Statistical Analysis of Wind Speed and Evaluation of Wind Power Density for Colaba, Mumbai

Francis, Felix A.; Nalamutt, Tejaswini D.

doi:10.34218/ijaret.10.1.2019.003

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The Weibull distribution is a probability distribution that is continuous. According to many academics, the Weibull model is the most appropriate methodology to apply because it provides a good relationship to experimental data (Alavi et al, 2016; Boudia and Guerri, 2015; Francis and Nalamutt, 2019). As a result, the probability distribution function of the Weibull model is the most relevant statistic to extract from a set of wind speed data for use in evaluating wind energy resources.…”

Section: Methodology and Mathematical Modelingmentioning

confidence: 99%

Electricity production and comparative analysis for wind availability power potential assessment at four sites in Egypt

Ibrahim

2021

Wind Engineering

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This research represents the first wind energy potential assessment that covers major provinces in Egypt. The paper investigates a realistic study technically and economically of wind energy as a talented renewable source for electricity production of various regions in Egypt. More accurate prediction and measurement of wind speed and direction allow wind plants to supply clean, renewable power to businesses, and homeowners at lower costs. Wind resource assessments must be precise in order for wind farms to be built successfully. Wind resource assessments have been carried out in this study. Wind resources evaluation and precise assessment of wind capacity for the four selected sites in Egypt’s provinces from 2017 for 3 years at 10, 50 m above ground level (AGL): Hurgada, Aswan, Alexandria, and the capital of Egypt (Cairo). The wind speed data is taken from NASA for different sites in Egypt. The average annual wind speed was estimated to be 4.44, 4.31, 4.91, and 3.9 m/s at 10 m height, respectively. The economical factors such as NPC and COE in the selected regions are estimated. The optimum location for wind assessment in Egypt is Alexandria which gives maximum wind speed, maximum annual energy, minimum levelized cost of energy, and highest capacity factor. The proposed wind assessment will generated 20,1729 kWh of electricity per year and electricity generation cost per kWh/$ is 0.0818844. This planned cost of wind electric generation is compatible with the local electricity tariff. Also, Feasibility of Construction small wind turbine in this site is investigated. In addition, a criterion of wind farm site selection is presented here with Environmental Impact Assessment (EIA) study through Birds Migration aspect that decreases with increase turbine tower length and short blade length. Through reducing Egypt’s domestic fossil fuel consumption, this work will potentially save tons of carbon emissions each year.

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Section: Methodology and Mathematical Modelingmentioning

confidence: 99%

Electricity production and comparative analysis for wind availability power potential assessment at four sites in Egypt

Ibrahim

2021

Wind Engineering

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“…At present, most works on WPD are based on Weibull distribution 19–23 . The specific calculation method is to substitute the two‐parameter Weibull distribution function into the WPD calculation formula:

P ((), v) = \frac{1}{2} ρ \int_{0}^{+ \infty} f ((), v) \cdot v^{3} normalⅆ v = \frac{1}{2} ρ \int_{0}^{+ \infty} ((), \frac{k}{c}) {(\frac{v}{c})}^{k - 1} italicexp ([], - {(\frac{v}{c})}^{k}) v^{3} normalⅆ v .

…”

Section: Statistical Functionsmentioning

confidence: 99%

New estimation method of wind power density with three‐parameter Weibull distribution: A case on Central Inner Mongolia suburbs

et al. 2021

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Suburbs have the potential for future wind energy development because of their open terrain, good wind conditions, closeness to the city, wind power that can be absorbed in place, and other advantages. Therefore, accurately evaluating the wind energy resources in the suburbs is particularly important. The three‐parameter Weibull distribution can accurately describe the wind speed in the suburbs, but research on the estimation of wind power density (WPD) with it is limited. On the basis of the mathematical models of wind speed probability distribution as Rayleigh, two‐parameter Weibull, and three‐parameter Weibull, a new WPD estimation formula with three‐parameter Weibull distribution is derived using the partial integration method and by introducing incomplete gamma function to solve the transcendental integration. A field experiment platform is built, and the accuracy of the formula is verified by the measured data. The analysis of the measured data collected in recent 3 years in the suburbs of Hohhot, Inner Mongolia, concludes that the statistical characteristics of the measured wind speed and the distribution of wind energy resources are more conducive to capture the scattered and changing wind energy in the suburbs by taking the month as the time scale. Moreover, the three‐parameter Weibull distribution estimated by the least square method can be used as the local wind speed model. The new method of WPD estimation can also reduce the calculation error by approximately 21.06%. This study provides an important reference for the subsequent analysis of wind power fluctuation characteristics and wind energy development planning.

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Study of Performance Ratio of Newly Developed High-Performance Airfoil with Respect to the Airborne Wind Energy System

Shah,

Kothari,

Shaikh

et al. 2024

Green Energy and Technology

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Statistical Analysis of Wind Speed and Evaluation of Wind Power Density for Colaba, Mumbai

Cited by 4 publications

References 18 publications

Electricity production and comparative analysis for wind availability power potential assessment at four sites in Egypt

Electricity production and comparative analysis for wind availability power potential assessment at four sites in Egypt

New estimation method of wind power density with three‐parameter Weibull distribution: A case on Central Inner Mongolia suburbs

Study of Performance Ratio of Newly Developed High-Performance Airfoil with Respect to the Airborne Wind Energy System

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