Characteristics for wind energy and wind turbines by considering vertical wind shear

Yu-qiao Zheng , Rong-zhen Zhao

Journal of Central South University ›› 2015, Vol. 22 ›› Issue (6) : 2393 -2398.

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Journal of Central South University ›› 2015, Vol. 22 ›› Issue (6) : 2393 -2398. DOI: 10.1007/s11771-015-2765-6
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Characteristics for wind energy and wind turbines by considering vertical wind shear

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Abstract

The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm, the power-law process was used to simulate the wind speeds at a hub height of 60 m. The Weibull and Rayleigh distributions were chosen to express the wind speeds at two different heights. The parameters in the model were estimated via the least square (LS) method and the maximum likelihood estimation (MLE) method, respectively. An adjusted MLE approach was also presented for parameter estimation. The main indices of wind energy characteristics were calculated based on observational wind speed data. A case study based on the data of Hexi area, Gansu Province of China was given. The results show that MLE method generally outperforms LS method for parameter estimation, and Weibull distribution is more appropriate to describe the wind speed at the hub height.

Keywords

Weibull distribution / wind power / vertical wind shear / power-law process / parameter estimation

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Yu-qiao Zheng, Rong-zhen Zhao. Characteristics for wind energy and wind turbines by considering vertical wind shear. Journal of Central South University, 2015, 22(6): 2393-2398 DOI:10.1007/s11771-015-2765-6

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References

[1]

ISLAMM R, MEKHILEFS, SAIDURR. Progress and recent trends of wind energy technology [J]. Renewable and Sustainable Energy Reviews, 2013, 21(1): 456-488

[2]

ZHANGW-j, HUANGS-d, GAOJ, CHENZhe. An analytic electromagnetic calculation method for performance evolution of doubly fed induction generators for wind turbines [J]. Journal of Central South University, 2013, 20: 2763-2774

[3]

LIUD, NIUD-x, WANGHui. Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm [J]. Renewable Energy, 2014, 62(1): 592-597

[4]

DAHMOUNIA W, SALAHM B, ASKRIF, KERKENIC, NASRALLAHS B. Assessment of wind energy potential electricity generation in Brojcedria, Tunisia [J]. Renewable and Sustainable Energy Reviews, 2011, 15(1): 815-820

[5]

SAFARIB. Modeling wind speed and wind power distributions in Rwanda [J]. Renewable and Sustainable Energy Reviews, 2011, 15(2): 925-935

[6]

FRANDSENS T, JORGENSENH E, RATHMANNO L E. The making of a second-generation wind farm efficiency model complex [J]. Wind Energy, 2009, 12(5): 445-458

[7]

HOWELLR, QINN, EDWARDSJ, DURRANIN. Wind tunnel and numerical study of a small vertical axis wind turbine [J]. Renewable Energy, 2010, 35(2): 412-422

[8]

BALOUKTSISA, CHASSAPISD, KARAPANTSIOST D. A nomogram method for estimating the energy produced by wind turbine generators [J]. Solar Energy, 2002, 72(3): 251-259

[9]

AKPINARE K, AKPINARS. An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics [J]. Energy Conversion and Management, 2005, 46(11/12): 1848-1867

[10]

FOLEYA M, LEAHYP G, MARVUGLIAA, MCKEOGHE J. Current methods and advances in forecasting of wind power generation [J]. Renewable Energy, 2012, 37(1): 1-8

[11]

DORVLOA S S. Estimating wind speed distribution [J]. Energy Conversion and Management, 2002, 43(17): 2311-2318

[12]

ZHUB, LIC-h, LUD-rong. Wind energy resource assessment of Jiuquan, Gansu Province [J]. Journal of Arid Meteorology, 2009, 27(2): 152-156

[13]

LINL, YANGH X, BURNETTJ. Investigation on wind power potential on Hong Kong islands-an analysis of wind power and wind turbine characteristics [J]. Renewable Energy, 2002, 27(1): 1-12

[14]

WANGC-x, ZHANGYuanWind power generation [M], 2003BejingChina Power Press29-31

[15]

KANTARY M, USTAI. Analysis of wind speed distributions: Wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function [J]. Energy Conversion and Management, 2008, 49(5): 962-973

[16]

AKPINARE K, AKPINARS. Determination of the wind energy potential for Maden, Turkey [J]. Energy Conversion and Management, 2004, 45(18/19): 2901-2914

[17]

MAL, LUANS-y, JIANGC-w, LIUH-l, ZHANGYan. A review on the forecasting of wind speed and generated power [J]. Renewable and Sustainable Energy Reviews, 2009, 13(4): 915-920

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