Regarding the influence of the Van der Hoven spectrum on wind energy applications in the meteorological mesoscale and microscale

被引:13
|
作者
Soberanis, M. A. Escalante [1 ]
Merida, W. [1 ]
机构
[1] Univ British Columbia, Clean Energy Res Ctr, Vancouver, BC V67 1Z3, Canada
关键词
Wind energy; Turbulence; Van der Hoven spectrum; High frequency data; TURBULENT WIND; SPEED; POWER; TURBINE; MODEL; OUTPUT;
D O I
10.1016/j.renene.2015.03.048
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
We demonstrate the use of high frequency data (HFD) to reproduce the power spectrum shown by Van der Hoven in 1957. His work represents the basis of wind energy standards such as averaging and variability in the frequency domain. Our results unveil discrepancies with Van der Hoven's approach, which can be related to constraints in the computing capabilities in the 1950's. We show a major eddy-energy peak at a period of 2 days and a smaller eddy-energy peak contribution at frequencies higher than the region known as the spectrum gap. The variance calculated by the area under the curve indicated that the spectral energy is mainly due to the Power Spectral Density (PSD) values located in the microscale region. We calculated the economic value of this energy based on the turbulence kinetic energy of the wind data set. We also conclude that, given the results of the present study, HFD analysis in the frequency domain uncover eddy energy peaks that determine energy fluctuations in the short and long terms. This information is lost every time data are erased from current monitoring systems. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:286 / 292
页数:7
相关论文
共 50 条
  • [1] A method based on the Van der Hoven spectrum for performance evaluation in prediction of wind speed
    Kaya, Elif
    Barutcu, Burak
    Mentes, Sukran Sibel
    TURKISH JOURNAL OF EARTH SCIENCES, 2013, 22 (04) : 681 - 689
  • [2] Mesoscale to Microscale Coupling for Wind Energy Applications: Addressing the Challenges
    Haupt, S. E.
    Berg, L.
    Churchfield, M.
    Kosovic, B.
    Mirocha, J.
    Shaw, W.
    NAWEA WINDTECH 2019, 2020, 1452
  • [4] Downscaling mesoscale meteorological models for computational wind engineering applications
    Yamada, Tetsuji
    Koike, Katsuyuki
    JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2011, 99 (04) : 199 - 216
  • [5] Wind-Climate Estimation Based on Mesoscale and Microscale Modeling: Statistical-Dynamical Downscaling for Wind Energy Applications
    Badger, Jake
    Frank, Helmut
    Hahmann, Andrea N.
    Giebel, Gregor
    JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY, 2014, 53 (08) : 1901 - 1919
  • [6] On Bridging A Modeling Scale Gap: Mesoscale to Microscale Coupling for Wind Energy
    Haupt, Sue Ellen
    Kosovic, Branko
    Shaw, William
    Berg, Larry K.
    Churchfield, Matthew
    Cline, Joel
    Draxl, Caroline
    Ennis, Brandon
    Koo, Eunmo
    Kotamarthi, Rao
    Mazzaro, Laura
    Rocha, Jeffrey M.
    Moriarty, Patrick
    Munoz-Esparza, Domingo
    Quon, Eliot
    Rai, Raj K.
    Robinson, Michael
    Sever, Gokhan
    BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2019, 100 (12) : 2533 - 2550
  • [7] Mesoscale modeling and remote sensing for wind energy applications
    Chavez, R.
    Gomez, H.
    Francisco Herbert, J.
    Romo, A.
    Probst, O.
    REVISTA MEXICANA DE FISICA, 2013, 59 (02) : 114 - 129
  • [8] Wind Power Energy in Southern Brazil: evaluation using a mesoscale meteorological model
    Krusche, Nisia
    Peralta, Carlos
    Chang, Chi-Yao
    Stoevesandt, Bernhard
    EUROPEAN GEOSCIENCES UNION GENERAL ASSEMBLY 2015 - DIVISION ENERGY, RESOURCES AND ENVIRONMENT, EGU 2015, 2015, 76 : 164 - 168
  • [9] Downscaling wind energy resource from mesoscale to microscale model and data assimilating field measurements
    Duraisamy, V. J.
    Dupont, E.
    Carissimo, B.
    SCIENCE OF MAKING TORQUE FROM WIND 2012, 2014, 555
  • [10] A Model for the Spectrum of the Lateral Velocity Component from Mesoscale to Microscale and Its Application to Wind-Direction Variation
    Larsen, Xiaoli G.
    Larsen, Soren E.
    Petersen, Erik L.
    Mikkelsen, Torben K.
    BOUNDARY-LAYER METEOROLOGY, 2021, 178 (03) : 415 - 434