Relevant bibliographies by topics / Wind Speed Estimation

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Academic literature on the topic 'Wind Speed Estimation'

Author: Grafiati
Published: 6 September 2023
Last updated: 7 July 2024

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Journal articles on the topic "Wind Speed Estimation"

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 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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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 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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3

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 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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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 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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7

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 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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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 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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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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