Wind energy resource assessments for Pentecost Island and Epi Island in Vanuatu were carried out using one year of wind data. The wind data were used to calculate the daily average wind speed, diurnal variation of wind speed and monthly average wind speed. The diurnal variation of wind shear coefficient of the site was also studied and it correlated well with the temperature variation. Ten methods were used to determine the Weibull parameters and the wind power density of the site. The best method was determined using the goodness of fit test/error where the correlation coefficient, coefficient of efficiency, root mean square error, maximum absolute error and maximum absolute percentage error for the 10 methods were compared. It was found that the moments method was the best method for obtaining the shape parameter (k), the scale parameter (A) and the correct wind power density for the Pentecost Island site, whereas for the Epi site, the median and quartiles method performed the best. The mean wind speed for the Pentecost site was 5.60 m/s, while that for the Epi site was 5.86 m/s. The Weibull parameters were also estimated for the two seasons for both the islands. The wind resource maps showing the wind power density were also obtained. The annual energy production from 10 Vergnet 275 kW wind turbines, positioned at good locations on the digital wind map, was estimated. Finally, an economic analysis of the turbines was carried out, which indicated a payback period of 4.85 years.
Funds for carrying out this work were provided by Korea International Cooperation Agency (KOICA) under its East-Asia Climate Partnership program. The project number was 2009-00042.
Wind energy resource assessment for two sites, Fakaofo and Atafu, in Tokelau is carried out with the help of a detailed statistical analysis of one year of measured wind data. The average wind speeds recorded for the sites were 3.81 m/s and 3.92 m/s for the Fakaofo and Atafu sites respectively at 34 m above ground level (AGL). The turbulence intensities (TI) for the two sites were also estimated. The wind shear coefficient correlated well with the temperature for both the sites. The best Weibull distribution method of approximation for the Fakaoko site was the WAsP method whereas it was the Empirical Method of Justus (EMJ) for the Atafu site from the 10 different methods that were used. The payback periods for installing the wind turbines were estimated to be 7.39 years and 7.85 years respectively for Fakaofo and Atafu.
Fiji needs to invest in renewable energy sources to meet its energy needs to reduce the country’s dependence on imported fossil fuels. For investing in wind energy projects, a detailed assessment of wind energy resource is required. In this work, wind speeds were measured at 34 m and 20 m above ground level at a site in Suva for three years and the daily, monthly, yearly and seasonal averages were estimated. Average turbulence intensities at the two heights were also estimated. The Weibull parameters, average wind speed and the wind power density were estimated by using eleven frequentist methods and a Bayesian technique. These twelve methods were compared against each other for their performance using five goodness of fit test and error measures. The best method was found to be the empirical method of Lysen (EML) which gave a mean wind speed of 5.04 m/s and a wind power density (WPD) of 147.79 W/m2. A horizontal axis wind turbine of 30 kW capacity was designed and optimized using Harp_Opt software which works on a multi-objective genetic algorithm. The blade sections (airfoils) were designed using an in-house multi-objective genetic algorithm code by mathematically parametrized 7th order Bezier curve coupled with XFOIL software. The lift and drag coefficients were interpolated using AirfoilPrep to get the data in the required format as needed by the Harp_Opt GUI. The Weibull parameters from the statistical analysis of the measured data were used to optimize the performance characteristics of the wind turbine. The output power curve shows a cut-in speed of about 2 m/s and a rated wind speed of 10 m/s. The AEP was optimized from around 47.3 MWhr/year to 48.3 MWhr/year after 50th iteration of Harp_Opt.
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