Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 5 Articles
Dual-Doppler lidar is a powerful remote sensing technique that can accurately measure\nhorizontal wind speeds and enable the reconstruction of two-dimensional wind fields based on\nmeasurements from two separate lidars. Previous research has provided a framework of dual-Doppler\nalgorithms for processing both radar and lidar measurements, but their application to wake\nmeasurements has not been addressed in detail yet. The objective of this paper is to reconstruct\ntwo-dimensional wind fields of wind turbine wakes and assess the performance of dual-Doppler lidar\nscanning strategies, using the newly developed Multiple-Lidar Wind Field Evaluation Algorithm\n(MuLiWEA). This processes non-synchronous dual-Doppler lidar measurements and solves the\nhorizontal wind field with a set of linear equations, also considering the mass continuity equation.\nMuLiWEA was applied on simulated measurements of a simulated wind turbine wake, with two\ntypical dual-Doppler lidar measurement scenarios. The results showed inaccuracies caused by the\ninhomogeneous spatial distribution of the measurements in all directions, related to the ground-based\nscanning of a wind field at wind turbine hub height. Additionally, MuLiWEA was applied on a\nreal dual-Doppler lidar measurement scenario in the German offshore wind farm ââ?¬Å?alpha ventusââ?¬Â.\nIt was concluded that the performance of both simulated and real lidar measurement scenarios\nin combination with MuLiWEA is promising. Although the accuracy of the reconstructed wind\nfields is compromised by the practical limitations of an offshore dual-Doppler lidar measurement\nsetup, the performance shows sufficient accuracy to serve as a basis for 10 min average steady wake\nmodel validation....
Wind farm power production is known to be strongly affected by turbine wake effects.\nThe purpose of this study is to develop and test a new analytical model for the prediction of wind\nturbine wakes and the associated power losses in wind farms. The new model is an extension of\nthe one recently proposed by Bastankhah and Port�©-Agel for the wake of stand-alone wind turbines.\nIt satisfies the conservation of mass and momentum and assumes a self-similar Gaussian shape of the\nvelocity deficit. The local wake growth rate is estimated based on the local streamwise turbulence\nintensity. Superposition of velocity deficits is used to model the interaction of the multiple wakes.\nFurthermore, the power production from the wind turbines is calculated using the power curve.\nThe performance of the new analytical wind farm model is validated against power measurements\nand large-eddy simulation (LES) data from the Horns Rev wind farm for a wide range of wind\ndirections, corresponding to a variety of full-wake and partial-wake conditions. A reasonable\nagreement is found between the proposed analytical model, LES data, and power measurements.\nCompared with a commonly used wind farm wake model, the new model shows a significant\nimprovement in the prediction of wind farm power....
The wave energy in a shallow water location is evaluated considering the influence of the\nlocal tide and wind on the wave propagation. The target is the coastal area just north of the Portuguese\ncity of Peniche, where a wave energy converter operates on the sea bottom. A wave modelling system\nbased on SWAN has been implemented and focused on this coastal environment in a multilevel\ncomputational scheme. The first three SWAN computational belonging to this wave prediction\nsystem were defined using the spherical coordinates. In the highest resolution computational domain,\nCartesian coordinates have been considered, with a resolution of 25 m in both directions. An in-depth\nanalysis of the main characteristics of the environmental matrix has been performed. This is based\non the results of eight-year model system simulations (2005ââ?¬â??2012). New simulations have been\ncarried out in the last two computational domains with the most relevant wave and wind patterns,\nconsidering also the tide effect. The results show that the tide level, together with the wind intensity\nand direction, may influence to a significant degree the wave characteristics. This especially concerns\nthe wave power in the location where the wave converter operates....
Based on the standardized modelling of the International Modelling Team, study on double-fed\ninduction generator (DFIG) wind turbine is processed in this paper, aiming at capability of universally and\nreasonably reflecting key performance related to large scale system analysis. The standardized model proposed\nis of high degree of structural modularity, easy functional extension and universalization of control strategy\nand signal. Moreover, it is applicable for wind turbines produced by different manufacturers through model\nparameter adjustment. The complexity of the model can meet both needs of grid-connected characteristic\nsimulation of wind turbine and large scale power system simulation....
Wind turbine power curves are calibrated by turbine manufacturers under requirements\nstipulated by the International Electrotechnical Commission to provide a functional mapping\nbetween the mean wind speed v and the mean turbine power output P. Wind plant operators\nemploy these power curves to estimate or forecast wind power generation under given wind\nconditions. However, it is general knowledge that wide variability exists in these mean calibration\nvalues. We first analyse how the standard deviation in wind speed ÃÆ?v affects the mean P and the\nstandard deviation ÃÆ?P of wind power. We find that the magnitude of wind power fluctuations scales\nas the square of the mean wind speed. Using data from three planetary locations, we find that\nthe wind speed standard deviation ÃÆ?v systematically varies with mean wind speed v, and in some\ninstances, follows a scaling of the form ÃÆ?v = C Ã?â?? vÃ?±; C being a constant and Ã?± a fractional power. We\nshow that, when applicable, this scaling form provides a minimal parameter description of the power\ncurve in terms of v alone. Wind data from different locations establishes that (in instances when this\nscaling exists) the exponent Ã?± varies with location, owing to the influence of local environmental\nconditions on wind speed variability. Since manufacturer-calibrated power curves cannot account\nfor variability influenced by local conditions, this variability translates to forecast uncertainty in\npower generation. We close with a proposal for operators to perform post-installation recalibration\nof their turbine power curves to account for the influence of local environmental factors on wind\nspeed variability in order to reduce the uncertainty of wind power forecasts. Understanding the\nrelationship between windââ?¬â?¢s speed and its variability is likely to lead to lower costs for the integration\nof wind power into the electric grid....
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