Assessment of Iec 61400-1 Normal Turbulence Model for Wind Conditions in Taiwan West Coast Areas

Leu, Tzong-Shyng; Yo, Jui-Ming; Tsai, Yi‐Lin; Miau, Jiu-Jih; Wang, Ta-Chung; Tseng, Chien-Chou

doi:10.1142/s2010194514603822

Cited by 8 publications

(8 citation statements)

References 1 publication

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“…To consider the effect of wind turbulence, the red power curve is convoluted with a Gaussian filter of a variable width corresponding to a fraction of the wind speed where it is calculated. This fraction corresponds to the wind turbulence intensity and is set to 10% for offshore wind turbines as measured in the Horn Rev wind farms [45] and slightly higher at 15% for onshore wind turbines [46]. The smoothed power curve thus calculated and represented with a black dashed line in Figure 3A) is very similar to the manufacturer's power curve plotted in red.…”

Section: ( ) ( )mentioning

confidence: 83%

Past, present and future environmental footprint of the Danish wind turbine fleet with LCA_WIND_DK, an online interactive platform

Besseau¹,

Sacchi²,

Blanc³

et al. 2019

Renewable and Sustainable Energy Reviews

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Renewable energy systems are promoted and developed notably due to their low environmental footprint. Fleet-wide robust environmental assessments are needed to drive the sustainable transition of energy systems worldwide. This study introduces a tailored comprehensive impact assessment methodology for fleets of renewable energy systems based on Life Cycle Analysis and its application to Danish wind turbines fleet through an online platform LCA_WIND_DK (viewer.webservice-energy.org/lca-wind-dk/). This platform enables to visualize environmental performances of wind turbines in Denmark and their temporal evolution. The fleet is known in detail from 1980 to 2016 and projected from 2017 to 2030 based on national objectives for onshore/offshore capacity and pre-approved offshore projects. Each turbine's future electricity production is estimated from its power curve and geo-localized wind timeseries. More than 10,000 cradle-to-grave life cycle inventories are generated, considering the spatio-temporal context and technological characteristics. The comprehensive analysis of the Danish fleet over fifty years reveals long-term trends for several impact categories. Improvements in all categories follow similar trends as in climate change, which decreases from 40 to 13 g CO2-eq/kWh between 1980 and 2030. Improvements stem from combined economies of scale and higher load factors linked to increasingly large and powerful turbines. The interactive mapping tool LCA_WIND_DK may provide statistics to support renewable energy oriented policy scenarios and unique spatiotemporal environmental information to project developers. This novel approach designed for large territories, here applied to the Danish wind turbine fleet, is generic and can be applied to other renewable energy systems and/or to other territories.

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Section: ( ) ( )mentioning

confidence: 83%

Past, present and future environmental footprint of the Danish wind turbine fleet with LCA_WIND_DK, an online interactive platform

Besseau¹,

Sacchi²,

Blanc³

et al. 2019

Renewable and Sustainable Energy Reviews

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“…The authors therefore propose the determination of the suitable parameters of the model for the site in order to improve the accuracy of the estimate. Leu et al (2014) studied the performance of the model in Taiwan and also determined the appropriate parameters of this model for this study area as a reference for future wind farms. Wang et al (2013) after having evaluated the IEC61400-3 normal turbulence model at three sites in Asia, Europe and the United States, the authors developed a new model.…”

Section: Introductionmentioning

confidence: 99%

Assessment of IEC Normal Turbulence Model and Modelling of the Wind Turbulence Intensity for Small Wind Turbine Design in Tropical Area: Case of the Coastal Region of Benin

Donnou

Akpo

Hounguè

et al. 2020

Int. J. Renew. Energy Dev.

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The wind turbulence intensity observed on a site have an influence the wind turbine energy production and the lifetime of the blades. It is therefore primordial to master this parameter for the optimization of the production. So therefore, this study is interested on the modelling of the wind turbulence intensity at 10 m above the ground on the coast of Benin. Four years of wind data measured on the site of Cotonou Port Authority (PAC) from 2011 to 2014 and recorded with a temporal resolution of 10 min were used. From the transport equation of turbulent kinetic energy followed by a numerical simulation based on the Nelder-Mead algorithm developed under the Matlab software, we proposed five new models for estimating the wind turbulence intensity. The results of the different simulations reveal that four of proposed models and based on the roughness, the speed of friction and the length of Obukhov better fit the data, during the periods of January, April, June, July, August, September and December. The estimators of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) vary from (0.02; 0.01) in December to (0.09; 0.07) in August. As for the model which is a function of roughness and the wind shear coefficient (expressed only according to the wind speed), it gives better performance whatever the time of the year and the atmosphere stability conditions. The estimations errors are included between (0.02; 0.01) obtained in December and (0.08; 0.06) observed in March. A comparative study between the existing models in the literature and the best model proposed in this study showed that only this model gives the best adjustment with the data. It can therefore be used on the sites where turbulence is influenced by the roughness and the atmosphere stability. Finally, from this model a new wind turbine design class has been proposed for the site of Cotonou. It takes into account the actual levels of turbulence observed and thus allow to optimize the energy production. ©2020. CBIORE-IJRED. All rights reserved

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“…To better understand the behavior of such a turbulent system, therefore, a correct modeling of these fluctuations is crucial for estimating the fatigue loads on wind turbine blades [26]. Longitudinal and transversal wind fluctuations which provide important information on the turbulence properties in the atmospheric boundary layer [27] are therefore essential inputs for turbulence intensity estimation models that are instrumental in the design and operation of wind turbines [28]. The choice of turbines and their precise location, then, entail a more accurate determination of wind turbulence conditions, taking into account local factors [29].…”

Section: Introductionmentioning

confidence: 99%

Wind Turbulence Intensity Characteristics at 10m Above Ground Along the Cotonou Coast, Benin

Donnou¹,

Akpo²,

Djossou³

et al. 2019

IJSGE

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The characteristics of the wind turbulence intensity that are essential to know before installing a wind turbine at a site were investigated along the coast of Cotonou in Benin. The average speed, direction, roughness length, friction velocity, turbulence intensity and relationship between the roughness and wind turbulence intensity were evaluated as well. Using the estimators derived from a simple isotropic Gaussian model of turbulent wind fluctuations, we proposed modified models for estimating the turbulence intensity of wind components. Wind speed and direction data recorded at 10 m above ground level from 2011 to 2014 during the first Compact of the Millennium Challenge Account (MCA) in Benin were utilized. The results obtained indicated that the annual average roughness length is evaluated at 1.25×10-4 m, and the annual mean friction velocity is equal to 0.41 m.s-1. Peak values of the turbulence intensity vary from 0.3 to 0.6 except during the months of January, April, July, August and September. The high values obtained could jeopardize the production of wind energy during these months. The correlation between the turbulence intensity and roughness length ranging from 0.75 in January to 0.94 in August revealed that these two parameters are linked by an increasing linear function. Finally, modified formulations of the longitudinal and transversal wind turbulence intensity developed from the van den Hurk and de Bruin model and based on the best-fitting approach were proposed. The error estimators (MAE; RMSE) computed to validate these modified models vary respectively from (0.0099; 0.0141) to (0.0614; 0.0890).

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Assessment of Iec 61400-1 Normal Turbulence Model for Wind Conditions in Taiwan West Coast Areas

Abstract: This paper studies the applicability of Normal Turbulence Model (NTM) in IEC61400-1 for wind conditions in Taiwan

Cited by 8 publications

References 1 publication

Past, present and future environmental footprint of the Danish wind turbine fleet with LCA_WIND_DK, an online interactive platform

Past, present and future environmental footprint of the Danish wind turbine fleet with LCA_WIND_DK, an online interactive platform

Assessment of IEC Normal Turbulence Model and Modelling of the Wind Turbulence Intensity for Small Wind Turbine Design in Tropical Area: Case of the Coastal Region of Benin

Wind Turbulence Intensity Characteristics at 10m Above Ground Along the Cotonou Coast, Benin

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