energy injected into power grid. In order to evaluate the effect on reliability of power system with wind farms, the author considers feature of time-sequential and self-correlation of wind speeds and builds an auto-regressive and moving average (ARMA) model to forecast wind speeds. Combining with state models of conventional generating units, transmission lines and transformers, a time-sequential Monte Carlo simulation reliability model is proposed to do reliability assessment of composite generation and transmission system with wind farm. IEEE-RTS test system is introduced to prove the proposed model. Analysis and comparison of results show that reliability can be improved clearly after integration of wind farm.

Assessment of wind energy potential and characteristics in Qatar for clean electricity generation

2021 ◽  
pp. 0309524X2110438
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Rose ◽  
Energy Potential ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Wind Energy Potential

The present study analyzes the wind energy potential of Qatar, by generating a wind atlas and a Wind Power Density map for the entire country based on ERA-5 data with over 41 years of measurements. Moreover, the wind speeds’ frequency and direction are analyzed using wind recurrence, Weibull, and wind rose plots. Furthermore, the best location to install a wind farm is selected. The results indicate that, at 100 m height, the mean wind speed fluctuates between 5.6054 and 6.5257 m/s. Similarly, the Wind Power Density results reflect values between 149.46 and 335.06 W/m2. Furthermore, a wind farm located in the selected location can generate about 59.7437, 90.4414, and 113.5075 GWh/y electricity by employing Gamesa G97/2000, GE Energy 2.75-120, and Senvion 3.4M140 wind turbines, respectively. Also, these wind farms can save approximately 22,110.80, 17,617.63, and 11,637.84 tons of CO2 emissions annually.

ALL-DC Offshore Wind Farm With Series-Connected Wind Turbines to Overcome Unequal Wind Speeds

2019 ◽  
Vol 34 (2) ◽  
pp. 1370-1381 ◽  
Author(s):  
Fei Rong ◽  
Gongping Wu ◽  
Xing Li ◽  
Shoudao Huang ◽  
Bin Zhou
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wind Speeds

Modeling of Flow Acceleration Around Wind Farms

2007 ◽  
Author(s):  
Patrick Moriarty ◽  
Tetsuya Kogaki
Keyword(s):  
Wind Farm ◽  
Computational Models ◽  
Three Dimensional ◽  
Wind Farms ◽  
Navier Stokes ◽  
Local Wind ◽  
Flow Acceleration ◽  
Wind Speeds ◽  
Optimum Spacing ◽  
Future Work

Recent measurements from operating wind farms demonstrate that the layout of the farm and interactions between turbine wakes strongly affects the overall efficiency of the wind farm. In some wind farms arranged in rectangular layouts, winds coming from the direction of the rectangular corner create a potential acceleration around the wind farm. This acceleration inherently leads to stronger local wind speeds at wind turbines downstream of the corner turbine, thereby increasing the power output of the downstream turbines. In this study, computational models are developed to predict this complex behavior seen in wind farms. The model used to examine these effects is a fully three-dimensional unsteady incompressible Navier-Stokes code, with the turbulence model turned off. Preliminary results show an optimum spacing configuration is possible. However, the results have yet to be verified at higher Reynolds number, which will be the effort of future work. Ultimately, these tools may lead to more optimal wind farm layouts.

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