Wind Speed Data Analysis Using Weibull and Rayleigh Distribution Functions, Case Study: Five Cities Northern Morocco

Bidaoui, Hicham; Abbassi, Ikram El; Bouardi, Abdelmajid El; Darcherif, Abdelmoumen

doi:10.1016/j.promfg.2019.02.286

Cited by 67 publications

(45 citation statements)

References 9 publications

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“…In order to find the best model to predict wind speed on a short time scale we rely on meteorological data from the city of Tetouan [13] [14] taken every hour throughout 2018 from the site www.wunderground.com. Our data is identified by wind speed (WS) en m/s, wind direction (DIR) en deg, the temperature (TMP) en °C, the dew temperature (DEW) en °C, atmospheric pressure (ATM) en hPa, and humidity (H) en %.…”

Section: Methodsmentioning

confidence: 99%

Levenberg-Marquardt Training Function using on MLP, RNN and Elman Neural Network to Optimize Hourly Forecasting in Tetouan City (Northern Morocco)

Amellas¹,

Djebli²,

Echchelh³

2020

JESTR

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Section: Methodsmentioning

confidence: 99%

Levenberg-Marquardt Training Function using on MLP, RNN and Elman Neural Network to Optimize Hourly Forecasting in Tetouan City (Northern Morocco)

Amellas¹,

Djebli²,

Echchelh³

2020

JESTR

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“…Ground obstructions retard the movement of air close to the ground surface, causing a reduction of wind speed. Several methods are used to correct the wind speed for different height [18][19][20][21][22][23][24]. Logarithm law, which is used in many research work in the literature [18,19], is an empirical formula in which the mean wind speed at a given height using this law can be evaluated as follows:…”

Section: Corrected Wind Speed Data At Different Heightmentioning

confidence: 99%

Comparative study between measured and estimated wind energy yield

Al-Quraan¹,

Al-Mahmodi²,

Radaideh³

et al. 2020

Turk J Elec Eng & Comp Sci

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This paper proposes a power-speed (P-V) model of the wind turbine by assuming three different functions for the first performance region; cubic, quadratic and uncorrected cubic. These three functions have been compared with the manufacturer models of five different wind turbines which were installed in five different locations in Jordan; Tafila, Hofa, Fujeij, Al Rajef, and Deahan. The wind turbine of these wind farms are considered as large scale HAWT in the range of Mw. The generated P-V models are developed by applying a new method described in this paper which is basically based on generating a multiplier factor x. In this study, the quadratic model shows the highest correlation compared with the other models. The wind energy yield for the selected wind farms has been estimated by a mathematical modelling based on Rayleigh distribution function, derived in this paper. The energy yield using this mathematical model has been compared with the measured energy output of four wind farms, Tafila, Hofa, Al Rajef, and Deahan. The measured energy were provided by the operators of these wind farms which are: Jordan Wind Project Company(JWPC), Central Electricity Generation Company (CEGC), Green Watts Renewable Energy (GWRC)and Korean Southern Power Company(KOSPO). Results show that the estimated energy using the quadratic wind turbine model for all wind farms are very close to the actual output. Accuracy analysis for the quadratic model resulted in an error of less than 10 % between the measured and estimated energy output for all wind farms. The capacity factors for the selected wind farms have been estimated using the quadratic P-V model. Results show that Tafila wind farm has the highest capacity factor which is around 47 % .

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“…It is considered for this study three different values of V 0 (4, 6 and 8 m/s) and three different values of k (1.5, 2 and 3). These values cover wind velocity distributions for different regions of the world [26][27][28][29][30][31][32][33][34][35] and velocities around the viability of small-scale HAWTs [36].…”

Section: Figmentioning

confidence: 99%

Combined effects of pitch angle, rotational speed and site wind distribution in small HAWT performance

Oliveira

Tofaneli

Santos

2020

J Braz. Soc. Mech. Sci. Eng.

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Constant speed horizontal axis wind turbines operate at power coefficients different than its maximum due to operation at different tip speed ratios (TSRs). Whereas rotational speed and the site wind speed distribution determine the TSR range of the operation, the pitch angle is significant for the aerodynamic performance of the wind turbine. In this paper, a blade element momentum approach is used to study the power production effects of pitch angle and rotational speed of NREL Phase VI wind turbine at different site wind distributions (characterized by Weibull distributions). It is shown that the best combination of rotational speed and pitch angle always occurs at the same pitch angle, but for different rotational speeds (other than the best one) the best pitch angle may vary. The best rotational speed is found to be dependent on both form and scale factors of Weibull wind speed distribution. Improvements in turbine pitch angle and rotational speed are found to increase generated energy up to four times, but the increasing factor is highly dependent on wind speed distribution: For the studied turbine, high-wind-speeds sites can benefit more from the adjustment of these parameters than low-speed ones. Keywords Wind turbine • BEM model • Pitch angle • Rotational speed • Wind distribution Greek symbols Pitch angle (°) Gamma function Turbine rotational speed (rad/s) Local flow angle (°) Solidity (-) Air density (kg/m 3)

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Wind Speed Data Analysis Using Weibull and Rayleigh Distribution Functions, Case Study: Five Cities Northern Morocco

Cited by 67 publications

References 9 publications

Levenberg-Marquardt Training Function using on MLP, RNN and Elman Neural Network to Optimize Hourly Forecasting in Tetouan City (Northern Morocco)

Levenberg-Marquardt Training Function using on MLP, RNN and Elman Neural Network to Optimize Hourly Forecasting in Tetouan City (Northern Morocco)

Comparative study between measured and estimated wind energy yield

Combined effects of pitch angle, rotational speed and site wind distribution in small HAWT performance

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