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

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.

Comparison of numerical methods in estimating Weibull parameters to install a sustainable wind farm in mount Bamboutos, Cameroon

“…The evaluation of the wind potential of the South-West and littoral regions allowed concluding that EPFM and moment method were the two most efficient methods for estimating the scale and shape parameters (Ayuketang Arreyndip and Joseph, 2016;Arreyndip et al, 2016;Kengne et al, 2019). Other research works were also investigated to evaluate the wind potential of different regions in the world (Ayodele et al, 2012;Gupta, 1986;Alimi et al, 2012;Sarkar et al, 2011;Siti et al, 2011;Zheng and Zhao, 2015;Feijoo and Villanueva, 2016;Safari and Gasore, 2010;Morgan, 1995;Justus et al, 1977;Deaves and Lines, 1997;Seguro and Lambert, 2000;Akpinar and Akpinar, 2005;Dorvlo, 2002;Mathew, 2006;Cook, 2001;Shata and Hanitsch, 2006;Basumatary et al, 2005;Genc et al, 2005;Pérez et al, 2005;Bagiorgas et al, 2008;Baseer et al, 2017). The present work aims to evaluate the wind potential of Mount Bamboutos through the determination of Weibull parameters using nine numerical methods, namely, the empirical method of Justus (EMJ), the empirical method of lysen (EML), the MoM, the graphical method (GM), the Mabchour's method (MMab), the EPFM, the maximum likelihood method (MLM), the MMLM and the alternate maximum likelihood method (AMLM).…”

A novel method for studying the wind speed probability distribution and estimating the average wind energy density

“…Researchers have initially proposed various single probability distribution models to fit the wind speed distribution. Single models mainly include the Weibull distribution, the Normal distribution, and the Rayleigh distribution, of which the Weibull distribution is the most commonly used [12][13][14]. The Weibull distribution model fits the data well for most areas where the actual wind speed probability distribution curve is singlepeaked, except at low wind speeds where the fit error is large.…”

Robust optimization for composite blade of wind turbine based on kriging model

“…Its long blade span, limited capacity to control blade tip deflection for ensuring a safe distance between tip and tower, along with the trend of individual wind turbine capacity increasing year by year, all suggest that it requires a higher stiffness than other small and medium blades. [1][2][3][4] For these HAWTs, studies have found that the blade weight grows with a rotor radius at R 2.3 while the rotor power grows with R 2.1 . 5 Future larger wind turbines for higher power extraction capabilities require increasing blade stiffness to accommodate the significantly increased blade weight as the rotor radius grows.…”