Journal articles: 'Wind Speed Estimation'

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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 (June 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 characterization of the joint probability of occurrence of wind speed and SWH, which is derived from archival reanalysis sea-state records. The Bayesian estimator is applied to spaceborne data collected by the Technology Demonstration Satellite-1 (TechDemoSat-1) and is found to provide significant improvement in wind speed retrieval at low winds, relative to a conventional retrieval that does not account for SWH. At higher wind speeds, the wind speed and SWH are more highly correlated and there is much less need for the correction.

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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 (January 20, 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 the operation of a squirrel-cage induction generator-based wind energy conversion system. The performance of the proposed estimator was verified by the well estimated wind speed obtained under the wind speed variation.

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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 (May 18, 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 around the coasts of the United States and discuss the implications for the wind power industry and offshore wind farms. CCMP2.0 uses ERA-Interim 10 m winds as the background to fill observational gaps. CCMP3.0 uses ERA5 10 m neutral winds as the background. Because ERA5 winds are biased towards lower values at higher wind conditions, CCMP3.0 corrected this bias by matching ERA5 wind speeds with satellite scatterometer wind speeds using a histogram matching method. Our evaluation indicates that CCMP3.0 has better agreement with the independent buoy winds, primarily for higher winds (>10 m/s). This is reflected by the higher correlation and lower root-mean-squared differences of CCMP3.0 versus buoy winds, especially for higher wind conditions. For the U.S. coastal region (within 200 km), the mean wind speed of CCMP3.0 is enhanced by 1–2%, and the wind speed standard deviation is enhanced by around 3–5%. These changes in wind speed and its standard deviation from CCMP2.0 to CCMP3.0 cause an 8–12% increase in wind power density. The wind power density along the U.S. coastal region is also correlated with various climate indices depending on locations, providing a useful approach for predicting wind power on subseasonal to interannual timescales.

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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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Mohandes, Mohamed A., Shafiqur Rehman, and Syed Masiur Rahman. "Spatial estimation of wind speed." International Journal of Energy Research 36, no. 4 (August 25, 2010): 545–52. http://dx.doi.org/10.1002/er.1774.

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BHARGAVA, P. K. "Estimation of monsoon wind characteristics in India." MAUSAM 53, no. 1 (January 18, 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 applicable for all the stations. It is also seen that the average wind speeds during monsoon period over major part of India varies from 7 to 14 kmph. Further, at most of the stations average monsoon wind speed is generally higher than average annual wind speeds. It is also noted that most of the time the wind speed exceeds 10 kmph in coastal regions of Gujarat and southern parts of the peninsular India. The information generated is of multi fold application such as (i) Identification of sites suitable for installation of Wind Energy Conversion Systems (ii) Development of Driving Rain Index and (iii) Design of buildings for creating comfortable environment indoors.

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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 (July 18, 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 the evaluation of an opportune “safety index” related to extreme values of wind speeds or gusts. This topic has gained increasing attention in recent years in both wind energy generation assessment and also in risk and structural reliability and safety analysis. With regard to wind energy generation, there is great sensitivity in the relationship between wind speed extreme upper quantiles and the corresponding wind energy quantiles. Concerning the risk and reliability analysis of structures, extreme wind speed value characterization is useful for a proper understanding of the destructive wind forces that may affect structural tower reliability analysis and, consequently, the proper choice of the cut off wind speed value; therefore, the above two kinds of analyses are somewhat related to each other. The focus is on the applications of the Bayesian inference technique for estimating the above safety index due to its effectiveness and usefulness.

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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 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.

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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 (December 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 (April 19, 2018): 856–67. http://dx.doi.org/10.1002/asjc.1779.

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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 (May 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 aircraft hovering 10 m above ground adjacent to one or more sonic anemometers. Wind speed and direction show good agreement with sonic anemometer measurements in the initial experiments. Typical errors in wind speed and direction are smaller than 0.5 and 30°, respectively. Multirotor aircraft provide a promising alternative to traditional platforms for vertical profiling in the atmospheric boundary layer, especially in conditions where a tethered balloon system is typically deployed.

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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 (July 1, 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 error. To avoid this problem, this paper presents a new method of estimating the WPD distribution curve through a direct estimation of the curve using a Gauss–Hermite expansion. It is demonstrated that the proposed method provides a much more reliable estimate of the WPD distribution curve.

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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 (August 1, 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 turbine systems are reviewed.

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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 (August 1, 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 turbine systems are reviewed.

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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 (August 1, 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 estimations during an entire seasonal cycle may differ up to 240% depending on the wind speed product and the gas exchange parameterization. The comparison of satellite and model derived winds with observations at buoys advises against the systematic overestimation of NCEP-2 and the underestimation of NCEP-1. In this region, QuikSCAT has the best performing, although ERA-Interim becomes the best choice in areas near the coastline or when the time resolution is the constraint.

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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 – December 24, 2023. The model design comprises data pre-processing, dominant wind direction analysis, hyperparameter determination, training, and testing on actual data. TCN estimation models are divided into easterly, westerly, transitional, and all-directional models. These wind speed estimation models strongly correlate with actual data, with correlation coefficients of 0.70, 0.77, and 0.87. Overall, the accuracy of the TCN-based estimation model conforms to World Meteorological Organization (WMO) requirements for wind speed measurements. It achieves RMSE<5 m/s and MAE<3 m/s. As for computation duration, TCN processes the training for 87 seconds per epoch and completes the estimation in 37 seconds, much faster than CNN-BiDLSTM’'s training duration of 2206 seconds per epoch and estimation completion of 548 seconds.

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Jiang, Haoyu. "Wind speed and direction estimation from wave spectra using deep learning." Atmospheric Measurement Techniques 15, no. 1 (January 3, 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 integrates preceding wind conditions. The overall root-mean-square error (RMSE) of estimated wind speed is ∼1.1 m s−1, and the RMSE of the wind direction is ∼ 14∘ when wind speed is 7–25 m s−1. This model can be used not only for the wind estimation for compact wave buoys but also for the quality control of wind and wave measurements from meteorological buoys.

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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 (May 3, 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 transfer velocity parameterizations. Results show that net CO2 flux estimations during an entire seasonal cycle (September 2002–September 2003) may vary by a factor of ~ 3 depending on the selected wind speed product and the gas exchange parameterization, with the highest impact due to the last one. The comparison of satellite- and model-derived winds with observations at buoys advises against the systematic overestimation of NCEP-2 and the underestimation of NCEP-1. In the coastal region, the presence of land and the time resolution are the main constraints of QuikSCAT, which turns CCMP and ERA-Interim in the preferred options.

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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 (March 1, 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 information to adjust the speed and correct for the directional bias, a new algorithm was developed for estimation of the tropical cyclone surface wind field from the AMSU-derived 850-hPa winds. The algorithm was evaluated in two independent cases from Hurricanes Floyd (1999) and Michelle (2001), which were observed simultaneously by AMSU, QuikSCAT, and H*Wind. In this evaluation the AMSU adjustment algorithm for wind speed worked well. Results also showed that the bias correction algorithm for wind direction has room for improvement.

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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 historical data allows estimating the DSD, but in most cases, there are no simultaneously measured wind speed data coupled to the historical precipitation data. Characteristic data of wind speed can be estimated based on the wind speed statistics, and these data can be utilized for the statistical correction of the precipitation measurements. The statistical correction means that the rainfall data can be adjusted with the expected value of the wind speed for a more extended observation period, assuming a stationarity of wind speed statistics for the given location. After the statistical correction, the unique data will not be unbiased, but statistically they will be closer to the actual value, and the correction will be statistically correct in inherited perecipitation cheracteristics, as for example the IDF curves. For this correction, an investigation is necessary to find the adequate wind statistics for the rainfall correction. This paper shows the results of a study about the relation of statistics of wind speeds during precipitation, based on a 10-minute sampling period. The wind speed data were independent of the rain depth (or intensity) data. The result of the study shows that the distribution of wind speeds differs of the wind speed distribution measured in the precipitation events. This difference can be treated easily using the stable rate of the means of these distributions. This result gives a step toward correcting the wind-affected error of historical precipitation data.

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Dussol, Abïgaëlle, and Cédric Chavanne. "Estimation of the Wind Field with a Single High-Frequency Radar." Remote Sensing 16, no. 13 (June 21, 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 same spatial coverage as for surface radial currents. Wind direction is estimated using the ratio of the positive and negative first-order Bragg peaks intensity, with a new simple algorithm to remove the left/right directional ambiguity from a single HF radar. Wind speed is estimated from wind direction and de-tided surface radial currents using an artificial neural network which has been trained with in situ wind speed observations. Radar-derived wind estimations are compared with in situ observations in the Lower Saint-Lawrence Estuary (Quebec, Canada). The correlation coefficients between radar-estimated and in situ wind directions range from 0.84 to 0.95 for Wellen Radars (WERAs) and from 0.79 to 0.97 for Coastal Ocean Dynamics Applications Radars (CODARs), while the root mean square differences range from 8° to 12° for WERAs and from 10° to 19° for CODARs. Correlation coefficients between the radar-estimated and the in situ wind speeds range from 0.89 to 0.93 for WERAs and from 0.81 to 0.93 for CODARs, while the root mean square differences range from 1.3 m.s−1 to 2.3 m.s−1 for WERAs and from 1.6 m.s−1 to 3.9 m.s−1 for CODARs.

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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 (December 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 combined with contemporaneous oceanographic profiles over extended spatial and temporal scales. We demonstrate the methodology in three glider surveys in the Mediterranean Sea during winter 2012/13. Wind speeds ranged from 2 to 21.5 m s−1, and the relationship to underwater ambient noise measured from the glider was quantified. A two-regime linear model is proposed, which validates a previous linear model for light winds (below 12 m s−1) and identifies a regime change in the noise generation mechanism at higher wind speeds. This proposed model improves on previous work by extending the validated model range to strong winds of up to 21.5 m s−1. The acquisition, data processing, and calibration steps are described. Future applications for glider-based wind speed observations and the development of a global wind speed estimation model are discussed.

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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 the analysis to increase the statistics. None of the wind gust estimation methods shows best results at every skill score. The proposed hybrid method shows good balance between the probability of detection and the false alarm ratio estimates.

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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 (October 1, 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 order to retrieve wind speeds under tropical cyclone conditions. Experiments, undertaken on actual X-band TerraSAR-X (TS-X) SAR images of tropical cyclones (Typhoon Megi, Hurricane Sandy, and Hurricane Miriam) and using collocated WAVEWATCH-III (WW3) simulations, revealed the robustness of the proposed approach, which resulted in a root-mean-square error (RMSE) of 2.54 m s−1 when comparing the retrieved wind speeds with the values from products delivered by the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD). However, the applicability of the algorithm herein will be further confirmed at very strong storms.

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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, especially in the south and west coastal regions. In addition, nontyphoon factors, such as a topographical factor with atmospheric instability in a mountainous coastal region, can cause localized severe weather in the form of strong wind. By performing the weather research and forecasting (WRF) model simulation, an abrupt increase in wind speed up to 20 m·s−1 was reproduced under the condition of onshore prevailing winds heading toward a mountain ridge in a coastal region. Estimation of strong wind spatial distribution can help the region-to-region establishment of an action plan to prepare for damage caused by strong winds.

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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 (March 20, 2011): 211–17. http://dx.doi.org/10.6113/jpe.2011.11.2.211.

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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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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 (September 15, 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 height, was evaluated through wind speed extrapolation using the power law. The dominant wind direction was determined by plotting a polar chart based on a frequency distribution table prepared using both wind direction and wind speed data. The turbulence intensity of the wind was calculated using the turbulence intensity equation. The Weibull parameters were estimated using the maximum likelihood estimation method. The Weibull probability distribution was used to analyze wind speed distribution and power density. The extrapolated Weibull parameters were utilized to calculate wind power density at various heights. The accuracy of the wind regime distribution in Narok was assessed by employing the R2 technique. Results: The wind regime in Narok exhibited an average annual wind speed of 4.3 m/s and a mean wind power density of 126 W/m2. Analysis of diurnal wind speed variation revealed peak wind speeds around noon, with wind speeds exceeding the cut-in wind threshold (3 m/s) between 0430hrs and 2100hrs. March and October were identified as the windiest months, exhibiting the highest wind power densities, while June and December demonstrated the lowest values. Wind speed and, consequently, wind power density increased exponentially with height. The prevailing wind directions in Narok were primarily from the East, followed by the North and North West. The wind regime in Narok exhibited turbulence, as indicated by average turbulence intensities exceeding 0.25. The wind regime in Narok was accurately described by the Weibull distribution, with an approximation accuracy of 0.94 based on the R2 error. Conclusion: The wind regime in Narok is generally suitable for extracting wind power at heights above 15 m, regardless of the scale of the wind power extraction.

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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 (December 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 changes. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Wind speed distributions conditioned on wind direction are estimated using two statistical methods, i.e., a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resultant estimated distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the robustness of the future projections. In particular, this work extends the concept of internal variability in the climate mean to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model captures both historical wind speed and wind direction and their dependencies reasonably well over both inland and offshore locations. Under the RCP8.5 scenario, most of the studied locations show no significant changes in the mean wind speeds in both winter and summer, while the changes in the standard deviation and 95th quantile show some robust changes over certain locations in winter. Specifically, high wind speeds (95th quantile) conditioned on direction in winter are projected to decrease in the northwestern, Colorado, and northern Great Plains locations in our study. In summer, high wind speeds conditioned on direction over the southern Great Plains increase slightly, while high wind speeds conditioned on direction over offshore locations do not change much. The proposed conditional approach enables a combined characterization of the wind speed distributions conditioned on direction and wind direction distributions, which offers a flexible alternative that can provide additional insights for the joint assessment of speed and direction.

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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 (May 18, 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 blade and tower, the tower diameter, and the blade diameter are very critical design factors that should be considered to reduce the output power fluctuations of a wind turbine generator. To model realistic characteristics while considering the critical factors of a wind turbine system, a wind turbine model is implemented using a squirrel-cage induction motor. Since the wind speed is the most important factor in modeling the aerodynamics of wind turbine, an accurate measurement or estimation is essential to have a valid model. This paper estimates the average wind speed, instead of measuring, from the generator power and rotating speed and models the turbine’s aerodynamics, including tower shadow and wind shear components, without having to measure the wind speed at any height. The proposed algorithm overcomes the errors of measuring wind speed in single or multiple locations by estimating the wind speed with estimation error less than 2%.

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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 (December 4, 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 three methods, the former two exclude the availability rate and give the exact value for wind capacity addition. The third method is based on the availability rate and provides a little higher capacity value, indicating a clear correlation between availability and capacity value. Originality/value The methods that exclude the availability rate show consistent results. By including the availability rate, the third method predicts the inverse relation between the availability rate and the capacity value.

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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 (May 22, 2017): 1089–96. http://dx.doi.org/10.1007/s12555-016-0537-1.

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Hu, Y., K. Stamnes, M. Vaughan, J. Pelon, C. Weimer, D. Wu, M. Cisewski, et al. "Sea surface wind speed estimation from space-based lidar measurements." Atmospheric Chemistry and Physics 8, no. 13 (July 8, 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 larger than 7 m/s and 13.3 m/s, respectively. For wind speed less than 7 m/s, the slope variance is proportional to the square root of the wind speed, assuming a two dimensional isotropic Gaussian wave slope distribution. This slope variance – wind speed relation becomes linear if a one dimensional Gaussian wave slope distribution and linear slope variance – wind speed relation are assumed. Contributions from whitecaps and subsurface backscattering are effectively removed by using 532 nm lidar depolarization measurements. This new slope variance – wind speed relation is used to derive sea surface wind speed from CALIPSO single shot lidar measurements (70 m spot size), after correcting for atmospheric attenuation. The CALIPSO wind speed result agrees with the collocated AMSR-E wind speed, with 1.2 m/s rms error. Ocean surface with lowest atmospheric loading and moderate wind speed (7–9 m/s) is used as target for lidar calibration correction.

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Hu, Y., K. Stamnes, M. Vaughan, J. Pelon, C. Weimer, D. Wu, M. Cisewski, et al. "Sea surface wind speed estimation from space-based lidar measurements." Atmospheric Chemistry and Physics Discussions 8, no. 1 (February 12, 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 speed larger than 7 m/s and 13.3 m/s, respectively. For wind speed less than 7 m/s, the slope variance is proportional to the square root of the wind speed, assuming a two dimensional isotropic Gaussian wave slope distribution. This slope variance – wind speed relation becomes linear if a one dimensional Gaussian wave slope distribution is assumed. Contributions from whitecaps and subsurface backscattering are effectively removed by using 532 nm lidar depolarization measurements. This new slope variance – wind speed relation is used to derive sea surface wind speed from CALIPSO single shot lidar measurements (70 m spot size), after correcting for atmospheric attenuation. The CALIPSO wind speed result agrees with the collocated AMSR-E wind speed, with 1.2 m/s rms error.

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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 (October 10, 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 indicators used to assess estimation accuracy are the root mean square error (RMSE) and relative percentage error (RPE). Study results show that good accuracy in Weibull parameters and power density estimation is obtained with sampled wind speed data of 30% for Accra, 20% for Cotonou, 80% for Kano, 20% for Lomé, and 20% for Ouagadougou site. This study showed that for wind potential assessing at a site, wind speed data random sampling is sufficient to calculate wind power density. This is very useful in wind energy exploitation development.

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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 (November 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 obtained and then used for point and interval predictions of wind speed accordingly. After various experiments and comparisons on the real wind speed data at four wind observation stations of China, it is found that the EMD-LQR-KDE and EMD-QRNN-KDE generally have the best performance and robustness in both point and interval predictions. By taking conditional quantiles obtained by the EMD-QRNN-KDE model as the input, probability density functions (PDFs) of wind speed at different times are obtained by the KDE method, whose bandwidth is optimally determined according to the normal reference criterion. It is found that most actual wind speeds lie near the peak of predicted PDF curves, indicating that the probabilistic density prediction by EMD-QRNN-KDE is believable. Compared with the PDF curves of the 90% confidence level, the PDF curves of the 80% confidence level usually have narrower wind speed ranges and higher peak values. The PDF curves also vary with time. At some times, they might be biased, bimodal, or even multi-modal distributions. Based on the EMD-QRNN-KDE model, one can not only obtain the specific PDF curves of future wind speeds, but also understand the dynamic variation of density distributions with time. Compared with the traditional point and interval prediction models, the proposed QR-KDE models could acquire more information about the randomness and uncertainty of the actual wind speed, and thus provide more powerful support for the decision-making work.

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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 (April 25, 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 roughness uncertainty arising from differing wind-observation and turbine-prediction sites; this is done for the case of roughness bias as well as for the general case. For estimation of uncertainty in annual energy production (AEP), we also develop a generalized analytical turbine power curve, from which we derive a relation between mean wind speed and AEP. Following our developments, we provide guidance on approximate roughness uncertainty magnitudes to be expected in industry practice, and we also find that sites with larger background roughness incur relatively larger uncertainties.

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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, analyzing the dynamic SVG characteristics allowed us to establish a mathematical model. Then, the corresponding relationship between the sensitivity values of the parameters to be identified and the fluctuating wind speed was established, and low and high wind speed models were constructed. Finally, for accurate estimation considering wind speed fluctuation, the parameter initial values are obtained by combining the low wind speed and high wind speed model identification parameters, and we introduce the multimode hybrid estimation of the SVG parameters, providing a new method for accurately identifying the SVG model parameters. The simulation results of the parameter estimation demonstrate the accuracy and stability of the proposed method.

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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 the conventional PI controller, thereby improving the anti-interference and robustness of the system. The sliding mode observer (SMO) is used to estimate the rotor speed. While satisfying the conditions of Lyapunov’s inequality, the design of the SMO is discussed in detail. The wind speed estimation method proposed for evaluating permanent magnet semidirect drive wind turbine and its application performance in maximum power tracking. The simulation was carried out in MATLAB/Simulink; the simulation confirmed that the estimated wind speed under different wind conditions is accurate. Compared with the traditional PI control, the utilization rate of wind energy is increased by 2%, which can be used for the MPPT control of the wind energy conversion system.

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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 (April 13, 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 with Weibull probability distributions and consider wind speed data of time intervals corresponding to prediction horizons of 30, 25, 20, 15 and 10 days ahead. Second, we perform the prediction of a wind speed distribution with genetic programming using the parameters of the Weibull distribution and other relevant meteorological variables. Third, the estimation of wind power is obtained by integrating the forecasted wind velocity distribution into the wind turbine power curve. To demonstrate the feasibility of the proposed method, we present a case study for a location in Mexico with low wind speeds. Estimation results are promising when compared against real data, as shown by MAE and MAPE forecasting metrics.

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Li, Huang, Angui Li, Linhua Zhang, Yicun Hou, Changqing Yang, Lu Chen, and Na Lu. "Estimation of Wind Speed Based on Schlieren Machine Vision System Inspired by Greenhouse Top Vent." Sensors 23, no. 15 (August 3, 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 with feature points is used to match the changes in air flow images in adjacent frames to estimate the velocity from pixel change. Through experiments, this method is suitable for the speed estimation of turbulent or disturbed fluid images. When the supply air speed remains constant, the method in this article obtains 760 sets of effective feature matching point groups from 150 frames of video, and approximately 500 sets of effective feature matching point groups are within 0.1 difference of the theoretical dimensionless speed. Under the supply conditions of high-frequency wind speed changes and compared with the digital signal of fan speed and data from wind speed sensors, the trend of wind speed changes is basically in line with the actual changes. The estimation error of wind speed is basically within 10%, except when the wind speed supply suddenly stops or the wind speed is 0 m/s. This method involves the ability to estimate the wind speed of air mixing with different densities, but further research is still needed in terms of statistical methods and experimental equipment.

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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 (September 7, 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 constant speed, power, and torque control in the high wind speed region. The designed control laws utilize a Luenberger observer for the estimation of the aerodynamic torque and a shallow neural network for wind speed estimation. The effectiveness of the proposed method was verified through tests in a laboratory setup. Moreover, a comparison with other solutions from the literature allowed us to better evaluate the performances achieved and to highlight the originality of the proposed control scheme.

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Journal articles on the topic 'Wind Speed Estimation'

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