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Relevant bibliographies by topics / Wind Speed Estimation

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Journal articles

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 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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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 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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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 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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