Multi-Point Locational Wind Speed Estimation from Meso-Scale Wind Speeds for Wind Farm Applications

2019 ◽  
Author(s):  
Matthew Groch ◽  
Hendrik J. Vermeulen
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Speed Estimation ◽  
Wind Speeds ◽  
Meso Scale

scholarly journals Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

2021 ◽  
Vol 6 (6) ◽  
pp. 1427-1453
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

scholarly journals Results from a Wake Steering Experiment at a Commercial Wind Plant: Investigating the Wind Speed Dependence of Wake Steering Performance

2021 ◽  
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the three-month experiment period, we estimate that wake steering reduced wake losses by 5.7 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.8 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the predicted achieved yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

Offshore wind farm far field - Results of the project WIPAFF

2020 ◽  
Author(s):  
Andreas Platis ◽  
Jens Bange ◽  
Konrad Bärfuss ◽  
Beatriz Canadillas ◽  
Marie Hundhausen ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Order Approximation ◽  
Wind Farms ◽  
In Situ Measurements ◽  
Offshore Wind ◽  
Stable Conditions ◽  
Wind Park ◽  
Meso Scale

<p>Wind farm far wakes are of particular interest for offshore installations, as turbulence intensity, which is the main driver for wake dissipation, is much lower over the ocean than over land. Therefore, wakes behind offshore wind turbines and wind parks are expected to be much longer than behind onshore parks. </p><p>In situ measurements of the far wakes were missing before the initiation of the research project WIPAFF (WInd PArk Far Fields) in 2015. The main results of which are reported here. WIPAFF has been funded by the German Federal Ministry for Economic Affairs and Energy and ran from November 2015 to April 2019.  The main goal of WIPAFF was to perform a large number of in situ measurements from aircraft operations at hub height behind wind parks in the German Bight (North Sea), to evaluate further SAR images and to update and validate existing meso-scale and industrial models on the basis of the observations to enable a holistic coverage of the downstream wakes.<br> <br>A  unique  dataset  from  airborne in situ data,  remote sensing  by  laser  scanner  and  SAR  gained  during  the WIPAFF  project  proves  that  wakes  up to  several  tens of kilometers exist downstream of offshore wind farms during stable conditions, while under neutral/unstable conditions, the wake length amounts to 15 km or less. Turbulence occurs at the lateral boundaries of the wakes, due to shear between the reduced wind speed inside the wake and the undisturbed flow. Data also indicates that a denser wind park layout increases the wake length additionally due to a higher initial wind speed deficit. The recovery of the decelerated flow in the wake can be modeled as a first order approximation by an exponential function. The project could also reveal that wind-farm parameterizations in the numerical meso-scale WRF model show a feasible agreement with the observations. </p>

scholarly journals Local-scale wind speed features captured by seasonal forecasts

2021 ◽  
Author(s):  
Jaume Ramon ◽  
Llorenç Lledó ◽  
Pierre-Antoine Bretonnière ◽  
Margarida Samsó ◽  
Francisco J. Doblas-Reyes
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Surface Wind ◽  
Climate Modelling ◽  
Seasonal Forecasts ◽  
Economic Sectors ◽  
Skill Levels ◽  
Near Surface ◽  
Wind Speeds ◽  
Surface Climate

<p>Thanks to the recent advances in climate modelling, seasonal predictions are becoming more skilful at anticipating the future state of near-surface climate variables over extratropics. Nevertheless, such predictions are delivered on too coarse grids with horizontal resolutions of hundreds of kilometres so that local events happening at much finer scales cannot be reproduced. This is particularly noted for variables with high spatial variability like wind or precipitation: wind speeds can vary substantially over a few kilometres, from the top of a mountain to a valley floor. The differences in magnitude might be relevant for the deriving sectoral indicators, for example, within the wind industry and at a wind farm level.</p><p>This work presents and applies a downscaling methodology to generate fine-scale seasonal forecasts ---up to station scale--- for near-surface wind speeds in Europe. The hybrid forecasts are based on a statistical downscaling with a perfect prognosis approach, fitting a multi-linear regression with the four main Euro-Atlantic Teleconnections (EATC) indices as predictors. Seasonal predictions of EATC indices, which are predictable with relatively good skill levels, are later inserted into the multi-linear model. This results in skilful seasonal predictions of surface wind speeds. Indeed, the comparison of the hybrid forecasts against the dynamical forecasts of wind speed shows that the skill of such forecasts is not only maintained but also increased over most of Europe. The hybrid forecasts are generated at 17 locations where tall tower wind speed data are available and at a pan-European scale using the 100-metre wind speeds from the ERA5 reanalysis. Improving the accuracy of seasonal predictions is an essential step to inform weather-and-climate-vulnerable socio-economic sectors of seasonal anomalies a few months ahead.</p>

scholarly journals Assessing Variability of Wind Speed: Comparison and Validation of 27 Methodologies

2018 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
M. Jason Fields ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Resource Assessment ◽  
Assessment Process ◽  
Wind Speeds ◽  
Statistical Confidence ◽  
Monthly Total ◽  
Mean Wind Speed

Abstract. Because wind resources vary from year to year, the inter-monthly and inter-annual variability (IAV) of wind speed is a key component of the overall uncertainty in the wind resource assessment process thereby causing challenges to wind-farm operators and owners. We present a critical assessment of several common approaches for calculating variability by applying each of the methods to the same 37-year monthly wind-speed and energy-production time series to highlight the differences between these methods. We then assess the accuracy of the variability calculations by correlating the wind-speed variability estimates to the variabilities of actual wind-farm energy production. We recommend the Robust Coefficient of Variation (RCoV) for systematically estimating variability, and we underscore its advantages as well as the importance of using a statistically robust and resistant method. Using normalized spread metrics, including RCoV, high variability of monthly mean wind speeds at a location effectively denotes strong fluctuations of monthly total energy generations, and vice versa. Meanwhile, the wind-speed IAVs computed with annual-mean data fail to adequately represent energy-production IAVs of wind farms. Finally, we find that estimates of energy-generation variability require 10 ± 3 years of monthly mean wind-speed records to achieve 90 % statistical confidence. This paper also provides guidance on the spatial distribution of wind-speed RCoV.

scholarly journals Survei Penempatan Pembangkit Listrik Tenaga Bayu Di Tanah Laut Berdasarkan Citra Radar Banjarmasin

2019 ◽  
Vol 4 ◽  
pp. 129
Author(s):  
Hanif Kurniadi ◽  
Arifah Dwi Yuliani ◽  
Ismah Atikah Khairunnisa ◽  
Syadza Siskayani Putri ◽  
Eko Wardoyo ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Renewable Energy Sources ◽  
Turbine Blades ◽  
Electricity Consumption ◽  
Meteorological Station ◽  
Maximum Height ◽  
Wind Speeds ◽  
Average Wind ◽  
Radar Imagery

<p><strong>Abstract: </strong>Indonesia's electricity consumption has increased every year. One way to overcome this problem is by utilizing renewable energy sources such as wind. Utilization of this energy uses wind turbines installed at locations that have met the requirements. Therefore, information on wind conditions in several layers is required by using radar products such as CAPPI, PPI, and HWIND which are processed using Rainbow 5 software and then interpreted in a daily wind speed graph. Data obtained from radar imagery of Syamsudin Noor Meteorological Station-Banjarmasin. And to determine the boundary conditions of the wind layer is determined according to the length of the turbine blades to calculate the minimum wind speed needed to drive the turbine blades. The results of this study show that wind conditions in layers of 100 to 600 meters tend to be the same, making it difficult to determine the maximum height of the wind layer and from 7 days of the observation sample, it is found that some average wind speeds per day are 4.076923 m / s, 4.777778 m / s, 4.393939 m / s, 0.75 m / s, 0.72973 m / s, 3.678571 m / s, and 1.4375 m / s, which are known to have not met the minimum wind speed requirements for wind farm (PLTB) to produce optimal energy.</p><p><strong>Abstrak: </strong>Konsumsi listrik Indonesia mengalami peningkatan setiap tahunnya. Salah satu untuk mengatasi masalah tersebut dengan memanfaatkan sumber energi terbarukan seperti angin. Pemanfaatan energi ini menggunakan turbin angin yang dipasang pada lokasi yang telah memenuhi syarat. Karena itu, diperlukan informasi kondisi angin dibeberapa lapisan dengan menggunakan produk radar seperti CAPPI, PPI, dan HWIND yang diolah menggunakan perangkat lunak Rainbow 5 lalu diintrepretasikan dalam grafik kecepatan angin harian. Data diperoleh dari citra radar Stasiun Meteorologi Kelas II Syamsudin Noor-Banjarmasin. Dan untuk menentukan kondisi batas lapisan angin ditentukan sesuai panjang dari baling-baling turbin untuk memperhitungkan kecepatan angin minimal yang diperlukan untuk menggerakkan baling-baling turbin. Hasil penelitian ini memperlihatkan kondisi angin di lapisan 100 hingga 600 meter cenderung sama, sehingga sulit untuk menentukan ketinggian lapisan angin maksimum dan dari 7 hari sebagai sampel pengamatan didapatkan beberapa kecepatan angin rata-rata perhari antara lain 4.076923 m/s,  4.777778 m/s,  4.393939 m/s, 0,75 m/s, 0.72973 m/s, 3.678571 m/s, dan 1.4375 m/s yang diketahui belum memenuhi persyaratan kecepatan angin minimum yang diperlukan Pembangkit Listrik Tenaga Bayu (PLTB) untuk menghasilkan energi yang optimal.</p>

scholarly journals Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over France

2020 ◽  
Vol 17 ◽  
pp. 63-77 ◽  
Author(s):  
Bénédicte Jourdier
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Regional Scale ◽  
Power Production ◽  
Local Scale ◽  
Electric System ◽  
Wind Speeds ◽  
Diurnal Cycles ◽  
Nwp Model

Abstract. As variable renewable energies are developing, their impacts on the electric system are growing. To anticipate these impacts, prospective studies may use wind power production simulations in the form of 1 h or 30 min time series that are often based on reanalysis wind-speed data. The purpose of this study is to assess how several wind-speed datasets are performing when used to simulate wind-power production at the local scale, when no observation is available to use bias correction methods. The study evaluates two global reanalysis (MERRA-2 from NASA and ERA5 from ECMWF), two high-resolution models (COSMO-REA6 reanalysis from DWD, AROME NWP model from Météo-France) and the New European Wind Atlas mesoscale data. The study is conducted over continental France. In a first part, wind-speed measurements (between 55 and 100 m above ground) at eight locations are directly compared to modelled wind speeds. In a second part, 30 min wind-power productions are simulated for every wind farm in France and compared to two open datasets of observed production published by the distribution and transmission system operators, either at the local scale in terms of annual bias, or aggregated at the regional scale, in terms of bias, correlations and diurnal cycles. ERA5 is very skilled, despite its low resolution compared to the regional models, but it underestimates wind speeds, especially in mountainous areas. AROME and COSMO-REA6 have better skills in complex areas and have generally low biases. MERRA-2 and NEWA have large biases and overestimate wind speeds especially at night. Several problems affecting diurnal cycles are detected in ERA5 and COSMO-REA6.

scholarly journals Time Series Analysis and Forecasting for Wind Speeds Using Support Vector Regression Coupled with Artificial Intelligent Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Ping Jiang ◽  
Shanshan Qin ◽  
Jie Wu ◽  
Beibei Sun
Keyword(s):  
Wind Speed ◽  
Support Vector Regression ◽  
Wind Turbines ◽  
Large Scale ◽  
Wind Farm ◽  
Hybrid Models ◽  
Parameter Determination ◽  
Support Vector ◽  
Short Term ◽  
Wind Speeds

Wind speed/power has received increasing attention around the earth due to its renewable nature as well as environmental friendliness. With the global installed wind power capacity rapidly increasing, wind industry is growing into a large-scale business. Reliable short-term wind speed forecasts play a practical and crucial role in wind energy conversion systems, such as the dynamic control of wind turbines and power system scheduling. In this paper, an intelligent hybrid model for short-term wind speed prediction is examined; the model is based on cross correlation (CC) analysis and a support vector regression (SVR) model that is coupled with brainstorm optimization (BSO) and cuckoo search (CS) algorithms, which are successfully utilized for parameter determination. The proposed hybrid models were used to forecast short-term wind speeds collected from four wind turbines located on a wind farm in China. The forecasting results demonstrate that the intelligent hybrid models outperform single models for short-term wind speed forecasting, which mainly results from the superiority of BSO and CS for parameter optimization.

Improving short-term wind power forecasts during ramp events by including nacelle wind speed observations

2021 ◽  
Author(s):  
Moritz Lochmann ◽  
Heike Kalesse-Los ◽  
Michael Schäfer ◽  
Ingrid Heinrich ◽  
Ronny Leinweber
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Mean Deviation ◽  
Short Term ◽  
Wind Speeds ◽  
Sonic Anemometers ◽  
Ramp Events

&lt;p&gt;Wind energy is and will be one of the key technologies for a transition to green electricity. However, the smooth integration of the generated wind energy into the electrical grid depends on reliable power forecasts. Rapid changes in power generation, so-called ramps, are not always reflected properly in NWP data and pose a challenge for power predictions and, therefore, grid operation. While contributions to the topic of ramp forecasting increased in the recent years, this work approaches the mitigation of deviations from the forecast more directly.&lt;/p&gt; &lt;p&gt;The power forecast tool used here is based on an artificial neural network, trained and evaluated on multiple years of data. It is applied in comparison to power generation data for a 44&amp;#160;MW wind farm in Brandenburg. For short-term wind power forecasts, NWP wind speeds in this power forecast tool are replaced with recent Doppler Lidar wind profiles and nacelle wind speed observations from ultra-sonic anemometers, aiming to provide an easy-to-implement way to reduce negative impacts of ramps. Compared to NWP input data, this persistence approach with observational data aims to improve the forecast quality especially during the time of wind ramps.&lt;/p&gt; &lt;p&gt;Different ramp definitions and forecast horizons are explored. In general, the number of ramps detected increases dramatically when using wind speed observations instead of the (too smooth) NWP model data. In addition, the mean deviation between power forecast and actual power generation around ramp events decreases, indicating a reduced need for balancing efforts.&lt;/p&gt;

Configuration of Statistical Postprocessing Techniques for Improved Low-Level Wind Speed Forecasts in West Texas

2020 ◽  
Vol 35 (1) ◽  
pp. 129-147
Author(s):  
Meghan J. Mitchell ◽  
Brian Ancell ◽  
Jared A. Lee ◽  
Nicholas H. Smith
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wrf Model ◽  
Turbine Rotor ◽  
Low Level ◽  
Wind Speeds ◽  
West Texas ◽  
Model Output Statistics ◽  
Sonic Detection

Abstract The wind energy industry needs accurate forecasts of wind speeds at turbine hub height and in the rotor layer to accurately predict power output from a wind farm. Current numerical weather prediction (NWP) models struggle to accurately predict low-level winds, partially due to systematic errors within the models due to deficiencies in physics parameterization schemes. These types of errors are addressed in this study with two statistical postprocessing techniques—model output statistics (MOS) and the analog ensemble (AnEn)—to understand the value of each technique in improving rotor-layer wind forecasts. This study is unique in that it compares the techniques using a sonic detection and ranging (SODAR) wind speed dataset that spans the entire turbine rotor layer. This study uses reforecasts from the Weather Research and Forecasting (WRF) Model and observations in west Texas over periods of up to two years to examine the skill added to forecasts when applying both MOS and the AnEn. Different aspects of the techniques are tested, including model horizontal and vertical resolution, number of predictors, and training set length. Both MOS and the AnEn are applied to several levels representing heights in the turbine rotor layer (40, 60, 80, 100, and 120 m). This study demonstrates the degree of improvement that different configurations of each technique provides to raw WRF forecasts, to help guide their use for low-level wind speed forecasts. It was found that both AnEn and MOS show significant improvement over the raw WRF forecasts, but the two methods do not differ significantly from each other.

Sign in / Sign up

Export Citation Format

Share Document