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Journal articles on the topic 'Wind Speed Estimation'

Author: Grafiati

Published: 6 September 2023

Last updated: 7 July 2024

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1

Clarizia, Maria Paola, and Christopher S. Ruf. "Bayesian Wind Speed Estimation Conditioned on Significant Wave Height for GNSS-R Ocean Observations." Journal of Atmospheric and Oceanic Technology 34, no. 6 (2017): 1193–202. http://dx.doi.org/10.1175/jtech-d-16-0196.1.

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AbstractSpaceborne Global Navigation Satellite System reflectometry observations of the ocean surface are found to respond to components of roughness forced by local winds and to a longer wave swell that is only partially correlated with the local wind. This dual sensitivity is largest at low wind speeds. If left uncorrected, the error in wind speeds retrieved from the observations is strongly correlated with the significant wave height (SWH) of the ocean. A Bayesian wind speed estimator is developed to correct for the long-wave sensitivity at low wind speeds. The approach requires a character

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2

Naba, Agus, and Ahmad Nadhir. "Power Curve Based-Fuzzy Wind Speed Estimation in Wind Energy Conversion Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 1 (2018): 76–87. http://dx.doi.org/10.20965/jaciii.2018.p0076.

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Availability of wind speed information is of great importance for maximization of wind energy extraction in wind energy conversion systems. The wind speed is commonly obtained from a direct measurement employing a number of anemometers installed surrounding the wind turbine. In this paper a sensorless fuzzy wind speed estimator is proposed. The estimator is easy to build without any training or optimization. It works based on the fuzzy logic principles heuristically inferred from the typical wind turbine power curve. The wind speed estimation using the proposed estimator was simulated during t

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Wang, Xiaochun, Tong Lee, and Carl Mears. "Evaluation of Blended Wind Products and Their Implications for Offshore Wind Power Estimation." Remote Sensing 15, no. 10 (2023): 2620. http://dx.doi.org/10.3390/rs15102620.

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The Cross-Calibrated Multi-Platform (CCMP) wind analysis is a satellite-based blended wind product produced using a two-dimensional variational method. The current version available publicly is Version 2 (CCMP2.0), which includes buoy winds in addition to satellite winds. Version 3 of the product (CCMP3.0) is being produced with several improvements in analysis algorithms, without including buoy winds. Here, we compare CCMP3.0 with a special version of CCMP2.0 that did not include buoy winds, so both versions are independent of buoy measurements. We evaluate them using wind data from buoys aro

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4

Østergaard, K. Z., P. Brath, and J. Stoustrup. "Estimation of effective wind speed." Journal of Physics: Conference Series 75 (July 1, 2007): 012082. http://dx.doi.org/10.1088/1742-6596/75/1/012082.

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5

Mohandes, Mohamed A., Shafiqur Rehman, and Syed Masiur Rahman. "Spatial estimation of wind speed." International Journal of Energy Research 36, no. 4 (2010): 545–52. http://dx.doi.org/10.1002/er.1774.

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6

BHARGAVA, P. K. "Estimation of monsoon wind characteristics in India." MAUSAM 53, no. 1 (2022): 19–30. http://dx.doi.org/10.54302/mausam.v53i1.1614.

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A detailed statistical analysis of monthly average wind speed data of monsoon period (June-September) for the year 1921-90 for 57 stations spread all over India have been reported. Probability densities, average wind speeds, standard deviations, kurtosis and skewness of wind speed frequency distribution for each station have been worked out. Histograms depicting relative frequency distribution of average wind speeds have also been prepared. It is observed that the different histograms do not exhibit any similarity among themselves indicating thereby that no single distribution is uniformly app

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Chiodo, Elio, Bassel Diban, Giovanni Mazzanti, and Fabio De Angelis. "A Review on Wind Speed Extreme Values Modeling and Estimation for Wind Power Plant Design and Construction." Energies 16, no. 14 (2023): 5456. http://dx.doi.org/10.3390/en16145456.

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Rapid growth of the use of wind energy calls for a more careful representation of wind speed probability distribution, both for identification and estimation purposes. In particular, a key point of the above identification and estimation aspects is representing the extreme values of wind speed probability distributions, which are of great interest both for wind energy applications and structural tower reliability analysis. The paper reviews the most adopted probability distribution models and estimation methods. In particular, for reasons which are properly discussed, attention is focused on t

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8

Bingöl, Ferhat. "Comparison of Weibull Estimation Methods for Diverse Winds." Advances in Meteorology 2020 (July 6, 2020): 1–11. http://dx.doi.org/10.1155/2020/3638423.

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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

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9

Li, Dan-Yong, Wen-Chuan Cai, Peng Li, Zi-Jun Jia, Hou-Jin Chen, and Yong-Duan Song. "Neuroadaptive Variable Speed Control of Wind Turbine With Wind Speed Estimation." IEEE Transactions on Industrial Electronics 63, no. 12 (2016): 7754–64. http://dx.doi.org/10.1109/tie.2016.2591900.

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10

Barambones, Oscar. "Robust Wind Speed Estimation and Control of Variable Speed Wind Turbines." Asian Journal of Control 21, no. 2 (2018): 856–67. http://dx.doi.org/10.1002/asjc.1779.

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11

Yu, Wenzheng, Yang Gao, Zhengyu Yuan, Xin Yao, Mingxuan Zhu, and Hanxiaoya Zhang. "Poisson-Gumbel Model for Wind Speed Threshold Estimation of Maximum Wind Speed." Computers, Materials & Continua 73, no. 1 (2022): 563–76. http://dx.doi.org/10.32604/cmc.2022.027008.

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12

Velo, Ramón, Paz López, and Francisco Maseda. "Wind speed estimation using multilayer perceptron." Energy Conversion and Management 81 (May 2014): 1–9. http://dx.doi.org/10.1016/j.enconman.2014.02.017.

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13

Palomaki, Ross T., Nathan T. Rose, Michael van den Bossche, Thomas J. Sherman, and Stephan F. J. De Wekker. "Wind Estimation in the Lower Atmosphere Using Multirotor Aircraft." Journal of Atmospheric and Oceanic Technology 34, no. 5 (2017): 1183–91. http://dx.doi.org/10.1175/jtech-d-16-0177.1.

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AbstractUnmanned aerial vehicles are increasingly used to study atmospheric structure and dynamics. While much emphasis has been on the development of fixed-wing unmanned aircraft for atmospheric investigations, the use of multirotor aircraft is relatively unexplored, especially for capturing atmospheric winds. The purpose of this article is to demonstrate the efficacy of estimating wind speed and direction with 1) a direct approach using a sonic anemometer mounted on top of a hexacopter and 2) an indirect approach using attitude data from a quadcopter. The data are collected by the multirotor

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14

Morrissey, Mark L., Werner E. Cook, and J. Scott Greene. "An Improved Method for Estimating the Wind Power Density Distribution Function." Journal of Atmospheric and Oceanic Technology 27, no. 7 (2010): 1153–64. http://dx.doi.org/10.1175/2010jtecha1390.1.

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Abstract The wind power density (WPD) distribution curve is essential for wind power assessment and wind turbine engineering. The usual practice of estimating this curve from wind speed data is to first estimate the wind speed probability density function (PDF) using a nonparametric or parametric method. The density function is then multiplied by one-half the wind speed cubed times the air density. Unfortunately, this means that minor errors in the estimation of the wind speed PDF can result in large errors in the WPD distribution curve because the cubic term in the WPD function magnifies the

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15

Lombardo, Franklin T. "Improved extreme wind speed estimation for wind engineering applications." Journal of Wind Engineering and Industrial Aerodynamics 104-106 (May 2012): 278–84. http://dx.doi.org/10.1016/j.jweia.2012.02.025.

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16

Wang, Jianzhou, Jianming Hu, and Kailiang Ma. "Wind speed probability distribution estimation and wind energy assessment." Renewable and Sustainable Energy Reviews 60 (July 2016): 881–99. http://dx.doi.org/10.1016/j.rser.2016.01.057.

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17

Khoshrodi, M. Najafi, Mohammad Jannati, and Tole Sutikno. "A Review of Wind Speed Estimation for Wind Turbine Systems Based on Kalman Filter Technique." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (2016): 1406. http://dx.doi.org/10.11591/ijece.v6i4.10735.

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This paper presents a review of wind speed estimation based on Kalman filter technique applied to wind turbine systems. Generally, wind speed measurement is performed by anemometer. The wind speed provided by the anemometer is measured at a single point of the rotor plane which is not the accurate wind speed. Also, using anemometer increases the system cost, maintenance, complexity and reduces the reliability. For these reasons, estimation of wind speed is needed for wind turbine systems. In this paper, the several wind speed estimation methods based on Kalman filter method used for wind turbi

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Khoshrodi, M. Najafi, Mohammad Jannati, and Tole Sutikno. "A Review of Wind Speed Estimation for Wind Turbine Systems Based on Kalman Filter Technique." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (2016): 1406. http://dx.doi.org/10.11591/ijece.v6i4.pp1406-1411.

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This paper presents a review of wind speed estimation based on Kalman filter technique applied to wind turbine systems. Generally, wind speed measurement is performed by anemometer. The wind speed provided by the anemometer is measured at a single point of the rotor plane which is not the accurate wind speed. Also, using anemometer increases the system cost, maintenance, complexity and reduces the reliability. For these reasons, estimation of wind speed is needed for wind turbine systems. In this paper, the several wind speed estimation methods based on Kalman filter method used for wind turbi

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19

Otero, P., X. A. Padín, M. Ruiz-Villarreal, L. M. García-García, A. F. Ríos, and F. F. Pérez. "Net sea-air CO<sub>2</sub> flux uncertainties in the Bay of Biscay based on the choice of wind speed products and gas transfer parameterizations." Biogeosciences Discussions 9, no. 8 (2012): 9993–10017. http://dx.doi.org/10.5194/bgd-9-9993-2012.

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Abstract. The estimation of sea-air CO2 fluxes are largely dependent on wind speed through the gas transfer velocity parameterization. In this paper, we quantify uncertainties in the estimation of the CO2 uptake in the Bay of Biscay resulting from using different sources of wind speed such as three different global reanalysis meteorological models (NCEP/NCAR 1, NCEP/DOE 2 and ERA-Interim), one regional high-resolution forecast model (HIRLAM-AEMet) and QuikSCAT winds, in combination with some of the most widely used gas transfer velocity parameterizations. Results show that net CO2 flux estimat

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20

Wicaksana, Haryas, Faqihza Mukhlish, Naufal Ananda, Irvan Budiawan, Arif Nur Khamdi, and Abdul Hamid Al Habib. "Surface Wind Speed Estimation on Multisites Anemometer Using Temporal Convolutional Network." Jurnal Otomasi Kontrol dan Instrumentasi 16, no. 1 (2024): 44–52. http://dx.doi.org/10.5614/joki.2024.16.1.5.

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Surface winds in various locations are measured simultaneously using a multisite anemometer network. This network is susceptible to system failures due to sensor damage, causing a data gap during sensor removal and reinstallation. This research develops a wind speed estimation model on a multisite anemometer using the Temporal Convolutional Network (TCN) algorithm. TCN processes time domain signals in parallel, thus significantly cutting the computation time. Minutely wind speed data set was obtained from four anemometers at Juanda International Airport in Surabaya from January 1, 2022 – Decem

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21

Jiang, Haoyu. "Wind speed and direction estimation from wave spectra using deep learning." Atmospheric Measurement Techniques 15, no. 1 (2022): 1–9. http://dx.doi.org/10.5194/amt-15-1-2022.

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Abstract. High-frequency parts of ocean wave spectra are strongly coupled to the local wind. Measurements of ocean wave spectra can be used to estimate sea surface winds. In this study, two deep neural networks (DNNs) were used to estimate the wind speed and direction from the first five Fourier coefficients from buoys. The DNNs were trained by wind and wave measurements from more than 100 meteorological buoys during 2014–2018. It is found that the wave measurements can best represent the wind information about 40 min previously because the high-frequency portion of the wave spectrum integrate

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22

Otero, P., X. A. Padin, M. Ruiz-Villarreal, L. M. García-García, A. F. Ríos, and F. F. Pérez. "Net sea–air CO<sub>2</sub> flux uncertainties in the Bay of Biscay based on the choice of wind speed products and gas transfer parameterizations." Biogeosciences 10, no. 5 (2013): 2993–3005. http://dx.doi.org/10.5194/bg-10-2993-2013.

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Abstract. The estimation of sea–air CO2 fluxes is largely dependent on wind speed through the gas transfer velocity parameterization. In this paper, we quantify uncertainties in the estimation of the CO2 uptake in the Bay of Biscay resulting from the use of different sources of wind speed such as three different global reanalysis meteorological models (NCEP/NCAR 1, NCEP/DOE 2 and ERA-Interim), one high-resolution regional forecast model (HIRLAM-AEMet), winds derived under the Cross-Calibrated Multi-Platform (CCMP) project, and QuikSCAT winds in combination with some of the most widely used gas

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23

Bessho, Kotaro, Mark DeMaria, and John A. Knaff. "Tropical Cyclone Wind Retrievals from the Advanced Microwave Sounding Unit: Application to Surface Wind Analysis." Journal of Applied Meteorology and Climatology 45, no. 3 (2006): 399–415. http://dx.doi.org/10.1175/jam2352.1.

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Abstract Horizontal winds at 850 hPa from tropical cyclones retrieved using the nonlinear balance equation, where the mass field was determined from Advanced Microwave Sounding Unit (AMSU) temperature soundings, are compared with the surface wind fields derived from NASA's Quick Scatterometer (QuikSCAT) and Hurricane Research Division H*Wind analyses. It was found that the AMSU-derived wind speeds at 850 hPa have linear relations with the surface wind speeds from QuikSCAT or H*Wind. There are also characteristic biases of wind direction between AMSU and QuikSCAT or H*Wind. Using this informati

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24

Rácz, Tibor. "Wind speed estimation for the correction of wind-caused errors in historical precipitation data." Időjárás 127, no. 2 (2023): 199–216. http://dx.doi.org/10.28974/idojaras.2023.2.3.

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The wind has a significant impact on the accuracy of precipitation measurement in the case of collecting gauges. As widely known, the velocity field of wind suffers a deformation over and around the precipitation gauges, which causes deviations in the measured quantities. This error must be corrected if it is possible. Thanks to numerous researches, correction formulas give tools for adjusting precipitation data in the function of the wind speed and raindrop distribution (DSD) relationship, gauge parameters, and for the case of snow and temperature. The measured intensity of precipitation in h

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25

Dussol, Abïgaëlle, and Cédric Chavanne. "Estimation of the Wind Field with a Single High-Frequency Radar." Remote Sensing 16, no. 13 (2024): 2258. http://dx.doi.org/10.3390/rs16132258.

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Over several decades, high-frequency (HF) radars have been employed for remotely measuring various ocean surface parameters, encompassing surface currents, waves, and winds. Wind direction and speed are usually estimated from both first-order and second-order Bragg-resonant scatter from two or more HF radars monitoring the same area of the ocean surface. This limits the observational domain to the common area where second-order scatter is available from at least two radars. Here, we propose to estimate wind direction and speed from the first-order scatter of a single HF radar, yielding the sam

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Cauchy, Pierre, Karen J. Heywood, Nathan D. Merchant, Bastien Y. Queste, and Pierre Testor. "Wind Speed Measured from Underwater Gliders Using Passive Acoustics." Journal of Atmospheric and Oceanic Technology 35, no. 12 (2018): 2305–21. http://dx.doi.org/10.1175/jtech-d-17-0209.1.

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AbstractWind speed measurements are needed to understand ocean–atmosphere coupling processes and their effects on climate. Satellite observations provide sufficient spatial and temporal coverage but are lacking adequate calibration, while ship- and mooring-based observations are spatially limited and have technical shortcomings. However, wind-generated underwater noise can be used to measure wind speed, a method known as Weather Observations Through Ambient Noise (WOTAN). Here, we adapt the WOTAN technique for application to ocean gliders, enabling calibrated wind speed measurements to be comb

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Kurbatova, Maria, Konstantin Rubinstein, Inna Gubenko, and Grigory Kurbatov. "Comparison of seven wind gust parameterizations over the European part of Russia." Advances in Science and Research 15 (November 19, 2018): 251–55. http://dx.doi.org/10.5194/asr-15-251-2018.

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Abstract. Wind gusts are extreme events which can cause severe damage. Gusts can reach significant values even during medium winds. However, numerical atmospheric models are designed to reproduce average wind speed, not gusts. There are several approaches to estimating wind gusts. Seven different methods are applied to WRF-ARW model output. Results are compared to high-frequency wind speed measurements using ultrasonic anemometers and temperature profiler measurement at the same point in Moscow. Data gathered from synoptic station network over the European part of Russia were also included in

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Shao, Weizeng, Yuyi Hu, Ferdinando Nunziata, Valeria Corcione, Maurizio Migliaccio, and Xiaoming Li. "Cyclone Wind Retrieval Based on X-Band SAR-Derived Wave Parameter Estimation." Journal of Atmospheric and Oceanic Technology 37, no. 10 (2020): 1907–24. http://dx.doi.org/10.1175/jtech-d-20-0014.1.

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AbstractIn this study, a method for retrieving wind speed from synthetic aperture radar (SAR) imagery collected under extreme weather conditions is proposed. The rationale for this approach relies on the fact that, although copolarized channels exhibit saturation for wind speed >~20 m s−1, the wave growth can be successfully exploited to gather information on wind speed under extreme weather conditions. Hence, in this study, the intrinsic relationship among the wind-wave triplets [wind speed at 10 m above the sea surface, significant wave height (SWH), and peak wave period] is exploited in

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29

Hahm, Jae-Hee, Ha-Yoon Jeong, and Kyung-Hwan Kwak. "Estimation of Strong Wind Distribution on the Korean Peninsula for Various Recurrence Periods: Significance of Nontyphoon Conditions." Advances in Meteorology 2019 (April 14, 2019): 1–10. http://dx.doi.org/10.1155/2019/8063169.

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Long-term automated synoptic observing system (ASOS) data collected from 101 stations over a period of 50 years (1967–2016) were analyzed to investigate the distribution of strong winds on the Korean peninsula by utilizing a statistical method. The Gumbel distribution was used to estimate the wind speed for recurrence periods of 1, 10, 50, 75, and 100 years. For all recurrence periods, the coastal regions experienced higher wind speeds, which exceeded the strong wind advisory level, than the inland and metropolitan regions. The strong winds were predominantly induced by summertime typhoons, es

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Nam, Yoon-Su, Jeong-Gi Kim, In-Su Paek, Young-Hwan Moon, Seog-Joo Kim, and Dong-Joon Kim. "Feedforward Pitch Control Using Wind Speed Estimation." Journal of Power Electronics 11, no. 2 (2011): 211–17. http://dx.doi.org/10.6113/jpe.2011.11.2.211.

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31

Wang, Yu, Deji Wang, Jianghai Zhao, and Changan Zhu. "Wind speed spatial estimation using geostatistical kriging." IOP Conference Series: Earth and Environmental Science 619 (December 22, 2020): 012049. http://dx.doi.org/10.1088/1755-1315/619/1/012049.

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32

Okoth, Steven, Otieno Fredrick, and Isaac Motochi. "Investigation of Wind Characteristics and Estimation of Wind Power Potential of Narok County Using Weibull Distribution." Journal of Energy Research and Reviews 15, no. 2 (2023): 35–46. http://dx.doi.org/10.9734/jenrr/2023/v15i2305.

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Aim: To investigate wind characteristics and estimate wind power density of Narok weather station in Narok county using Weibull distribution. Research Design: Historical hourly wind direction and speed data recorded by the Kenya Meteorological Department in Narok weather station was analyzed. Place and duration: The study utilized data samples collected at Narok weather station over a period spanning from 2011 to 2021. Methods: To assess the temporal characteristics, a statistical average technique was employed. The spatial aspect, specifically wind speed variation with heigh

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Wu, Qiuyi, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi. "A conditional approach for joint estimation of wind speed and direction under future climates." Advances in Statistical Climatology, Meteorology and Oceanography 8, no. 2 (2022): 205–24. http://dx.doi.org/10.5194/ascmo-8-205-2022.

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Abstract. This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by a combined characterization of the wind direction distribution and conditional distribution of wind on the direction, hence enabling an assessment of the joint wind speed and direction distribution and their cha

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Abo-Khalil, Ahmed G., Saeed Alyami, Khairy Sayed, and Ayman Alhejji. "Dynamic Modeling of Wind Turbines Based on Estimated Wind Speed under Turbulent Conditions." Energies 12, no. 10 (2019): 1907. http://dx.doi.org/10.3390/en12101907.

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Large-scale wind turbines with a large blade radius rotates under fluctuating conditions depending on the blade position. The wind speed is maximum in the highest point when the blade in the upward position and minimum in the lowest point when the blade in the downward position. The spatial distribution of wind speed, which is known as the wind shear, leads to periodic fluctuations in the turbine rotor, which causes fluctuations in the generator output voltage and power. In addition, the turbine torque is affected by other factors such as tower shadow and turbine inertia. The space between the

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Lydia, M., S. Suresh Kumar, A. Immanuel Selvakumar, and G. Edwin Prem Kumar. "Wind resource estimation using wind speed and power curve models." Renewable Energy 83 (November 2015): 425–34. http://dx.doi.org/10.1016/j.renene.2015.04.045.

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Hou Lio, Alan Wai, and Fanzhong Meng. "Effective wind speed estimation for wind turbines in down-regulation." Journal of Physics: Conference Series 1452 (January 2020): 012008. http://dx.doi.org/10.1088/1742-6596/1452/1/012008.

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Kumar, Manish, and Cherian Samuel. "Wind energy potential estimation with prediction of wind speed distribution." International Journal of Intelligent Systems Technologies and Applications 17, no. 1/2 (2018): 19. http://dx.doi.org/10.1504/ijista.2018.091585.

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Kumar, Manish, and Cherian Samuel. "Wind energy potential estimation with prediction of wind speed distribution." International Journal of Intelligent Systems Technologies and Applications 17, no. 1/2 (2018): 19. http://dx.doi.org/10.1504/ijista.2018.10012880.

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Nayak, Ashwini Kumar, Kanungo Barada Mohanty, Vinaya Sagar Kommukuri, and Kishor Thakre. "Capacity value estimation of wind power incorporating hourly wind speed." World Journal of Engineering 14, no. 6 (2017): 497–502. http://dx.doi.org/10.1108/wje-12-2016-0160.

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Purpose The purpose of this paper is to show the effect of randomness of wind speed on the capacity value estimation of wind power. Three methods that incorporate hourly wind speed have been evaluated. Design/methodology/approach Wind speed is simulated using autoregressive moving average method and is included in the calculation of reliability index as a negative load on an hourly basis. The reliability index is calculated before and after the addition of wind capacity. Increment of load or alteration of conventional capacity will lead to capacity estimation. Findings Among the aforementioned

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Song, Dongran, Jian Yang, Mi Dong, and Young Hoon Joo. "Kalman filter-based wind speed estimation for wind turbine control." International Journal of Control, Automation and Systems 15, no. 3 (2017): 1089–96. http://dx.doi.org/10.1007/s12555-016-0537-1.

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Hu, Y., K. Stamnes, M. Vaughan, et al. "Sea surface wind speed estimation from space-based lidar measurements." Atmospheric Chemistry and Physics 8, no. 13 (2008): 3593–601. http://dx.doi.org/10.5194/acp-8-3593-2008.

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Abstract. Global satellite observations of lidar backscatter measurements acquired by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and collocated sea surface wind speed data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), are used to investigate the relation between wind driven wave slope variance and sea surface wind speed. The new slope variance – wind speed relation established from this study is similar to the linear relation from Cox-Munk (1954) and the log-linear relation from Wu (1990) for wind speed lar

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Hu, Y., K. Stamnes, M. Vaughan, et al. "Sea surface wind speed estimation from space-based lidar measurements." Atmospheric Chemistry and Physics Discussions 8, no. 1 (2008): 2771–93. http://dx.doi.org/10.5194/acpd-8-2771-2008.

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Abstract. Global satellite observations of lidar backscatter measurements acquired by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and collocated sea surface wind speed data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), are used to investigate the relation between wind driven wave slope variance and sea surface wind speed. The new slope variance – wind speed relation established from this study is similar to the linear relation from Cox-Munk (1954) and the log-linear relation from Wu (1972, 1990) for wind spe

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Salami, Adekunlé Akim, Seydou Ouedraogo, Koffi Mawugno Kodjoa, and Ayité Sénah Akoda Ajavona. "Influence of the Random Data Sampling in Estimation of Wind Speed Resource: Case Study." International Journal of Renewable Energy Development 11, no. 1 (2021): 133–43. http://dx.doi.org/10.14710/ijred.2022.38511.

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In this study, statistical analysis is performed in order to characterize wind speeds distribution according to different samples randomly drawn from wind speed data collected. The purpose of this study is to assess how random sampling influences the estimation quality of the shape (k) and scale (c) parameters of a Weibull distribution function. Five stations were chosen in West Africa for the study, namely: Accra Kotoka, Cotonou Cadjehoun, Kano Mallam Aminu, Lomé Tokoin and Ouagadougou airport. We used the energy factor method (EPF) to compute shape and scale parameters. Statistical indicator

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Zhang, Lei, Lun Xie, Qinkai Han, Zhiliang Wang, and Chen Huang. "Probability Density Forecasting of Wind Speed Based on Quantile Regression and Kernel Density Estimation." Energies 13, no. 22 (2020): 6125. http://dx.doi.org/10.3390/en13226125.

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Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the noise of raw wind speed series. Both linear QR (LQR) and nonlinear QR (NQR, including quantile regression neural network (QRNN), quantile regression random forest (QRRF), and quantile regression support vector machine (QRSVM)) models are, respectively, utilized to study the de-noised wind speed series. An ensemble of conditional quantiles is obtaine

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Kelly, Mark, and Hans E. Jørgensen. "Statistical characterization of roughness uncertainty and impact on wind resource estimation." Wind Energy Science 2, no. 1 (2017): 189–209. http://dx.doi.org/10.5194/wes-2-189-2017.

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Abstract. In this work we relate uncertainty in background roughness length (z0) to uncertainty in wind speeds, where the latter are predicted at a wind farm location based on wind statistics observed at a different site. Sensitivity of predicted winds to roughness is derived analytically for the industry-standard European Wind Atlas method, which is based on the geostrophic drag law. We statistically consider roughness and its corresponding uncertainty, in terms of both z0 derived from measured wind speeds as well as that chosen in practice by wind engineers. We show the combined effect of ro

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Nie, Yonghui, He Wang, Lei Gao, Chunying Wu, and Meng Xi. "Adaptive Parameter Estimation for Static Var Generators Based on Wind Speed Fluctuation of Wind Farms." International Transactions on Electrical Energy Systems 2022 (March 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/3877777.

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The emergence of flexible AC transmission technology provides a new technical means for ensuring the reliable grid connection and stable operation of wind farms. Among them, the static reactive power generator has a fast response speed, which can accurately compensate for the reactive power of the wind farm and improve the power factor; this is widely used in wind farms. To obtain accurate static var generator (SVG) parameters to meet the reliability requirements of a power system, we propose an adaptive estimation method that considers the wind speed fluctuation of wind farms. First, analyzin

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Bian, Fengshuo, Keqilao Meng, Yan Jia, Jianlong Ma, and Rihan Hai. "Application of Effective Wind Speed Estimation and New Sliding Mode Observer in Wind Energy Conversion System." Mathematical Problems in Engineering 2022 (May 4, 2022): 1–11. http://dx.doi.org/10.1155/2022/8863163.

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The wind speed information measured by the wind speed sensor in the wind turbine generator may differ from sufficient wind speed. Therefore, this paper proposes a new effective wind speed calculation method and applies it to the optimal maximum power point tracking (MPPT) of wind energy. After estimating the turbine torque and its rotor speed, the process reverses the turbine’s aerodynamic model. The extended state observer (ESO) based on sliding mode control is used to estimate the aerodynamic torque, which solves complex and challenging tuning of the traditional ESO parameters, and replaces

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Borunda, Mónica, Katya Rodríguez-Vázquez, Raul Garduno-Ramirez, Javier de la Cruz-Soto, Javier Antunez-Estrada, and Oscar A. Jaramillo. "Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming." Energies 13, no. 8 (2020): 1885. http://dx.doi.org/10.3390/en13081885.

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Given the imminent threats of climate change, it is urgent to boost the use of clean energy, being wind energy a potential candidate. Nowadays, deployment of wind turbines has become extremely important and long-term estimation of the produced power entails a challenge to achieve good prediction accuracy for site assessment, economic feasibility analysis, farm dispatch, and system operation. We present a method for long-term wind power forecasting using wind turbine properties, statistics, and genetic programming. First, due to the high degree of intermittency of wind speed, we characterize it

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Li, Huang, Angui Li, Linhua Zhang, et al. "Estimation of Wind Speed Based on Schlieren Machine Vision System Inspired by Greenhouse Top Vent." Sensors 23, no. 15 (2023): 6929. http://dx.doi.org/10.3390/s23156929.

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Greenhouse ventilation has always been an important concern for agricultural workers. This paper aims to introduce a low-cost wind speed estimating method based on SURF (Speeded Up Robust Feature) feature matching and the schlieren technique for airflow mixing with large temperature differences and density differences like conditions on the vent of the greenhouse. The fluid motion is directly described by the pixel displacement through the fluid kinematics analysis. Combining the algorithm with the corresponding image morphology analysis and SURF feature matching algorithm, the schlieren image

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Calabrese, Diego, Gioacchino Tricarico, Elia Brescia, Giuseppe Leonardo Cascella, Vito Giuseppe Monopoli, and Francesco Cupertino. "Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer." Energies 13, no. 18 (2020): 4647. http://dx.doi.org/10.3390/en13184647.

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This paper proposes a new variable structure control scheme for a variable-speed, fixed-pitch ducted wind turbine, equipped with an annular, brushless permanent-magnet synchronous generator, considering a back-to-back power converter topology. The purpose of this control scheme is to maximise the aerodynamic power over the entire wind speed range, considering the mechanical safety limits of the ducted wind turbine. The ideal power characteristics are achieved with the design of control laws aimed at performing the maximum power point tracking control in the low wind speeds region, and the cons

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