Academic literature on the topic 'Wind farm estimation'
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Journal articles on the topic "Wind farm estimation":
Celeska, Maja. "EQUIVALENT WIND FARM POWER CURVE ESTIMATION." Journal of Electrical Engineering and Information Technologies 2, no. 2 (2017): 105–11. http://dx.doi.org/10.51466/jeeit172105c.
Celeska, Maja. "EQUIVALENT WIND FARM POWER CURVE ESTIMATION." Journal of Electrical Engineering and Information Technologies 2, no. 2 (2017): 105–11. http://dx.doi.org/10.51466/jeeit172105c.
Annoni, Jennifer, Christopher Bay, Kathryn Johnson, Emiliano Dall'Anese, Eliot Quon, Travis Kemper, and Paul Fleming. "Wind direction estimation using SCADA data with consensus-based optimization." Wind Energy Science 4, no. 2 (June 20, 2019): 355–68. http://dx.doi.org/10.5194/wes-4-355-2019.
ARINAGA, Shinji, Masaaki SHIBATA, Shigeto HIRAI, Toshiya NANAHARA, Takamitsu SATO, and Koji YAMAGUCHI. "Estimation of Fluctuating Output in Wind Farm." Proceedings of the JSME annual meeting 2004.3 (2004): 293–94. http://dx.doi.org/10.1299/jsmemecjo.2004.3.0_293.
Becker, Marcus, Dries Allaerts, and Jan-Willem van Wingerden. "Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn." Energies 15, no. 22 (November 16, 2022): 8589. http://dx.doi.org/10.3390/en15228589.
Meglic, Antun, and Ranko Goic. "Impact of Time Resolution on Curtailment Losses in Hybrid Wind-Solar PV Plants." Energies 15, no. 16 (August 17, 2022): 5968. http://dx.doi.org/10.3390/en15165968.
TSUCHIYA, Manabu, Yukinari FUKUMOTO, and Takeshi ISHIHARA. "The Wind Observation and Energy Production Estimation for Offshore Wind Farm." Wind Engineers, JAWE 2008, no. 115 (2008): 119–22. http://dx.doi.org/10.5359/jawe.2008.119.
S, Fredy H. Martínez, César A. Hernández S, and Fernando Martínez S. "Multivariate Wind Speed Forecasting with LSTMs for Wind Farm Performance Estimation." International Journal of Engineering and Technology 10, no. 6 (December 31, 2018): 1626–32. http://dx.doi.org/10.21817/ijet/2018/v10i6/181006025.
Petkovic, Dalibor. "Estimation of wind farm efficiency by ANFIS strategy." Godisnjak Pedagoskog fakulteta u Vranju, no. 7 (2016): 91–105. http://dx.doi.org/10.5937/gufv1607091p.
Farrell, W., T. Herges, D. Maniaci, and K. Brown. "Wake state estimation of downwind turbines using recurrent neural networks for inverse dynamics modelling." Journal of Physics: Conference Series 2265, no. 3 (May 1, 2022): 032094. http://dx.doi.org/10.1088/1742-6596/2265/3/032094.
Dissertations / Theses on the topic "Wind farm estimation":
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.
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
Hines, Paul. "WindSim Study of Hybrid Wind Farm in Complex Terrain." Thesis, Högskolan på Gotland, Institutionen för kultur, energi och miljö, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-216994.
Hellström, Erik. "Development of a model for estimation of wind farm production losses due to icing." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-207382.
Novanda, Happy. "Monitoring of power quality indices and assessment of signal distortions in wind farms." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/monitoring-of-power-quality-indices-and-assessment-of-signal-distortions-in-wind-farms(403a470c-279a-4b00-94dc-eaa2507dc579).html.
Nord, Erika. "Cost estimation of wind farms' internal grids." Thesis, KTH, Elektriska energisystem, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-47831.
Tücer, Renas. "INVESTIGATION OF POTENTIAL REASONS TO ACCOUNT FOR THE UNDERPERFORMANCE OF AN OPERATIONAL WIND FARM." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-299538.
Yu, Xi. "Modelling offshore wind farm operation and maintenance with view to estimating the benefits of condition monitoring." Thesis, University of Strathclyde, 2016. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27387.
Jones, Esther Lane. "Spatial ecology of marine top predators." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/12278.
Mendon?a, Ricardo Barros de. "Modelagem de usinas e?licas atrav?s de um processo de Markov e t?cnicas de confiabilidade para a estimativa anual da energia produzida." Universidade Federal do Rio Grande do Norte, 2009. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15314.
This study aims to use a computational model that considers the statistical characteristics of the wind and the reliability characteristics of a wind turbine, such as failure rates and repair, representing the wind farm by a Markov process to determine the estimated annual energy generated, and compare it with a real case. This model can also be used in reliability studies, and provides some performance indicators that will help in analyzing the feasibility of setting up a wind farm, once the power curve is known and the availability of wind speed measurements. To validate this model, simulations were done using the database of the wind farm of Macau PETROBRAS. The results were very close to the real, thereby confirming that the model successfully reproduced the behavior of all components involved. Finally, a comparison was made of the results presented by this model, with the result of estimated annual energy considering the modeling of the distribution wind by a statistical distribution of Weibull
Este trabalho tem por objetivo, utilizar um modelo computacional que considera as caracter?sticas estat?sticas do vento e as caracter?sticas de confiabilidade de uma turbina e?lica, tais como taxas de falha e de reparo, representando a usina e?lica por um processo de Markov, para determina??o da estimativa anual da energia gerada e compar?-la com um caso real. Este modelo tamb?m pode ser utilizado em estudos de confiabilidade, al?m de fornecer alguns indicadores de desempenho, que ajudar?o na an?lise de viabilidade de implanta??o de uma usina e?lica, uma vez conhecida a curva de pot?ncia do aerogerador e dispondo-se de medi??es anemom?tricas da velocidade do vento. Para a valida??o deste modelo, foram feitas simula??es utilizando o banco de dados da usina e?lica de Macau da PETROBRAS. Os resultados obtidos foram bem pr?ximos do real, confirmando, assim, que o modelo reproduziu com sucesso o comportamento de todos os componentes envolvidos. Finalmente, foi feita uma compara??o dos resultados apresentados por este modelo, com o resultado da energia anual estimada considerando a modelagem do comportamento do vento por uma distribui??o estat?stica de Weibull
阮詠修. "Estimation of Wind Energy and Maximum Power Generation of Offshore Wind Farms in Changhua Region Using Various MCP Methods." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/h385ey.
國立彰化師範大學
機電工程學系
106
Taiwan is densely populated and the redevelopment of the wind farm in the onshore area is limited. Compared to the onshore wind farm, the offshore wind farm is richer in resources and has not yet been developed. It has obtained a more reliable assessment of the wind energy in the western Taiwan sea area. Follow-up on the development of wind farms in the western coast of Taiwan is even more important. The geographical position of the Taiwan Strait is unique. According to a survey conducted by an international research institute, the average wind energy density in the coastal areas of Taiwan exceeds 750 W/m^2, especially wind speeds of over 7 meters per second, which are rare in the world. With an area of 2,300 square kilometers, Changhua Offshore Wind Farm has 4GW of huge wind capacity. Accounting for 56% of the total wind power generation in Taiwan. The investment and development of an offshore wind farm is in the tens of billions to 100 billion. The assessment of wind energy at the site is particularly important, and it will be a key to investment profitability. Offshore wind power generation in Taiwan is still in its infancy. At present, there is not much research on wind power in Changhua wind farm. This paper is different from satellite observation or numerical simulation of wind energy, using the Taipower company has just completed the construction of wind energy data collected by the Meteorological Observation Tower, using a variety of MCP (Measure correlate Predict) method, to regress to Changfeng offshoe wind farm ten years of wind The data can be used to analyze the wind energy of Changhua site more accurately and objectively and estimate the wind farm power generation.
Books on the topic "Wind farm estimation":
Suvire, Gastn Orlando, ed. Wind Farm - Technical Regulations, Potential Estimation and Siting Assessment. InTech, 2011. http://dx.doi.org/10.5772/673.
Book chapters on the topic "Wind farm estimation":
Glazunova, Anna, and Elena Aksaeva. "State Estimation of Grid-Connected Wind Farm." In Energy Ecosystems: Prospects and Challenges, 166–77. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-24820-7_15.
Peña-Sanchez, Y., M. Penalba, and V. Nava. "Faulty wind farm simulation: An estimation/control-oriented model." In Trends in Renewable Energies Offshore, 679–85. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003360773-76.
Legaz, Asier, Miroslav Zivanovic, Xabier Iriarte, Aitor Plaza, and Alfonso Carlosena. "Modal Frequency and Damping Estimation of Wind Turbines: Analysis of a Wind Farm." In Lecture Notes in Civil Engineering, 595–605. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61425-5_57.
Wang, W., A. Kamath, H. Bihs, and C. Pákozdi. "High-efficiency wind-farm-scale wave force estimation for preliminary design of offshore wind installations." In Trends in Renewable Energies Offshore, 699–706. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003360773-78.
Sobolewski, Robert Adam. "Implication of Availability of an Electrical System of a Wind Farm for the Farm’s Output Power Estimation." In Dependability Engineering and Complex Systems, 419–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39639-2_37.
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.
Marques, Joana, Luísa Rodrigues, Maria João Silva, Joana Santos, Regina Bispo, and Joana Bernardino. "Estimating Bird and Bat Fatality at Wind Farms: From Formula-Based Methods to Models to Assess Impact Significance." In Biodiversity and Wind Farms in Portugal, 151–204. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60351-3_7.
Rosa, Luís, Tiago Neves, Diana Vieira, and Miguel Mascarenhas. "Camera-Trapping Versus Conventional Methodology in the Assessment of Carcass Persistence for Fatality Estimation at Wind Farms." In Wind Energy and Wildlife Impacts, 165–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05520-2_11.
Coelho, Ricardo. "Power Curve Estimation of Wind Farms with Imprecise Data by Fuzzy Quadratic Programming." In Computational Intelligence Methodologies Applied to Sustainable Development Goals, 175–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97344-5_12.
"Wind Turbine Sound Power Estimation." In Wind Farm Noise: Measurement, Assessment, 157–79. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118826140.ch4.
Conference papers on the topic "Wind farm estimation":
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.
Littler, T., B. Fox, and D. Flynn. "Measurement-based estimation of wind farm inertia." In 2005 IEEE Russia Power Tech. IEEE, 2005. http://dx.doi.org/10.1109/ptc.2005.4524432.
Chiodo, E., and D. Lauria. "On-line estimation of wind farm transient stability." In 2009 International Conference on Clean Electrical Power (ICCEP). IEEE, 2009. http://dx.doi.org/10.1109/iccep.2009.5211968.
Xia, Yiqing, Yosuke Matsumoto, Iman Yousefi, Kazuyoshi Oouchi, Shunsuke Kaneko, Michio Nittouji, Kenji Fujii, and Kaho Machida. "Structural Load Estimation of Downstream Wind Turbines in an Offshore Wind Farm." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-80883.
Mifsud, Michael D., Robert N. Farrugia, and Tonio Sant. "Investigating the Influence of MCP Uncertainties on the Energy Storage Capacity Requirements for Offshore Windfarms." In ASME 2019 2nd International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/iowtc2019-7504.
Tizgui, Ijjou, Hassane Bouzahir, Fatima El Guezar, and Brahim Benaid. "Estimation of electricity production for a Moroccan wind farm." In 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA). IEEE, 2016. http://dx.doi.org/10.1109/icedsa.2016.7818555.
Li, Honglin, Cong Feng, and Jie Zhang. "A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-114762.
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.
Nath, Angshu Plavan, Santanu Paul, Zakir Hussain Rather, and Sadhan Mahapatra. "Estimation of Offshore Wind Farm Reliability Considering Wake Effect and Wind Turbine Failure." In 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). IEEE, 2019. http://dx.doi.org/10.1109/isgt-asia.2019.8880887.
Karami, Farzad, Yujie Zhang, Mario A. Rotea, Federico Bernardoni, and Stefano Leonardi. "Real-time Wind Direction Estimation using Machine Learning on Operational Wind Farm Data." In 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9683613.