Relevant bibliographies by topics / Wind Speed Estimation

Academic literature on the topic 'Wind Speed Estimation'

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Published: 6 September 2023
Last updated: 7 July 2024

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Journal articles on the topic "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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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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Dissertations / Theses on the topic "Wind Speed Estimation"

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Piper, Benjamin. "SODAR comparison methods for compatible wind speed estimation." Thesis, University of Salford, 2011. http://usir.salford.ac.uk/16501/.

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This thesis includes the results of a PhD study about methods to compare Sonic Detection And Ranging (SODAR) measurements to measurements from other instruments. The study focuses on theoretical analysis, the design of a transponder system for simulating winds and the measurement of the acoustic radiation patterns of SODARs. These methods are integrated to reduce uncertainty in SODAR measurements. Through theoretical analysis it is shown that the effective measurement volume of a range gate is 15% of a cone section based on the SODAR's Full Width Half Maximum (FWHM). Models of the beam pattern are used to calculate the ratio of air passing a turbine to that measured by a SODAR over 10 minutes with values of 3-5% found at 10ms-1. The model is used to find angles where significant Sound Pressure Levels (SPLs) occur close to a SODARs baffle giving the highest chance of fixed echoes. This is converted into an orientation guide for SODAR set-up. The design of a transponder system is detailed that aims to provide a calibration test of the processing applied by a SODAR. Testing has shown that the transponder can determine the Doppler shift equation used by a SODAR although further work is needed to make the system applicable to all SODARs. It is shown that anechoic measurements of single elements are useful for improving array models. Measurements of the FWHM and acoustic tilt angle can be achieved in the field using a tilt mechanism and a Sound Level Meter (SLM) on a 10m mast. The same mechanism can be used to calculate an effective tilt angle using the Bradley technique. It is proposed that these methods are integrated to calculate error slopes for the SODAR measurement with regards to a secondary location. It is shown that the slopes could be between 0 and 5% if the methods are fully realised and a Computational Fluid Dynamics (CFD) model is incorporated.
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Simley, Eric J. "Wind Speed Preview Measurement and Estimation for Feedforward Control of Wind Turbines." Thesis, University of Colorado at Boulder, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3721887.

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Wind turbines typically rely on feedback controllers to maximize power capture in below-rated conditions and regulate rotor speed during above-rated operation. However, measurements of the approaching wind provided by Light Detection and Ranging (lidar) can be used as part of a preview-based, or feedforward, control system in order to improve rotor speed regulation and reduce structural loads. But the effectiveness of preview-based control depends on how accurately lidar can measure the wind that will interact with the turbine.

In this thesis, lidar measurement error is determined using a statistical frequency-domain wind field model including wind evolution, or the change in turbulent wind speeds between the time they are measured and when they reach the turbine. Parameters of the National Renewable Energy Laboratory (NREL) 5-MW reference turbine model are used to determine measurement error for a hub-mounted circularly-scanning lidar scenario, based on commercially-available technology, designed to estimate rotor effective uniform and shear wind speed components. By combining the wind field model, lidar model, and turbine parameters, the optimal lidar scan radius and preview distance that yield the minimum mean square measurement error, as well as the resulting minimum achievable error, are found for a variety of wind conditions. With optimized scan scenarios, it is found that relatively low measurement error can be achieved, but the attainable measurement error largely depends on the wind conditions. In addition, the impact of the induction zone, the region upstream of the turbine where the approaching wind speeds are reduced, as well as turbine yaw error on measurement quality is analyzed.

In order to minimize the mean square measurement error, an optimal measurement prefilter is employed, which depends on statistics of the correlation between the preview measurements and the wind that interacts with the turbine. However, because the wind speeds encountered by the turbine are unknown, a Kalman filter-based wind speed estimator is developed that relies on turbine sensor outputs. Using simulated lidar measurements in conjunction with wind speed estimator outputs based on aeroelastic simulations of the NREL 5-MW turbine model, it is shown how the optimal prefilter can adapt to varying degrees of measurement quality.

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Bezerra, Rufino Ferreira Paiva Eduardo. "Wind Velocity Estimation for Wind Farms." Electronic Thesis or Diss., Université Paris sciences et lettres, 2023. http://www.theses.fr/2023UPSLM046.

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Cette thèse propose des algorithmes pour estimer la vitesse et la direction du vent pour des éoliennes et des parcs éoliens.Tout d'abord, nous proposons des méthodes basées sur les données pour estimer la vitesse effective du rotor (REWS) sans nécessiter la connaissance de certains paramètres physiques de l'éolienne, qui pourraient être inconnus de l'opérateur. Nous fournissons deux méthodes basées sur les données, l'une basée sur la régression par processus gaussien et l'autre combinant la régression par processus gaussien avec un observateur grand gain.Ensuite, en nous basant sur cette estimation locale de la REWS, au niveau d'une éolinenne, nous abordons la question de l'estimation du vent en écoulement libre au niveau du parc éolien.Nous commençons par nous concentrer sur l'estimation de la vitesse du vent, pour une direction du vent connue. Pour un parc éolien de géométrie simple, nous démontrons qu'une mesure locale de la vitesse perturbée par la présence des éoliennes peut être utilisée pour estimer la vitesse du vent en écoulement libre. Nous fondons notre méthodologie d'estimation sur une modélisation simplifiée de l'effet de sillage qui consiste en des équations aux dérivées partielles hyperboliques du premier ordre en cascade, et dont la vitesse de transport est la vitesse du vent en écoulement libre. Nous proposons d'utiliser une solution analytique de ces équations, impliquant des retards de transport, pour effectuer une estimation de la mesure locale et mettre à jour l'estimation de la vitesse du vent en écoulement libre. Nous démontrons formellement la convergence de cette estimation et illustrons numériquement l'efficacité de cette méthode.Enfin, nous passons à une configuration plus générale où à la fois la vitesse et la direction du vent en écoulement libre sont inconnues. Nous proposons d'utiliser une modélisation bidimensionelle du sillage et de nous appuyer sur une méthode basée sur l'optimisation. Le problème d'identification que nous formulons se révèle être particulièrement difficile en raison de l'apparition de retards de transport, mais nous montrons comment contourner cette difficulté en considérant une valeur moyenne de l'historique de la vitesse du vent en écoulement libre. Des résultats de simulation obtenus avec le simulateur FAST.Farm illustrent l'intérêt de la méthode proposée
This thesis designs algorithms to estimate the wind speed and direction for wind turbines and wind farms.First, we propose data-based methods to estimate the Rotor Effective Wind Speed (REWS) for a single turbine without prior knowledge of certain physical parameters of the turbine that might be unknown to an operator.We provide two data-based methods, based respectively on Gaussian Process Regression (GPR) and on an combination of GPR with high-gain observers.Second, grounding on this REWS estimation at the local level of one turbine, we address the question of estimating the free-flow wind at the level of a wind farm.We start by focusing on wind speed estimation, for a given known wind direction. For a wind farm with a simple geometry, we prove that a local speed measurement disturbed by the presence of the turbines can be used to estimate the free-flow wind speed. We ground our estimation methodology on a simplified wake model, which consists of first-order hyperbolic partial differential equations, the transport speed of which is the free-flow wind speed. We propose to use an analytical solution of these equations, involving transport delays, to perform an estimate of the local measurement and to update the free-flow wind speed estimate. We formally prove the convergence of this estimate and numerically illustrate the efficiency of this method.Finally, we move to a more general setup where both the free-flow wind speed and direction are unknown. We propose to use a two-dimensional wake model and to rely on an optimization-based method. This identification problem reveals to be particularly challenging due to the appearance of transport delays, but we illustrate how to circumvent this issue by considering an average value of the free flow wind speed history. Simulation results obtained with the simulator FAST.Farm illustrate the interest of the proposed method
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Tsang, Ho-on Frederick, and 曾可安. "Time variable parameter estimation on the wind speed air quality modelin Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31253283.

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Tsang, Ho-on Frederick. "Time variable parameter estimation on the wind speed air quality model in Hong Kong /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14723554.

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Nielsen, Mark A. "Parameter Estimation for the Two-Parameter Weibull Distribution." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2509.

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The Weibull distribution, an extreme value distribution, is frequently used to model survival, reliability, wind speed, and other data. One reason for this is its flexibility; it can mimic various distributions like the exponential or normal. The two-parameter Weibull has a shape (γ) and scale (β) parameter. Parameter estimation has been an ongoing search to find efficient, unbiased, and minimal variance estimators. Through data analysis and simulation studies, the following three methods of estimation will be discussed and compared: maximum likelihood estimation (MLE), method of moments estimation (MME), and median rank regression (MRR). The analysis of wind speed data from the TW Daniels Experimental Forest are used for this study to test the performance and flexibility of the Weibull distribution.
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Miguel, José Vítor Pereira. "A influência da duração da campanha de medição anemométrica na avaliação de recursos eólicos com base na aplicação de métodos MCP." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/106/106131/tde-18012017-144634/.

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Impulsionado pela mecânica de leilões de energia, o aproveitamento energético de recursos eólicos no Brasil atravessa um momento de expansão em participação na matriz de energia elétrica nacional. Não obstante, o desempenho da geração dos parques eólicos que estão em operação foi monitorado e apresentou, em média, resultados aquém daquilo que fora confiado ao Sistema Interligado Nacional, revelando que as estimativas de geração projetadas e declaradas por alguns dos projetos vencedores dos processos licitatórios podem ter sido supervalorizadas. Tal cenário provocou a exigência de medidas mais conservadoras para participação nos leilões de energia, como a já vigente adoção do P90 no cálculo da Garantia Física e o aumento da duração da campanha de medição anemométrica, a entrar em rigor a partir de 2017. Sendo o vento uma variável estocástica, existem incertezas intrínsecas à Avaliação de Recursos Eólicos que influenciam no processo de estimação da geração por um parque eólico e que devem, desta forma, ser identificadas, quantificadas e reduzidas, na medida do possível. Nesse sentido, este trabalho estuda a influência da duração da campanha de medição anemométrica na Avaliação de Recursos Eólicos com base na aplicação do método MCP ferramenta imprescindível no processo de caracterização do regime eólico no longo prazo com vistas para aprimorar a exatidão das previsões de geração pela fonte eólica. Para tanto, foram utilizadas quatro bases de dados contendo séries temporais de velocidade e direção do vento referentes a uma região de interesse. Inicialmente, nove diferentes métodos MCP foram testados e comparados, sendo que o método Vertical Slice aplicado com auxílio do software Windographer destacou-se dos demais e mostrou-se mais aderente aos dados utilizados conforme as métricas de Erro Absoluto Médio e Raiz Quadrada do Erro Quadrático Médio. Posteriormente, as bases de dados foram configuradas para simular campanhas de medição anemométricas com durações que variavam de 2 a 6 anos, de modo a avaliar o comportamento da incerteza relativa à caracterização histórica de recursos eólicos e analisar em que medida esta incerteza impacta no cálculo da estimativa de geração de eletricidade por um conjunto de aerogeradores hipoteticamente dispostos naquele local de interesse. Foi possível verificar que, para os dados e casos analisados, à medida que se aumentou a duração da campanha de medição anemométrica, a incerteza da caracterização histórica de recursos eólicos sofreu queda significativa; determinando, por conseguinte, redução da incerteza total que permeia a geração eólica. Ademais, a quantidade de energia estimada para o parque eólico hipotético exemplificado também decresceu, permitindo melhora na acurácia da previsão de geração e beneficiando a confiabilidade da fonte eólica no sistema elétrico brasileiro.
Driven by the energy auctions system, the energetic harnessing of wind resource in Brazil is now going through a phase of expansion in participation in the national electric energy mix. Nevertheless, the performance of power generation of in-operation wind farms was monitored and the results proved to be, on average, below what was initially entrusted to the National Grid System, indicating that the energy production estimations projected by some energy auctions winners could have been overestimated. This scenario has caused the requirements for participating in the energy auctions to be more conservative, with measures such as the adoption of the P90 on the calculation of the physical guarantee and the increase of the wind measurement campaigns time span the latter to be enforced as of 2017. The wind is a stochastic resource, hence there are uncertainties intrinsic to the Wind Resource Assessment that influence a wind farms power generation estimation and that need to be properly identified, quantified and reduced, as far as possible. In this respect, the influence of a wind measurement campaigns time span on the Wind Resource Assessment based on MCP methods an important tool in the process of characterizing the long-term wind regime was studied in order to detect the potential of enhancing the accuracy of wind power generation forecasts. For this purpose, four databases containing time series of wind speed and direction belonging to a target site were used. Firstly, nine different MCP methods were tested and compared, of which the Vertical Slice method implemented on the software Windographer outperformed all the others according to the Mean Absolute Error and Root Mean Square Error metrics. Subsequently, the databases were set to simulate campaigns with time spans varying from 2 to 6 years, in such a way to evaluate the behavior of the uncertainty in the long-term wind speed and to analyze how this uncertainty impacts the calculation of the energy production estimation of an array of wind turbines hypothetically placed on that target site. From the analyzed data and cases, it was verified that, as the wind measurement campaigns time span was increased, the uncertainty in the long-term wind speed was significantly diminished, thereby reducing the overall uncertainty that pervades the wind power harnessing. Furthermore, the energy production estimation of the exemplified hypothetical wind farm also decreased, allowing an improvement on the accuracy of the energy generation prediction and benefiting the reliability of wind power in the Brazilian electric system.
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Esmaili, Gholamreza. "Application of advanced power electronics in renewable energy sourcesand hybrid generating systems." The Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1141850833.

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Haas, Rabea [Verfasser], Michael [Akademischer Betreuer] Kerschgens, and Andreas [Akademischer Betreuer] Fink. "Estimation of regional-scale wind and gust speeds for Europe by statistical-dynamical downscaling / Rabea Haas. Gutachter: Michael Kerschgens ; Andreas Fink." Köln : Universitäts- und Stadtbibliothek Köln, 2014. http://d-nb.info/1071651358/34.

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Pradhan, P. P. "Wind speed estimation using neural networks." Thesis, 2014. http://ethesis.nitrkl.ac.in/5637/1/E-70.pdf.

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In electrical power system, prediction of Renewable energy sources has become essential for designing a control strategy to manage the electricity on the grid. To help the system operators for integration of wind power system to the existing power system, wind speed and power prediction is essential.Basically neural network is aimed for short-term forecasting problems as it is capable to learn non-linear relationship between inputs and outputs by a non-statistical approach and don’t require any predefined mathematical model. This thesis investigates the effectiveness of recurrent wavelet neural network (RWNN) and artificial wavelet neural network (AWNN) dynamics for wind speed forecasting. We evaluate the RWNN and AWNN against multilayer feed-forward neural network. The RWNN and AWNN are trained using back propagation gradient descent algorithm. The experimental results show that the performance of RWNN and AWNN approaches outperforms the multilayer feed-forward neural network. All the three models use Hourly averaged time series data (2982 numbers of samples) for wind speed collected from the National Renewable Energy Laboratory (NREL).
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Books on the topic "Wind Speed Estimation"

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Yum, Sang Guk. Extreme Storm Surge Return Period Prediction Using Tidal Gauge Data and Estimation of Damage to Structures from Storm-Induced Wind Speed in South Korea. [New York, N.Y.?]: [publisher not identified], 2019.

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Lottman, B. Evaluation of the MV (CAPON) coherent Doppler lidar velocity estimator. MSFC, Ala: National Aeronautics and Space Administration, Marshall Space Flight Center, 1997.

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Evaluation of the MV (CAPON) coherent Doppler lidar velocity estimator: Under grant NAG8-253. Marshall Space Flight Center, Ala: National Aeronautics and Space Administration, [George C. Marshall Space Flight Center], 1997.

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Book chapters on the topic "Wind Speed Estimation"

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Yu, Kegen. "Sea Surface Wind Speed Estimation." In Navigation: Science and Technology, 125–62. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0411-9_6.

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Kasperski, Michael. "Estimation of the Design Wind Speed." In Advanced Structural Wind Engineering, 27–58. Tokyo: Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54337-4_2.

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You, Xia, Bo Zhou, Qingxi Zeng, Yajie Lin, and Honghao Guo. "Wind Speed Estimation Based MPPT for WPGS." In The proceedings of the 10th Frontier Academic Forum of Electrical Engineering (FAFEE2022), 1057–65. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3408-9_93.

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Gupta, Sonali, Manika Manwal, and Vikas Tomer. "Estimation of Wind Speed Using Machine Learning Algorithms." In Advances in Intelligent Systems and Computing, 41–48. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1740-9_5.

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Liu, Zhigang. "Wave Motion Characteristic of Contact Line Considering Wind." In Detection and Estimation Research of High-speed Railway Catenary, 55–75. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2753-6_3.

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Cornejo-Bueno, L., J. Acevedo-Rodríguez, L. Prieto, and S. Salcedo-Sanz. "A Hybrid Ensemble of Heterogeneous Regressors for Wind Speed Estimation in Wind Farms." In Intelligent Distributed Computing XII, 97–106. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99626-4_9.

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Likso, Tanja. "Estimation of Wind Speed in the Suburban Atmospheric Surface Layer." In Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment, 843–47. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-18663-4_130.

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Eu, Kok Seng, Wei Zheng Chia, and Kian Meng Yap. "Wind Direction and Speed Estimation for Quadrotor Based Gas Tracking Robot." In Mobile and Wireless Technologies 2017, 645–52. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5281-1_71.

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Deligiorgi, Despina, Kostas Philippopoulos, and Georgios Kouroupetroglou. "Artificial Neural Network Based Methodologies for the Estimation of Wind Speed." In Assessment and Simulation Tools for Sustainable Energy Systems, 247–66. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5143-2_12.

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Xie, Xiqiang. "Study on a Rotor Speed Estimation Algorithm of PMSG Wind Power System." In Advances in Intelligent Systems and Computing, 450–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43306-2_64.

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Conference papers on the topic "Wind Speed Estimation"

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Hafidi, Ghizlane, and Jonathan Chauvin. "Wind speed estimation for wind turbine control." In 2012 IEEE International Conference on Control Applications (CCA). IEEE, 2012. http://dx.doi.org/10.1109/cca.2012.6402654.

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Villanueva, J., and L. Alvarez-Icaza. "Wind Turbine Torque and Wind Speed Estimation." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6063.

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An adaptive-observer to estimate both the parameters of a new model of the mechanical torque in a wind turbine and the wind velocity is proposed. The model is based in a relative velocity between the wind speed and blades of turbine. Results of torque estimation are compared with those obtained from a heuristic model, commonly used in the literature, showing very good agreement.
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Chiodo, E. "Wind speed extreme quantiles estimation." In 2013 International Conference on Clean Electrical Power (ICCEP). IEEE, 2013. http://dx.doi.org/10.1109/iccep.2013.6586944.

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Chiodo, E., D. Lauria, and C. Pisani. "Wind farm production estimation under multivariate wind speed distribution." In 2013 International Conference on Clean Electrical Power (ICCEP). IEEE, 2013. http://dx.doi.org/10.1109/iccep.2013.6586940.

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Groch, Matthew, and Hendrik J. Vermeulen. "Multi-Point Locational Wind Speed Estimation from Meso-Scale Wind Speeds for Wind Farm Applications." In 2019 9th International Conference on Power and Energy Systems (ICPES). IEEE, 2019. http://dx.doi.org/10.1109/icpes47639.2019.9105445.

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Chowdhury, Srinjoy Nag, and Saniya Dhawan. "Statistical estimation for fitting wind speed distribution." In 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS). IEEE, 2016. http://dx.doi.org/10.1109/iceets.2016.7582895.

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Petrich, Jan, and Kamesh Subbarao. "On-Board Wind Speed Estimation for UAVs." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-6223.

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Chiodo, E., D. Lauria, and C. Pisani. "Bayes Estimation of Wind Speed Extreme Values." In 3rd Renewable Power Generation Conference (RPG 2014). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.0911.

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Majdoub, Youssef, Ahmed Abbou, and Mohamed Akherraz. "Variable speed control of DFIG-wind turbine with wind estimation." In 2014 International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2014. http://dx.doi.org/10.1109/irsec.2014.7059879.

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Qu, Xiuli, and Jing Shi. "Characterizing Wind Speed and Air Density for Wind Energy Estimation." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-13059.

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Wind energy is the fastest growing renewable energy source in the past decade. To estimate the wind energy potential for a specific site, the long-term wind data need to be analyzed and accurately modeled. Wind speed and air density are the two key parameters for wind energy potential calculation, and their characteristics determine the long-term wind energy estimation. In this paper, we analyze the wind speed and air density data obtained from two observation sites in North Dakota and Colorado, and the variations of wind speed and air density in long term are demonstrated. We obtain univariate statistical distributions for the two parameters respectively. Excellent fitting performance can be achieved for wind speed for both sites using conventional univariate probability distribution functions, but fitting air density distribution for the North Dakota site appears to be less accurate. Furthermore, we adopt Farlie-Gumbel-Morgenstern approach to construct joint bivariate distributions to describe wind speed and air density simultaneously. Overall, satisfactory goodness-of-fit values are achieved with the joint distribution models, but the fitting performance is slightly worse compared with the univariate distributions. Further research is needed to improve air density distribution model and the joint bivariate distribution model for wind speed and air density.
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Reports on the topic "Wind Speed Estimation"

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Lyzenga, David R. Estimation of Ocean Surface Wind Speed and Direction From Polarimetric Radiometry Data. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533831.

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Meidani, Hadi, and Amir Kazemi. Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons. Illinois Center for Transportation, November 2021. http://dx.doi.org/10.36501/0197-9191/21-036.

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Fuel-consumption reduction in the truck industry is significantly beneficial to both energy economy and the environment. Although estimation of drag forces is required to quantify fuel consumption of trucks, computational fluid dynamics (CFD) to meet this need is expensive. Data-driven surrogate models are developed to mitigate this concern and are promising for capturing the dynamics of large systems such as truck platoons. In this work, we aim to develop a surrogate-based fluid dynamics model that can be used to optimize the configuration of trucks in a robust way, considering various uncertainties such as random truck geometries, variable truck speed, random wind direction, and wind magnitude. Once trained, such a surrogate-based model can be readily employed for platoon-routing problems or the study of pavement performance.
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Wenren, Yonghu, Luke Allen, and Robert Haehnel. SAGE-PEDD user manual. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/44960.

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SAGE-PEDD is a computational model for estimating snowdrift shapes around buildings. The main inputs to the model are wind speed, wind direction, building geometry and initial ground or snow-surface topography. Though developed mainly for predicting snowdrift shapes, it has the flexibility to accept other soil types, though this manual addresses snow only. This manual provides detailed information for set up, running, and viewing the output of a SAGE-PEDD simulation.
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Downing, W. Logan, Howell Li, William T. Morgan, Cassandra McKee, and Darcy M. Bullock. Using Probe Data Analytics for Assessing Freeway Speed Reductions during Rain Events. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317350.

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Rain impacts roadways such as wet pavement, standing water, decreased visibility, and wind gusts and can lead to hazardous driving conditions. This study investigates the use of high fidelity Doppler data at 1 km spatial and 2-minute temporal resolution in combination with commercial probe speed data on freeways. Segment-based space-mean speeds were used and drops in speeds during rainfall events of 5.5 mm/hour or greater over a one-month period on a section of four to six-lane interstate were assessed. Speed reductions were evaluated as a time series over a 1-hour window with the rain data. Three interpolation methods for estimating rainfall rates were tested and seven metrics were developed for the analysis. The study found sharp drops in speed of more than 40 mph occurred at estimated rainfall rates of 30 mm/hour or greater, but the drops did not become more severe beyond this threshold. The average time of first detected rainfall to impacting speeds was 17 minutes. The bilinear method detected the greatest number of events during the 1-month period, with the most conservative rate of predicted rainfall. The range of rainfall intensities were estimated between 7.5 to 106 mm/hour for the 39 events. This range was much greater than the heavy rainfall categorization at 16 mm/hour in previous studies reported in the literature. The bilinear interpolation method for Doppler data is recommended because it detected the greatest number of events and had the longest rain duration and lowest estimated maximum rainfall out of three methods tested, suggesting the method balanced awareness of the weather conditions around the roadway with isolated, localized rain intensities.
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Clark, E. L. Error propagation equations and tables for estimating the uncertainty in high-speed wind tunnel test results. Office of Scientific and Technical Information (OSTI), August 1993. http://dx.doi.org/10.2172/10178382.

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