Estimation of different wind characteristics parameters and accurate wind resource assessment for Kadavu, Fiji

Wind resource assessment is carried out for a site in Kadavu, Fiji Islands. This included estimating the Weibull parameters and wind power density using ten different methods and carrying out an economic analysis. The wind speeds at 34 m and 20 m above ground level, wind direction, atmospheric pressure and temperature were measured for 18 months and statistically analyzed. The overall average wind speed at a height of 34 m above ground level was found to be 3.63 m/s. The seasonal averages for the site were 3.81 m/s and 3.40 m/s for summer and winter respectively. The diurnal variation of the wind shear for the site was correlated with the temperature variation. The Moments Method was found to be the best method for the entire period of study. The Modified Maximum Likelihood Method was found to be the best for the summer season whilst Median and Quartile method was the best for the winter period. The mean wind power density at the location was found to be 45.88 W/m2. The WAsP software was used to create the wind resource map. Five potential sites were selected installing the wind turbines and for carrying out the economic analysis, which included an estimation of annual energy production. It was found that, for the five turbines average capacity factor would be 20.05%. The payback period for installing the wind turbines at 50 m above the ground level is estimated to be between 6.99 and 8.74 years

Wind Characteristics during Cyclones in Suva, Fiji, for the Last Six Cyclone Seasons

Wind speeds during the tropical cyclones that affected Suva, Fiji, during the last six cyclone seasons are statistically analysed. Since 2012, there were at least 13 cyclones that affected Suva. The wind data were recorded for the six cyclones seasons from October 2012 to April 2018. NRG#40C cup anemometers with a data-logger were used to record the wind speeds at 34 m and 20 m above ground level (AGL) for the above duration. The main investigation of these cyclones’ wind characteristics was based on the determination of turbulence intensity (TI), gust factor and the peak factor. The turbulence characteristics were then compared with the AS-NZ-1170.2-2002 standard and the peak factors were compared with the ISO standards. Detailed studies were carried out for the two strongest cyclones: Cyclone Evan and cyclone Winston, during which the 10-minute-averaged maximum wind speed exceeded 25 m/s at 34 m AGL

Wind energy resource assessment for Suva, Fiji, with accurate Weibull parameters

Wind resource assessment is carried out for Suva, the capital of the Republic of Fiji Islands. The wind speeds at 34 m and 20 m above ground level (AGL), wind direction, atmospheric pressure and temperature were measured for more than five years and statistically analyzed. The daily, monthly, yearly and seasonal averages were estimated. For the site, the overall average wind speed at 34 m AGL is found to be 5.18 m/s. The occurrence of effective wind (between the cut-in and cut-off wind speeds of the selected turbine) is predominantly from the east. An effective wind speed of 74.175% was recorded which can be used for power generation. The turbulence intensity (TI) and wind shear coefficient are estimated. The site’s overall TIs are 12.5% and 13.72% at 34 m and 20 m AGL respectively. The diurnal wind shear correlated with the temperature variation very well. The overall and seasonal wind distributions are analyzed which shows that the wind speed in Suva is mostly between 3-9 m/s although the winter season has higher wind speeds. The Weibull parameters and the wind power density were found using ten different methods. The wind power density is estimated to be 159 W/m2 using the best method, which is found to be the empirical method of Justus. A high resolution map around the site is digitized and the wind power density resource map is generated using WAsP. From the WAsP analysis, it is seen that Suva has high potential for power generation. Five possible locations are selected for installing wind turbines and the annual energy production is estimated using WAsP. The total annual energy production from the five sites is 1950 MWh. The average capacity factor of the five turbines is 17%. An economic analysis is performed which showed a payback period of 10.83 years

Wind speed forecasting using regression, time series and neural network models: a case study of Kiribati

There is an increase in demand for renewable sources of energy due to apprehensions about climate change, increase in the energy demand and unpredictability of the prices and supply of fossil fuels. Wind energy is one of the world’s fastest growing sources of energy. As a result of the stochastic behavior of wind, the demand for accurate wind forecasting has become imperative to reduce the risk of uncertainty. In this paper, the wind speed data are modelled and forecasted using three forecasting techniques: Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). To test these models for wind speed forecasting, daily wind speed, pressure, relative humidity and temperature data for the period of September 2012 to September 2013 for Abaiang in Kiribati were used in this work. The performance of the models was evaluated using four measures: root mean square error, mean absolute error, mean absolute percentage error and coefficient of determination (R2). The optimum model was also compared to a benchmark technique, persistence method. The empirical results reveal that the proposed model using Artificial Neural Network is more efficient and accurate in forecasting wind speed in comparison to the regression and time series models

Wind Energy Resource Assessment for Tokelau with Accurate Weibull Parameters

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

Assessment of wind energy potential for Tuvalu with accurate estimation of Weibull parameters

Wind resource assessments are carried out for two sites in Tuvalu: Funafuti and Nukufetau. The wind speeds at 34 m and 20 m above ground level (AGL) were recorded for approximately 12 months and analyzed. The averages of each site are computed as the overall, daily, monthly, annual and seasonal averages. The overall average wind speeds for Funafuti and Nukufetau at 34 m AGL were estimated to be 6.19 m/s and 5.36 m/s respectively. The turbulence intensities (TI) at the two sites were also analyzed. The TI is also computed for windy and low-wind days. Wind shear analysis was carried out and correlated with temperature variation. Ten different methods: median and quartiles method (MQ), the empirical method of Lysen (EML), the empirical method of Justus (EMJ), the moments method (MO), the least squares method (LS), the maximum likelihood method (ML), the modified maximum likelihood method (MML), the energy pattern factor method (EPF), method of multi-objective moments (MM) and the WAsP method were used to find the Weibull parameters. From these methods, the best method is used to determine the wind power density (WPD) for the site. The WPD for Funafuti is 228.18 W/m2 and for Nukufetau is 145.1 W/m2. The site maps were digitized and with the WAsP software, five potential locations were selected for each site from the wind resource map. The annual energy production for the sites were computed using WAsP to be 2921.34 MWh and 1848.49 MWh. The payback periods of installing the turbines for each site are calculated by performing an economic analysis, which showed payback periods of between 3.13 and 4.21 years for Funafuti and between 4.83 to 6.72 years for Nukufetau