Добірка наукової літератури з теми "Wind Speed Estimation"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Wind Speed Estimation".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Wind Speed Estimation"
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.
Повний текст джерелаNaba, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаØstergaard, 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.
Повний текст джерелаMohandes, 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.
Повний текст джерелаBHARGAVA, 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.
Повний текст джерелаChiodo, 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.
Повний текст джерелаBingöl, 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.
Повний текст джерелаLi, 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.
Повний текст джерелаBarambones, 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.
Повний текст джерелаДисертації з теми "Wind Speed Estimation"
Piper, Benjamin. "SODAR comparison methods for compatible wind speed estimation." Thesis, University of Salford, 2011. http://usir.salford.ac.uk/16501/.
Повний текст джерелаSimley, 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.
Повний текст джерелаWind 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.
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.
Повний текст джерелаThis 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
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.
Повний текст джерелаTsang, 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.
Повний текст джерелаNielsen, Mark A. "Parameter Estimation for the Two-Parameter Weibull Distribution." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2509.
Повний текст джерелаMiguel, 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/.
Повний текст джерелаDriven 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.
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.
Повний текст джерелаHaas, 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.
Повний текст джерелаPradhan, P. P. "Wind speed estimation using neural networks." Thesis, 2014. http://ethesis.nitrkl.ac.in/5637/1/E-70.pdf.
Повний текст джерелаКниги з теми "Wind Speed Estimation"
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.
Знайти повний текст джерелаLottman, B. Evaluation of the MV (CAPON) coherent Doppler lidar velocity estimator. MSFC, Ala: National Aeronautics and Space Administration, Marshall Space Flight Center, 1997.
Знайти повний текст джерелаEvaluation 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.
Знайти повний текст джерелаЧастини книг з теми "Wind Speed Estimation"
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.
Повний текст джерелаKasperski, 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.
Повний текст джерелаYou, 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.
Повний текст джерелаGupta, 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.
Повний текст джерелаLiu, 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.
Повний текст джерелаCornejo-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.
Повний текст джерелаLikso, 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.
Повний текст джерелаEu, 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.
Повний текст джерелаDeligiorgi, 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.
Повний текст джерелаXie, 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.
Повний текст джерелаТези доповідей конференцій з теми "Wind Speed Estimation"
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.
Повний текст джерелаVillanueva, 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.
Повний текст джерелаChiodo, 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.
Повний текст джерелаChiodo, 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.
Повний текст джерелаGroch, 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.
Повний текст джерелаChowdhury, 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.
Повний текст джерелаPetrich, 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.
Повний текст джерелаChiodo, 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.
Повний текст джерелаMajdoub, 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.
Повний текст джерелаQu, 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.
Повний текст джерелаЗвіти організацій з теми "Wind Speed Estimation"
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.
Повний текст джерелаMeidani, 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.
Повний текст джерелаWenren, 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.
Повний текст джерелаDowning, 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.
Повний текст джерелаClark, 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.
Повний текст джерела