Wind energy as a potential generation source at Ras Benas, Egypt

Ahmed, Ahmed Shata

doi:10.1016/j.rser.2010.03.006

Cited by 93 publications

(33 citation statements)

References 32 publications

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“…These seasonal variations of the wind are much more predictable. Finally, the short term time scale variability covers time scales of minutes to seconds, also well known by the term "turbulence" and which is of critical interest in the wind turbine design process [17][18][19][20][21][22][23]. For more than half a century the Weibull distribution has attracted the attention of statisticians working on theory and methods as well as various fields of statistics [22,[24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications

2014

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Abstract:The best Weibull distribution methods for the assessment of wind energy potential at different altitudes in desired locations are statistically diagnosed in this study. Seven different methods, namely graphical method (GM), method of moments (MOM), standard deviation method (STDM), maximum likelihood method (MLM), power density method (PDM), modified maximum likelihood method (MMLM) and equivalent energy method (EEM) were used to estimate the Weibull parameters and six statistical tools, namely relative percentage of error, root mean square error (RMSE), mean percentage of error, mean absolute percentage of error, chi-square error and analysis of variance were used to precisely rank the methods. The statistical fittings of the measured and calculated wind speed data are assessed for justifying the performance of the methods. The capacity factor and total energy generated by a small model wind turbine is calculated by numerical integration using Trapezoidal sums and Simpson's rules. The results show that MOM and MLM are the most efficient methods for determining the value of k and c to fit Weibull distribution curves.

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Section: Introductionmentioning

confidence: 99%

Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications

2014

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“…Today, wind energy is widely used to produce electricity in many countries such as Denmark, Spain, Germany, United States, and India [4].…”

Section: Introductionmentioning

confidence: 99%

Potential of Wind Speed for Wind Power Generation In Perlis, Northern Malaysia

et al. 2011

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Abstrak Makalah ini menyajikan analisa karakteristik-karakteristik kecepatan angin di Keywords: weibull distribution function, wind power generation, wind speed IntroductionRenewable energy has an increasing role in achieving the goals of sustainable development, energy security and environmental protection. Nowadays, it has been recognized as one of the most promising clean energy over the world because of its falling cost, while other renewable energy technologies are becoming more expensiveWind energy is one of renewable energy which produced by continuously blowing wind and can be captured using wind turbines that convert kinetic energy from wind into mechanical energy and then into electrical energy [3]. Today, wind energy is widely used to produce electricity in many countries such as Denmark, Spain, Germany, United States, and India [4].A lot of researchers have been studying the wind speed characteristics and its potential as a wind power generation in many countries worlwide. Six kinds of numerical methods for estimating Weibull parameters were reviewed by [5]; i.e. the moment, empirical, graphical, maximum likelihood, modified maximum likelihood, and energy pattern factor method. The result showed that the maximum likelihood modified maximum likelihood and moment methods present relatively more excellent ability throughout the simulation test. From analysis of actual data, it is found that if wind speed distribution matches well with Weibull function, the six methods are applicable, but if not, the maximum likelihood method performs best followed by the modified maximum likelihood and moment methods, based on double checks including

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“…where V 1 is the measured wind speed at 10m height, V 2 is the extrapolated wind speed at height Z=50m in this case, the value of roughness factor b varies from less than 0.1 over the top of the steep hills to over 0.25 in the sheltered location (Ahmed, 2010) or estimated from the relation…”

Section: Methodsmentioning

confidence: 99%

“…To be able to do this several equations have been propose in literature but in this study we choose to adopt to weibull function which gives an accurate match with experimental data (Ahmed, 2010). Weibull distribution function is characterized by two parameters; the dimensionless shape parameter k; and the scale parameter c which has a unit of similar to wind speed (m/s) (Mahyoub & Ahmed, 2012).…”

Section: Methodsmentioning

confidence: 99%

Accessement of Wind Power Potential in Katsina State from Meteorological Data

Balarabe¹,

Abdullah²,

Nawawi³

et al. 2014

MAS

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For the need to exploit and optimize the use of all available sources of energy in providing available power supply for industrial and economic development of the rural communities. Nigerian government has initiated some programs in 2005 to look into the potentialities of wind as a means for power generation. In order to complement this effort, surface synoptic wind speed observation data (2008-2013) from NOAA-NCDC for Nigeria was used to analyze the wind characteristics of katsina (13.007' N; 07.661' E; Altitude 05060.0 m; Air density 1.18 kg/m3). A modified form of an empirical relation for calculating wind power density and energy with height was proposed and uses to analyze site wind profile and the efficiency of electricity production for power generation in Katsina. The measurements have been performed for a period of 6years from January to December in order to cover all seasonal variation of the wind condition. The results showed that katsina falls under Class 7 of the International system of wind classification by recording annual values of mean wind speed extrapolated at 50m height to be in the range of 3.5-5.5 m/s, average power density of 160.6 W/m 2 and an average of 29.6Kwh/year of energy can be produced for each month of a year respectively, making the site very suitable for wind turbine applications for power generation.

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Wind energy as a potential generation source at Ras Benas, Egypt

Cited by 93 publications

References 32 publications

Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications

Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications

Potential of Wind Speed for Wind Power Generation In Perlis, Northern Malaysia

Accessement of Wind Power Potential in Katsina State from Meteorological Data

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