Wind energy resource assessment for Kiribati with a comparison of different methods of determining Weibull parameters

Aukitino, Tiaon; Khan, Mohammad G.M.; Ahmed, M. Rafiuddin

doi:10.1016/j.enconman.2017.09.027

Cited by 85 publications

(100 citation statements)

References 26 publications

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“…From the review, they found that if the wind speed distribution matched well with the Weibull function, the six methods were applicable; but if not, the maximum likelihood method performed best, followed by the modified maximum likelihood and moment methods. Furthermore, Aukitino et al [25] assessed the wind speed and found out that moment method was the best fitting, Akdag and Dinler [26] proposed a new method based on wind power density and mean wind speed called the power density (PD) method, Baseer et al [27] estimated the Weibull parameters using a least-squares regression method (LSRM) and the Wind Atlas Analysis and Application Program (WAsP) algorithm, and Ozay and Celiktas [28] did an statistical analysis in Turkey using WAsP.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Wind Energy Characterization in the Gulf of Mexico

2019

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In line with Mexico’s interest in determining its wind resources, in this paper, 141 locations along the states of the Gulf of Mexico have been analyzed by calculating the main wind characteristics, such as the Weibull shape (c) and scale (k) parameters, and wind power density (WPD), by using re-analysis MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications version 2) data with hourly records from 1980–2017 at a 50-m height. The analysis has been carried out using the R free software, whose its principal function is for statistical computing and graphics, to characterize the wind speed and determine its annual and seasonal (spring, summer, autumn, and winter) behavior for each state. As a result, the analysis determined two different wind seasons along the Gulf of Mexico;, it was found that in the states of Tamaulipas, Veracruz, and Tabasco wind season took place during autumn, winter, and spring, while for the states of Campeche and Yucatan, the only two states that shared its coast with the Caribbean Sea and the Gulf of Mexico, the wind season occurred only in winter and spring. In addition, it was found that by considering a seasonal analysis, more accurate information on wind characteristics could be generated; thus, by applying the Weibull distribution function, optimal zones for determining wind as a resource of energy can be established. Furthermore, a k-means algorithm was applied to the wind data, obtaining three clusters that can be seen by month; these results and using the Weibull parameter c allow for selecting the optimum wind turbine based on its power coefficient or efficiency.

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Section: Introductionmentioning

confidence: 99%

Seasonal Wind Energy Characterization in the Gulf of Mexico

2019

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“…Variations in wind speed are characterized by two functions, the first is the probability density function that indicates the percentage of time during which the wind flows with a specific wind speed. It is expressed mathematically as follows [14,15]:…”

Section: Theorymentioning

confidence: 99%

Statistical Analysis of Wind Speed & Wind Direction in Tantan Province, Morocco

Mohammed*,

Abdelaziz,

Mohammed

et al. 2019

IJRTE

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In this paper, a two-parameter Weibull statistical distribution is used to analyze the characteristics of the wind from the Saharan area, located in the Tantan province, Morocco, for 08 years at 10 m. During those 08 years (2009-2017) the frequency distribution of the wind speed, the wind direction, the mean wind speed, the shape and scale (k & c) Weibull parameters have been calculated for the province. The mean wind speed for the entire data set is 6.4 m/s. The parameters k & c are found as 1.9 and 2.52 m/s in relative order. The study also provides an analysis of the wind direction along with a wind rose chart for the province. The analysis suggests that the highest wind speeds that vary (vm = 5.1m/s; vmax = 18.5m/s) prevail between sectors 165-175 ° with an average frequency of 1.4% and lower wind speeds (vm = 2.5m/s; vmax = 9.7m/s) occur between sectors 245-255° with an average frequency of 0.6%. The results of this document help to understand the wind power potential of the province and serve as a source of wind power projects. From a perspective, the wind energy system is an alternative to the future of the Sahara province of Morocco.

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“…At tower height (80 m) the wind speed was determined as follow: in January. Based on the rule of thumb for annual average wind speeds [16], Great Cumbrae Island is suitable for grid connected wind farm for power production.…”

Section: = ( ) ∝ (15)mentioning

confidence: 99%

“…The remarkable renewable energy deployment targets in the UK by 2020, the Scottish Government has targeted to produce 100% of her electricity from renewable sources of energy. This corresponds to approximated installed renewable capacity of [14][15][16]. Currently, onshore wind turbines produce 7.3 GW [5].…”

Section: Introductionmentioning

confidence: 99%

Wind Energy Potential Assessment of Great Cumbrae Island Using Weibull Distribution Function

Kombe

Muguthu

2019

JENRR

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Wind energy is among the fastest growing energy generation technology which is highly preferred alternative to conventional sources of energy. The major Scottish Government target is to deliver 30% of her energy demand by 2020 from renewable sources of energy as well as meeting the emission targets as set under the Scotland Climate Change Act 2009. In this paper, wind energy potential assessment of Great Cumbrae Island was investigated. For this, a ten year mean monthly wind speed at height 50 m obtained from the National Aeronautic Space Administration (NASA) were analysed using the Weibull probability distributions to assess the wind energy potential of Great Cumbrae Island as a clean, sustainable energy resource. Results from the wind-speed model showed that Great Cumbrae Island as high wind-speed site with a mean wind speed of 7.598 m/s and having power density . The annual energy captured by four selected horizontal wind turbine models was determined. The result shows that GE 2.0 platform can capture 4.5 GWh energy in a year which is an acceptable quantity for wind energy.

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Wind energy resource assessment for Kiribati with a comparison of different methods of determining Weibull parameters

Cited by 85 publications

References 26 publications

Seasonal Wind Energy Characterization in the Gulf of Mexico

Seasonal Wind Energy Characterization in the Gulf of Mexico

Statistical Analysis of Wind Speed & Wind Direction in Tantan Province, Morocco

Wind Energy Potential Assessment of Great Cumbrae Island Using Weibull Distribution Function

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