State of the Art in Cup Anemometry

2021 ◽  
Author(s):  
Amir A. S. Pirooz ◽  
Richard G.J. Flay ◽  
Richard Turner ◽  
Cesar Azorin-Molina
Keyword(s):  
Wind Speed ◽  
Wind Farms ◽  
Field Measurements ◽  
Measuring System ◽  
Measurement Instruments ◽  
Response Characteristics ◽  
Wind Speeds ◽  
Experimental Approaches ◽  
Almost All

<p>Despite the great development of more accurate and sophisticated wind-measurement instruments, cup anemometers remain today the most widely used and popular anemometer in measuring wind speeds at meteorological stations and wind farms. In addition, almost all the available long-term wind speed time series across the world have been recorded by cup anemometers. Studying the response of cup anemometers and errors associated with their measurements, and also how the cup anemometer measurements are comparable with modern sensors, is of great importance, and can affect meteorological and climatological studies of long-term wind speed trends, and also wind energy estimations. </p><p>Although cup anemometers are known for being robust and reliable, long-term field measurements of wind speeds by these wind sensors can be associated with errors and uncertainties affecting the quality of recorded data and subsequent analyses. When analysing wind speed data, it is essential to understand these errors and compensate for them and distinguish them from the real climate signals.</p><p>A comprehensive review on various aspects of anemometry, particularly cup anemometers, is presented in this paper. This review includes the different designs and theory developed from the invention of this wind-speed measuring system to very recent works, the response characteristics of anemometers, anemometer calibration procedures, field and wind-tunnel experiments on anemometers, etc. In addition, the different sources of errors and uncertainties are introduced and methods, including statistical, mathematical and experimental approaches, proposed to quantify and remedy the effects of these errors are presented. Lastly, several comparative studies that investigated the response characteristics of different types of cup anemometers and other anemometers are reviewed.</p>

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 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 Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

2019 ◽  
Vol 11 (3) ◽  
pp. 665 ◽  
Author(s):  
Lingzhi Wang ◽  
Jun Liu ◽  
Fucai Qian
Keyword(s):  
Wind Speed ◽  
Frequency Distribution ◽  
Least Squares Method ◽  
Wind Farms ◽  
Rayleigh Distribution ◽  
Distribution Model ◽  
Exponential Polynomial ◽  
Distribution Models ◽  
Wind Speed Data ◽  
Wind Speeds

This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.

Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer Observations

2020 ◽  
Vol 37 (2) ◽  
pp. 279-297 ◽  
Author(s):  
Agustinus Ribal ◽  
Ian R. Young
Keyword(s):  
Wind Speed ◽  
Cross Validation ◽  
Regression Line ◽  
Total Duration ◽  
Global Ocean ◽  
High Wind ◽  
Wind Speeds ◽  
Buoy Data ◽  
Ocean Wind

AbstractGlobal ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.

scholarly journals A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1596 ◽  
Author(s):  
Xin Zhao ◽  
Haikun Wei ◽  
Chenxi Li ◽  
Kanjian Zhang
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
State Space ◽  
State Space Model ◽  
Hybrid Approach ◽  
Wind Farms ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Space Model ◽  
Wind Speeds

The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy.

scholarly journals The Transition from Downward to Upward Air–Sea Momentum Flux in Swell-Dominated Light Wind Conditions

2018 ◽  
Vol 75 (8) ◽  
pp. 2579-2588 ◽  
Author(s):  
Ulf Högström ◽  
Erik Sahlée ◽  
Ann-Sofi Smedman ◽  
Anna Rutgersson ◽  
Erik Nilsson ◽  
...  
Keyword(s):  
Wind Speed ◽  
Momentum Flux ◽  
Water Surface ◽  
Sea Waves ◽  
Progressive Wave ◽  
Wind Speeds ◽  
Low Wind Speed ◽  
Light Wind ◽  
Wind Sea ◽  
Almost All

Abstract Fifteen hours of consecutive swell data from the experiment Flux, État de la Mer, et Télédétection en Condition de Fetch Variable (FETCH) in the Mediterranean show a distinct upward momentum flux. The characteristics are shown to vary systematically with wind speed. A hysteresis effect is found for wave energy of the wind-sea waves when represented as a function of wind speed, displaying higher energy during decaying winds compared to increasing winds. For the FETCH measurements, the upward momentum transfer regime is found to begin for wind speeds lower than about U = 4 m s−1. For the lowest observed wind speeds U < 2.4 m s−1, the water surface appears to be close to dynamically smooth. In this range almost all the upward momentum flux is accomplished by the peak in the cospectrum between the vertical and horizontal components of the wind velocity. It is demonstrated that this contribution in turn is linearly related to the swell significant wave height Hsd in the range 0.6 < Hsd < 1.4 m. For Hsd < 0.6 m, the contribution is zero in the present dataset but may depend on the swell magnitude in other situations. It is speculated that the observed upward momentum flux in the smooth regime, which is so strongly related to the cospectral peak at the dominant swell frequency, might be caused by the recirculation mechanism found by Wen and Mobbs in their numerical simulation of laminar flow of a nonlinear progressive wave at low wind speed.

scholarly journals Numerical Simulation of the Aeroelastic Response of Wind Turbines in Typhoons Based on the Mesoscale WRF Model

2019 ◽  
Vol 12 (1) ◽  
pp. 34
Author(s):  
Long Wang ◽  
Cheng Chen ◽  
Tongguang Wang ◽  
Weibin Wang
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Direction ◽  
Flow Direction ◽  
Wrf Model ◽  
Normal Operation ◽  
Average Wind Speed ◽  
Aeroelastic Response ◽  
Response Characteristics ◽  
Wind Speeds

A new simulation method for the aeroelastic response of wind turbines under typhoons is proposed. The mesoscale Weather Research and Forecasting (WRF) model was used to simulate a typhoon’s average wind speed field. The measured power spectrum and inverse Fourier transform method were coupled to simulate the pulsating wind speed field. Based on the modal method and beam theory, the wind turbine model was constructed, and the GH-BLADED commercial software package was used to calculate the aerodynamic load and aeroelastic response. The proposed method was applied to assess aeroelastic response characteristics of a commercial 6 MW offshore wind turbine under different wind speeds and direction variation patterns for the case study of typhoon Hagupit (2008), with a maximal wind speed of 230 km/h. The simulation results show that the typhoon’s average wind speed field and turbulence characteristics simulated by the proposed method are in good agreement with the measured values: Their difference in the main flow direction is only 1.7%. The scope of the wind turbine blade in the typhoon is significantly larger than under normal wind, while that under normal operation is higher than that under shutdown, even at low wind speeds. In addition, an abrupt change in wind direction has a significant impact on wind turbine response characteristics. Under normal operation, a sharp variation of the wind direction by 90 degrees in 6 s increases the wind turbine (WT) vibration scope by 27.9% in comparison with the case of permanent wind direction. In particular, the maximum deflection of the wind tower tip in the incoming flow direction reaches 28.4 m, which significantly exceeds the design standard safety threshold.

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.

Empirical Procedures for Long-Term Prediction of Fatigue Damage for an Instrumented Marine Riser

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
C. Shi ◽  
L. Manuel ◽  
M. A. Tognarelli
Keyword(s):  
Fatigue Damage ◽  
Field Measurements ◽  
Damage Estimation ◽  
Current Profile ◽  
Short Term ◽  
Response Characteristics ◽  
Damage Rate ◽  
Using Data ◽  
Analytical Approaches

Slender marine risers used in deepwater applications can experience vortex-induced vibration (VIV). It is becoming increasingly common for field monitoring campaigns to be undertaken wherein data loggers such as strain sensors and/or accelerometers are installed on such risers to aid in VIV-related fatigue damage estimation. Such damage estimation relies on the application of empirical procedures that make use of the collected data. This type of damage estimation can be undertaken for different current profiles encountered. The empirical techniques employed make direct use of the measurements and key components in the analyszes (such as participating riser modes selected for use in damage estimation) are intrinsically dependent on the actual current profiles. Fatigue damage predicted in this manner is in contrast to analytical approaches that rely on simplifying assumptions on both the flow conditions and the response characteristics. Empirical fatigue damage estimates conditional on current profile type can account explicitly even for complex response characteristics, participating riser modes, etc. With significant amounts of data, it is possible to establish “short-term” fatigue damage rate distributions conditional on current type. If the relative frequency of different current types is known from metocean studies, the short-term fatigue distributions can be combined with the current distributions to yield integrated “long-term” fatigue damage rate distributions. Such a study is carried out using data from the Norwegian Deepwater Programme (NDP) model riser subject to several sheared and uniform current profiles and with assumed probabilities for different current conditions. From this study, we seek to demonstrate the effectiveness of empirical techniques utilized in combination with field measurements to predict the long-term fatigue damage and the fatigue failure probability.

Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing

Energy ◽  
2011 ◽  
Vol 36 (3) ◽  
pp. 1571-1581 ◽  
Author(s):  
L. Carro-Calvo ◽  
S. Salcedo-Sanz ◽  
N. Kirchner-Bossi ◽  
A. Portilla-Figueras ◽  
L. Prieto ◽  
...  
Keyword(s):  
Wind Speed ◽  
Evolutionary Computing ◽  
Wind Farms ◽  
Speed Estimation
Sign in / Sign up

Export Citation Format

Share Document