Relevant bibliographies by topics / Wind energy

Academic literature on the topic 'Wind energy'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Wind energy"

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Nah, Do-Baek, Hyo-Soon Shin, and Duck-Joo Nah. "Offshore Wind Power, Review." Journal of Energy Engineering 20, no. 2 (June 30, 2011): 143–53. http://dx.doi.org/10.5855/energy.2011.20.2.143.

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Suska-Szczerbicka, Magdalena. "WIND ENERGY FINANCING TOOLS." Economics & Sociology 3, no. 1a (July 20, 2010): 141–60. http://dx.doi.org/10.14254/2071-789x.2010/3-1a/10.

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Matsyura, Alex, Kazimierz Jankowski, and Marina Matsyura. "BIRDS’ FLIGHT ENERGY PREDICTIONS AND APPLICATION TO RADAR-TRACKING STUDY." Biological Bulletin of Bogdan Chmelnitskiy Melitopol State Pedagogical University 3, no. 03 (October 28, 2013): 135. http://dx.doi.org/10.15421/20133_45.

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<p>In offered research, we propose to observe diurnal soaring birds to check, whether there the positions of birds in formations are such, that the wing tip interval and depth meet the predictions of aerodynamic theory for achievement of maximal conservation of energy or predictions of the hypothesis of communication. We also can estimate, whether adverse conditions of a wind influence the ability of birds to support formation. We can assume that windy conditions during flight might make precision flight more difficult by inducing both unpredictable bird and vortex positions. To this, we need to found change in wing-tip spacing variation with increasing wind speed, suggesting or rejecting that in high winds bird skeins maintained similar variation to that on calm days. The interrelation between variation of mean depth and wind speed should prove this hypothesis. Little is known about the importance of depth, but in high winds the vortex is likely to break up more rapidly and its location become unpredictable the further back a bird flies; therefore, a shift towards skeins with more regular depths at high wind speeds may compensate for the unpredictability of the vortex locations. Any significant relationship between the standard deviation of wing-tip spacing and wind speed suggests that wind has a major effect on optimal positioning.</p> <p>Results of proposed study will be used also as the auxiliary tool in radar research of bird migration, namely in research of flight features of soaring birds. It is extremely important to determine all pertinent characteristics of flock for model species, namely flocking birds.</p> <p><em>Kew words: birds, flock, radar, flight</em></p><p> </p>
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Gartman, Victoria, Kathrin Wichmann, Lea Bulling, María Elena Huesca-Pérez, and Johann Köppel. "Wind of Change or Wind of Challenges: Implementation factors regarding wind energy development, an international perspective." AIMS Energy 2, no. 4 (2014): 485–504. http://dx.doi.org/10.3934/energy.2014.4.485.

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Früh, Wolf-Gerrit. "From local wind energy resource to national wind power production." AIMS Energy 3, no. 1 (2015): 101–20. http://dx.doi.org/10.3934/energy.2015.1.101.

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N Akour, Salih, and Hani Omar Bataineh. "Design considerations of wind funnel concentrator for low wind speed regions." AIMS Energy 7, no. 6 (2019): 728–42. http://dx.doi.org/10.3934/energy.2019.6.728.

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Barthelmie, Rebecca, and Martin Kühn. "Wind Energy special issue: offshore wind energy." Wind Energy 10, no. 6 (2007): 587. http://dx.doi.org/10.1002/we.261.

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Musau, Stephen K., Kathrin Stahl, Kevin Volkmer, Nicholas Kaufmann, and Thomas H. Carolus. "A design and performance prediction method for small horizontal axis wind turbines and its application." AIMS Energy 9, no. 5 (2021): 1043–66. http://dx.doi.org/10.3934/energy.2021048.

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<abstract> <p>The paper deals with small wind turbines for grid-independent or small smart grid wind turbine systems. Not all small turbine manufacturers worldwide have access to the engineering capacity for designing an efficient turbine. The objective of this work is to provide an easy-to-handle integrated design and performance prediction method for wind turbines and to show exemplary applications.</p> <p>The underlying model for the design and performance prediction method is based on an advanced version of the well-established blade-element-momentum theory, encoded in MATLAB™. Results are (i) the full geometry of the aerodynamically profiled and twisted blades which are designed to yield maximum power output at a given wind speed and (ii) the non-dimensional performance characteristics of the turbine in terms of power, torque and thrust coefficient as a function of tip speed ratio. The non-dimensional performance characteristics are the basis for the dimensional characteristics and the synthesis of the rotor to the electric generator with its load.</p> <p>Two parametric studies illustrate typical outcomes of the design and performance prediction method: A variation of the design tip speed ratio and a variation of the number of blades. The predicted impact of those parameters on the non-dimensional performance characteristics agrees well with common knowledge and experience.</p> <p>Eventually, an interplay of various designed turbine rotors and the given drive train/battery charger is simulated. Criterions for selection of the rotor are the annual energy output, the rotor speed at design wind speed as well as high winds, and the axial thrust exerted on the rotor by the wind. The complete rotor/drive train//battery charger assembly is tested successfully in the University of Siegen wind tunnel.</p> </abstract>
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Wu, Weijian, Zhen Pan, Jiangtao Zhou, Yingting Wang, Jijie Ma, Jianping Li, Yili Hu, Jianming Wen, and Xiaolin Wang. "Wind-Speed-Adaptive Resonant Piezoelectric Energy Harvester for Offshore Wind Energy Collection." Sensors 24, no. 5 (February 20, 2024): 1371. http://dx.doi.org/10.3390/s24051371.

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This paper proposes a wind-speed-adaptive resonant piezoelectric energy harvester for offshore wind energy collection (A-PEH). The device incorporates a coil spring structure, which sets the maximum threshold of the output rotational frequency, allowing the A-PEH to maintain a stable output rotational frequency over a broader range of wind speeds. When the maximum output excitation frequency of the A-PEH falls within the sub-resonant range of the piezoelectric beam, the device becomes wind-speed-adaptive, enabling it to operate in a sub-resonant state over a wider range of wind speeds. Offshore winds exhibit an annual average speed exceeding 5.5 m/s with significant variability. Drawing from the characteristics of offshore winds, a prototype of the A-PEH was fabricated. The experimental findings reveal that in wind speed environments, the device has a startup wind speed of 4 m/s, and operates in a sub-resonant state when the wind speed exceeds 6 m/s. At this point, the A-PEH achieves a maximum open-circuit voltage of 40 V and an average power of 0.64 mW. The wind-speed-adaptive capability of the A-PEH enhances its ability to harness offshore wind energy, showcasing its potential applications in offshore wind environments.
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SUTHAR, BHARAT D. "Wind Energy Integration for DfIg Based Wind Turbine fault Ride-Through." Indian Journal of Applied Research 4, no. 5 (October 1, 2011): 216–20. http://dx.doi.org/10.15373/2249555x/may2014/64.

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Dissertations / Theses on the topic "Wind energy"

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Ndzukuma, Sibusiso. "Statistical tools for wind energy generation." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1020627.

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In this study we conduct wind resource assessment to evaluate the annual energy production of a wind turbine. To estimate energy production of a wind turbine over a period of time, the power characteristics of the wind turbine are integrated with the probabilities of the wind speed expected at a chosen site. The first data set was obtained from a wind farm in Denmark. We propose several probability density functions to model the distribution of the wind speed. We use techniques from nonlinear regression analysis to model the power curve of a wind turbine. The best fit distribution model is assessed by performing numeric goodness–of–fit measures and graphical analyses. Johnson’s bounded (SB) distribution provides the best fit model with the smallest Kolmogorov–Smirnov (K-S) test statistic . 15. The four parameter logistic nonlinear regression (4PL) model is determined to provide the best fit to the power curve data, according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The estimated annual energy yield is compared to the actual production of the wind turbine. Our models underestimate the actual energy production by a 1 difference. In Chapter Six we conduct data processing, analyses and comparison of wind speed distributions using a data set obtained from a measuring wind mast mounted in Humansdorp, Eastern Cape. The expected annual energy production is estimated by using the certified power curve as provided by the manufacturer of the wind turbine under study. The commonly used Weibull distribution is determined to provide the best fit distribution model to our selected models. The annual energy yield is estimated at 7.33 GWh, with a capacity factor of 41.8 percent.
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Золотова, Світлана Григорівна, Светлана Григорьевна Золотова, Svitlana Hryhorivna Zolotova, and O. V. Leunova. "Wind Energy Sources." Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/13443.

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Goff, Charles. "Wind energy cost reductions." CONNECT TO ELECTRONIC THESIS, 2006. http://hdl.handle.net/1961/3598.

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Sankaranarayanan, Vairamayil. "Maintenance – Wind Energy Production." Thesis, Mälardalens högskola, Innovation och produktrealisering, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-27620.

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This thesis investigates issues like maintenance problems, key factors, maintenance challenges, maintenance solutions and practical difficulties in wind energy. In this case, surveys and interviews have been taken from several companies and maintenance experts, to find most prevailing problems and problem-solving methods since last few years. It helps to show, how the energy maintenance has been developed in past few years. Also it analyses the impact of fourth generation maintenance in wind energy production. From research questions, key factors involved in wind energy maintenance provides us with valuable suggestions to develop the maintenance methods in future vision.
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Sun, Huihui. "Miniature wind energy harvesters." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/416874/.

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Energy harvesting is a very attractive technique for a wide variety of self-powered microsystems such as wireless sensors. Airflow induced oscillations have been used as an attractive technique for energy harvesting because of its potential capacity for generating electrical power. The aero-elastic instability phenomenon such as flutter has been suggested especially for small scale energy harvesters. This paper describes the design, simulation, fabrication, measurement and performance of a miniature wind energy harvester based on a flapping cantilevered beam. The wind generator is based on oscillations of a cantilever that faces the direction of the airflow. The oscillation is amplified by interactions between an aerofoil attached on the cantilever and a bluff body placed in front of the aerofoil. To achieve the optimum design of the harvester, both computational simulations and experiments have been carried out to investigate the structure. Simulation is achieved with ANSYS to optimise the structure and predict the power generation for practical design. Both piezoelectric materials and electromagnetic transducers are used for the generator and tested. Three prototypes with the same volume of 37.5 cm3 are fabricated and tested through two aspects of the performance namely the threshold wind speed for operation and the output power. Wind tunnel test results are presented to determine the optimum structure and to characterize the performance of the harvesters. The piezoelectric generator is fabricated by thick-film screen printing technique. The optimized device finally achieved a working wind speed range from 2 m/s to 8 m/s. The power output was ranging from 0.35 to 3.6 μW and the open-circuit output voltage was from 0.6V to 1.9V. The first electromagnetic harvester had a working wind speed range from 1.35 m/s to 6 m/s with a maximum power output of 29.8 μW and a voltage of 293 mV. While for the second generator, the wind speed for operation is form 1.5 m/s to 6.5 m/s. The output power is ranging from 8.9 μW to 41 μW and the output voltage is up to 171 mV. Results verified the harvester can effectively convert wind energy into large amplitude mechanical vibration without strict frequency matching constraints.
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Wang, Jialin. "Building integrated wind energy." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/building-integrated-wind-energy(81978798-e68a-4189-87b0-4159b280b6e9).html.

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In considering methods of reducing the emission of carbon dioxide; there is a growing interest for use of wind power at domestic building in U.K. But the technology of wind turbines development in building environment is more complicated than in open areas. Small wind turbines in suburban areas have been reported as having unsatisfactory energy output, but it is not clear whether this is due to insufficient wind resource or low turbine efficiency. The aim of this research is to discover whether the wind resource in suburban areas is large enough for small wind turbines to produce a useful energy output.Historical wind data and manufacturers' turbine characteristics were used to estimate the hourly wind speed and energy output for different U.K. cities, terrain zones and turbines. It was found that for turbines at 10 m height in suburban areas and depending on city, the annual wind energy conversion efficiency ranged from about 20 to 40%, while the number of turbines required to produce the annual average electricity consumption of a UK dwelling ranged from about 6 for the smallest turbine (5.3 m² rotor area) to about 1 for the largest (35.26 m² rotor area).This analysis was based on average conditions, but the wind speed near buildings can vary considerably from one point to another. In order to predict the performance of wind turbines more accurately, the atmospheric boundary layer (ABL) of suburban areas was simulated in both CFD and wind tunnel models, and models of groups of semi-detached and terraced houses were set in this ABL. It was found that at 10 m height in the area of the houses, the turbulence intensity was too high for satisfactory operation of wind turbines (19 to 35%) while the mean velocity at different points ranged from 86 to 108% of the 10m reference velocity. At 30m height the turbulence intensity was satisfactory (less than 19 %), while the mean velocity ranged from 92 to 103 % of the 30 m reference velocity. It is concluded that for wind turbines in suburban areas, at 10 m height the wind speed is too low and the turbulence is too high for satisfactory performance, while at 30 m height the wind speed is much higher and the turbulence is low enough.
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Shelley, Dena L. "A wind energy landscape : the Searsburg Wind Park." Virtual Press, 2008. http://liblink.bsu.edu/uhtbin/catkey/1390311.

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Wind Energy facilities are becoming a more common occurrence among the U.S. landscape. The shift to renewable from non-renewable energy sources is an important agenda item for energy policy in the 21st century. Unlike other forms of energy, the unique visual aspects of wind energy provide opportunities to engage with and actually view the process of energy production. The sculptural element of turbines and their placement in highly visible areas, such as mountain ridges, provides opportunities of environmental interpretation and public interaction. Although existing security and safety precautions in the U.S. do not allow public use of these facilities, the integration of turbines into public places is becoming more common in other parts of the world. This creative project focuses on developing dynamic and unique cultural places that also serve as education spaces to celebrate wind and wind energy. Environmental art installations among the wind turbines serve as human-scaled interpretational guides to create meaningful, learning experiences between the user, the wind and the landscape.This project highlights the existing eleven-turbine (6MW) facility in the town of Searsburg in southern Vermont. This project includes inventory, analysis and site design of an existing wind facility. The methodology includes using GIS data and existing sight line data, as well as significant and environmental cultural points. Finally, general guidelines are included as a design foundation for other wind energy facilities.
Department of Landscape Architecture
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Moor, Gary Duncan. "Optimization of wind energy transfer using wind turbines." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53542.

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Thesis (MScIng)--Stellenbosch University, 2003.
ENGLISH ABSTRACT: The effect of topography and terrain on wind is examined in order to ensure that the wind turbine positioning will encourage a greater availability of wind energy to it. Maximum power point tracking methods are presented whereby the loading on the wind turbine is controlled to ensure that the maximum available energy from the wind is captured. The wind turbine system is modelled and used in simulations to evaluate the three proposed maximum power point trackers, named anemometer control, calculation control and constant step control for the purpose of this thesis. An additional analog system is also created whereby the complete wind turbine system is able to be simulated. An inverter is used to replicate the generator and the loading is controlled using an active rectifier since this will be used on the practical system. The results from the simulations and analog system are presented whereby one of the trackers is shown to be inadequate and the other two trackers are shown to be close to ideal. The appeal of the calculation method is in the redundancy of an anemometer making it attractive to less expensive, small-scale systems.
AFRIKAANSE OPSOMMING: Die invloed van die topografie en die terrein op die dinamika van wind word ondersoek om sodoende te verseker dat die posisionering van wind turbienes 'n beter effektiwiteit van wind energie oordrag sal bewerkstellig. Maksimum drywingspunt volger metodes word bespreek sodat die lading op die wind turbiene beheer kan word om sodoende te verseker dat die maksimum wind energie oorgedra kan word. Die wind turbiene stelsel word gemodeleer en geimplimenteer om die drie voorgestelde maksimum drywingspount volgers te evalueer, naamlik windspoedbeheer, berekening-beheer en konstantestap-beheer vir die doeleindes van hierdie tesis. 'n Adissionele analoog stelsel is ontwerp waarmee die volledige wind turbiene stelsel gesimuleer kan word. 'n Omsetter word gebruik om die generator na te boots en die belading word beheer deur middel van 'n aktiewe gelykrigter soos gebruik 'n praktese stelsel. Resultate van die simulasies en die analog stelsel is verskaf om te bewys dat een van die volg-metodes onvoldoende volging bewerkstellig, en die ander twee nabyaan ideale volging bewerkstellig. Dit is getoon dat die berekening metode meer aantreklik is vir kleinskaal stelsels, aangesien 'n windspoedsensor onnodig is.
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Weekes, Shemaiah Matthias. "Small-scale wind energy : methods for wind resource assessment." Thesis, University of Leeds, 2014. http://etheses.whiterose.ac.uk/6413/.

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Small-scale wind energy is a renewable energy technology with exciting prospects in a low carbon energy future. However, in order for the technology to be fully utilized, techniques capable of predicting the wind energy resource quickly, cheaply and accurately are urgently required. Specifically, the direct measurement approaches used in the large-scale wind industry are often not financially or practically viable in the case of small-scale installations. The subject of this thesis is the development of low-cost, indirect methods for predicting the wind resource using, (i) analytical models based on boundary layer meteorology and (ii) data-driven techniques based on measure-correlate-predict (MCP). The approaches were developed and tested using long-term (11 years) wind data from meteorological stations, short-term (1 year) data from a field trial of small-scale turbines, and output from an operational forecast model. As a first step, the performance of an existing boundary layer scaling model was evaluated at 38 UK sites and found to result in large site-specific errors. Based on these findings, a revised model was developed and shown to improve prediction accuracy. However, uncertainty analysis and comparison with onsite measurements revealed average errors in the predicted wind power density of over 60% due to uncertainties in the model input parameters. Hence, it was concluded that such an approach is best applied in a scoping context to identify sites worthy of further study. To investigate the ability of low-cost, data-driven techniques to reduce these uncertainties, MCP approaches were trialled using onsite measurement periods as short as 3 months at a subset of 22 of the above UK sites. In addition to established linear approaches, non-linear Gaussian process regression and bivariate conditional probability approaches were developed. Using a 3 month measurement period, the best performing MCP approaches resulted in average errors in the predicted wind power density of 14%, compared to 26% when using the boundary layer scaling approach at the same sites. The effect of seasonal variability in the prediction errors was investigated in detail and found to be most significant at coastal sites. This variability was found to be reduced by using output from an operational forecast model in place of long-term reference wind data. This work provides a means for low-cost and rapid wind resource assessment in cases where traditional approaches are not viable.
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Gnauck, Robert. "Innovation System Wind Energy Industry." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-149822.

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The wind industry has arrived at a state of technological maturity and is occasionally already today cost-competitive to conventional sources of energy. This thesis investigates the process of innovation that took place within the industry. A theoretical background into economical theory of innovation together with a status quo assessment of today's wind industry serves as introduction to the topic. In the analytical part, inducement mechanisms and functions of technological development will be identified as crucial drivers for innovation within the sector. The key findings of this thesis lead to conclude that it is now the responsibility of the industry to becoming fully cost-competitive to conventional sources. The advancement of technological lifecycle will primarily depend on turbine manufacturers and their capability to drive innovation more independently from governments.
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Books on the topic "Wind energy"

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Bowden, Rob. Energy: Wind energy. North Mankato, MN: Stargazer Books, 2006.

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Wind energy. South Yarra, Vic: Macmillan Education Australia, 2009.

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Armentrout, David. Wind energy. Vero Beach, FL: Rourke Pub., 2009.

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Peinke, Joachim, Peter Schaumann, and Stephan Barth, eds. Wind Energy. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-33866-6.

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Mathew, Sathyajith. Wind Energy. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-30906-3.

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Bowden, Rob. Wind energy. North Mankato, MN: Stargazer Books, 2007.

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Wind energy. Tarrytown, NY: Marshall Cavendish Benchmark, 2009.

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Wind energy. Minneapolis, MN: ABDO Pub. Co., 2013.

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Wind energy. London: HMSO, 1994.

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Wind energy. London: Franklin Watts, 2006.

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Book chapters on the topic "Wind energy"

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Lenzen, Manfred, and Olivier Baboulet. "Wind Energy." In Handbook of Climate Change Mitigation and Adaptation, 1975–2005. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-14409-2_34.

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Ghosh, Tushar K., and Mark A. Prelas. "Wind Energy." In Energy Resources and Systems, 1–77. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1402-1_1.

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Bostan, Ion, Adrian Gheorghe, Valeriu Dulgheru, Ion Sobor, Viorel Bostan, and Anatolie Sochirean. "Wind Energy." In Resilient Energy Systems, 361–422. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4189-8_5.

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Peuteman, J. "Wind Energy." In Energy, Environment, and Sustainability, 217–36. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3284-5_10.

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Wrixon, Gerard T., Anne-Marie E. Rooney, and Wolfgang Palz. "Wind Energy." In Renewable Energy-2000, 19–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-52347-2_3.

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Guerrero-Lemus, Ricardo, and Les E. Shephard. "Wind Energy." In Low-Carbon Energy in Africa and Latin America, 261–78. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52311-8_10.

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Lenzen, Manfred, and Olivier Baboulet. "Wind Energy." In Handbook of Climate Change Mitigation and Adaptation, 1–25. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6431-0_34-2.

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Zini, Gabriele, and Paolo Tartarini. "Wind Energy." In Solar Hydrogen Energy Systems, 73–89. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-1998-0_5.

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Brown, Charles E. "Wind Energy." In World Energy Resources, 175–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56342-3_10.

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Stout, B. A. "Wind Energy." In Handbook of Energy for World Agriculture, 355–83. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0745-4_7.

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Conference papers on the topic "Wind energy"

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Prasai, Anish, and Deepak Divan. "DC Collection for Wind Farms." In 2008 IEEE Energy 2030 Conference (Energy). IEEE, 2008. http://dx.doi.org/10.1109/energy.2008.4781054.

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Papavasiliou, A., and S. S. Oren. "Coupling Wind Generators with Deferrable Loads." In 2008 IEEE Energy 2030 Conference (Energy). IEEE, 2008. http://dx.doi.org/10.1109/energy.2008.4781058.

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Zhu, Yongqiang, Zejun Ding, and Yuan Gong. "Advanced Agricultural Irrigation System Applying Wind Power Generation." In 2008 IEEE Energy 2030 Conference (Energy). IEEE, 2008. http://dx.doi.org/10.1109/energy.2008.4781005.

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Schottler, Jannik, Agnieszka Hölling, Joachim Peinke, and Michael Hölling. "Wind tunnel tests on controllable model wind turbines in yaw." In 34th Wind Energy Symposium. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-1523.

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Ghate, Aditya S., and Sanjiva K. Lele. "A Modeling Framework for Wind Farm Analysis: Wind Turbine Wake Interactions." In 33rd Wind Energy Symposium. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-0725.

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Petrovic, Vlaho, and Carlo L. Bottasso. "Wind Turbine Envelope Riding." In 33rd Wind Energy Symposium. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-1213.

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Churchfield, Matthew J., and Senu Sirnivas. "On the Effects of Wind Turbine Wake Skew Caused by Wind Veer." In 2018 Wind Energy Symposium. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-0755.

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Robertson, Amy, Latha Sethuraman, and Jason M. Jonkman. "Assessment of Wind Parameter Sensitivity on Extreme and Fatigue Wind Turbine Loads." In 2018 Wind Energy Symposium. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-1728.

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Chaudhary, S. K., R. Teodorescu, and P. Rodriguez. "Wind Farm Grid Integration Using VSC Based HVDC Transmission - An Overview." In 2008 IEEE Energy 2030 Conference (Energy). IEEE, 2008. http://dx.doi.org/10.1109/energy.2008.4781061.

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Bottasso, Carlo L., and Filippo Campagnolo. "Wind Turbine and Wind Farm Control Testing in a Boundary Layer Wind Tunnel." In 32nd ASME Wind Energy Symposium. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-0875.

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Reports on the topic "Wind energy"

1

Author, Not Given. Wind energy bibliography. Office of Scientific and Technical Information (OSTI), May 1995. http://dx.doi.org/10.2172/67716.

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Weber, Jochem, Melinda Marquis, Aubryn Cooperman, Caroline Draxl, Rob Hammond, Jason Jonkman, Alexsandra Lemke, et al. Airborne Wind Energy. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1813974.

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3

Gill, Elizabeth, Matilda Kreider, and Suzanne MacDonald. Offshore Wind Energy Basics: Navigating Offshore Wind Energy Decision-Making Processes. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1897061.

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Gruenbacher, Don. Kansas Wind Energy Consortium. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1233445.

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anon. Wind Energy Teachers Guide. Office of Scientific and Technical Information (OSTI), January 2003. http://dx.doi.org/10.2172/836696.

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anon. Wind energy applications guide. Office of Scientific and Technical Information (OSTI), January 2001. http://dx.doi.org/10.2172/836856.

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Stromberg, Richard. Alaska Wind Energy Project. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1347390.

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Lundquist, Julie K., Andrew J. Clifton, Scott Dana, Arlinda Huskey, Patrick J. Moriarty, Jeroen J. Van Dam, and Tommy Herges. Wind Energy Instrumentation Atlas. Office of Scientific and Technical Information (OSTI), May 2019. http://dx.doi.org/10.2172/1513195.

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Joshi, Prateek, and Carishma Gokhale-Welch. Fundamentals of Wind Energy. Office of Scientific and Technical Information (OSTI), November 2022. http://dx.doi.org/10.2172/1898570.

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Christol, Corrie, Chloe Constant, and Jeremy Stefek. Defining Wind Energy Experience. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1896897.

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