Interests in wind energy have gained impetus in many developed and developing countries worldwide during the last three decades. This is due to awareness of the population about the depletion of fossil fuels as well as Government campaigns and initiatives to encourage the use of renewable sources of energy. This work focuses on the wind energy potential at two selected locations (Plaisance and Vacoas) in Mauritius. The emphasis is to assess whether small-wind turbines have a potential in these regions for generation of power for domestic applications. Such wind turbines can range in size from 400 W to 10 kW depending on the amount of electricity to be generated. The assessment is based on the correlation of the local wind speed data to a two-parameter Weibull probability distribution in order to effectively estimate the average wind power density of the sites. Nearly 40 years of mean wind speed data is utilized. Of the two sites investigated it is found that Plaisance yielded the highest wind velocity (as compared to Vacoas). The study also estimates the energy output of six commercial small-wind turbines of capacity ranging from 1 kW to 3 kW at these two sites, placed at multiple heights.
Mauritius is an island situated in the southwest region of the Indian Ocean. Its mainland covers an area of 1865 km2 and it has a coastline of 330 km. Mauritius has a population of around 1.27 million inhabitants and a growth rate of 1% per annum [
A map of the main island of Mauritius showing the locations of Vacoas and Plaisance and their respective elevations above sea level.
The assessment of wind energy potential of a region requires long-term data related to the wind speed and an accurate determination of its distribution. The variations in wind speed can be characterized by two functions which are the probability density function (PDF) and the corresponding cumulative density function (CDF). Dhunny et al. [
The Weibull probability density function (WPDF),
To estimate the parameters
Parameter
Wind speed data are normally measured at a certain height and, in most cases, the wind turbine hub is placed at a different height. In order to adjust the measured wind speed data to the level of the wind turbine hub, the power law is the most common expression accepted in the literature [
As the wind velocity varies with height, it is evident that the Weibull parameters also are functions of hub height. Oyedepo et al. [
The wind power
The instantaneous electrical energy
The main island of Mauritius spans around 60 km in length and 45 km in width and is located in the southwest tropical region of the Indian Ocean. Being of volcanic origin, its topography consists of a central plateau which is about 500 meters above sea level and gradually rising towards the southwest where it reaches its highest point at about 700 meters above sea level. This plateau is surrounded by a chain of mountains and some isolated peaks. Urban areas are mostly concentrated on the central plateau and coastal regions. The island of Mauritius enjoys a mild tropical maritime climate throughout the year. The meteorological conditions along the coasts are practically the same but cooler over the central plateau [
The geographical coordinates and altitudes for each site.
Site | Plaisance | Vacoas |
---|---|---|
Location | Southeast coast | Central plateau |
Longitude | 57° 40′E | 57° 29′E |
Latitude | 20° 25′S | 20° 17′S |
Altitude above sea level | 55 m | 425 m |
The Mauritius Meteorological Services (MMS) is the authorized Government organization for all meteorological activities in the island. Its endeavor is to provide accurate and timely weather information and meteorological products for the general welfare of the citizens and is also the Early Warning Center for natural disasters affecting the Republic of Mauritius. The MMS maintains a network of surface synoptic stations, agrometeorological and upper air stations which are fairly well distributed over the island. It has in its database, stored at the Utah Data Climate Center, over 40 years of mean wind speed data records which were measured at a height of 10 m above ground level. The prevailing trade winds over the island blow predominantly from the east with an average speed of ~5 m/s. Occasionally, during the peak winter months of July and August, with the passage of strong anticyclones, wind gusts are likely to reach 25 m/s in some exposed areas [
This study utilizes the wind data for the two selected sites. Data obtained for Vacoas is from 1 July 1977 to 30 July 2013, while data for Plaisance is from 1 January 1973 to 30 July 2013. The AWS [
Statistical description of wind data for Plaisance and Vacoas.
Statistics | Estimates (m/s) | |
---|---|---|
Plaisance | Vacoas | |
Mean ( |
4.05 | 3.57 |
Standard Deviation (SD) | 1.79 | 1.61 |
Minimum value ( |
0.07 | 0.08 |
Maximum value ( |
30.40 | 37.60 |
Daily mean wind data at Vacoas at 10 m above ground level from 1 June 1977 to 31 July 2013.
Wind time series
Wind rose
Daily mean wind data at Plaisance at 10 m above ground level from 1 June 1973 to 31 July 2013.
Wind time series
Wind rose
The methodology discussed in Section
The probability distribution of wind speed is needed in evaluating the potential of wind power at different sites. Hence the selection of appropriate wind turbines can be made to obtain optimum results. Figures
Weibull distribution curves fitted to wind speed data at Plaisance at 10 m above ground level, from January to December (1973–2013). The mean value for the data is also displayed.
January (
February (
March (
April (
May (
June (
July (
August (
September (
October (
November (
December (
Weibull distribution curves fitted to wind speed data at Vacoas at 10 m above ground level, from January to December (1977–2013). The mean value for the data is also displayed.
January (
February (
March (
April (
May (
June (
July (
August (
September (
October (
November (
December (
Tables
Weibull parameters for monthly wind data at Plaisance at 10 m above ground level.
Months | Parameters | 1973–1982 | 1983–1992 | 1993–2002 | 2003–2013 | 1973–2013 |
---|---|---|---|---|---|---|
January |
|
2.11 | 2.12 | 2.43 | 2.08 | 2.04 |
|
4.75 | 4.93 | 4.92 | 4.70 | 4.66 | |
|
||||||
February |
|
2.00 | 2.17 | 2.42 | 2.11 | 1.86 |
|
4.68 | 4.85 | 4.96 | 4.73 | 4.80 | |
|
||||||
March |
|
2.07 | 2.30 | 2.40 | 2.15 | 2.11 |
|
4.68 | 4.78 | 4.97 | 4.86 | 4.51 | |
|
||||||
April |
|
1.90 | 2.26 | 2.50 | 2.17 | 2.16 |
|
4.69 | 4.78 | 5.03 | 4.75 | 4.48 | |
|
||||||
May |
|
2.14 | 2.23 | 2.42 | 2.20 | 2.14 |
|
4.90 | 4.83 | 4.90 | 4.77 | 4.52 | |
|
||||||
June |
|
1.98 | 2.24 | 2.50 | 2.30 | 2.32 |
|
4.88 | 4.88 | 4.85 | 4.77 | 4.94 | |
|
||||||
July |
|
2.02 | 2.36 | 2.44 | 2.20 | 3.71 |
|
4.86 | 4.79 | 4.92 | 4.81 | 5.45 | |
|
||||||
August |
|
2.18 | 2.38 | 2.25 | 2.14 | 2.59 |
|
4.95 | 4.79 | 5.09 | 4.79 | 5.45 | |
|
||||||
September |
|
2.16 | 2.43 | 2.49 | 2.14 | 2.46 |
|
5.05 | 4.78 | 5.08 | 4.79 | 5.19 | |
|
||||||
October |
|
2.23 | 2.25 | 2.46 | 2.14 | 2.61 |
|
4.85 | 4.75 | 4.99 | 4.86 | 5.05 | |
|
||||||
November |
|
1.97 | 2.27 | 2.43 | 2.13 | 2.68 |
|
4.64 | 4.72 | 4.94 | 4.89 | 4.66 | |
|
||||||
December |
|
2.05 | 2.36 | 2.52 | 2.04 | 2.30 |
|
4.64 | 4.98 | 4.97 | 4.87 | 4.59 |
Weibull parameters for monthly wind data at Vacoas at 10 m above ground level.
Months | Parameters | 1977–1986 | 1987–1996 | 1997–2006 | 2007–2013 | 1977–2013 |
---|---|---|---|---|---|---|
January |
|
1.45 | 2.13 | 2.05 | 2.43 | 2.19 |
|
3.84 | 4.05 | 4.15 | 3.93 | 4.04 | |
|
||||||
February |
|
1.55 | 1.87 | 2.25 | 1.89 | 2.04 |
|
4.12 | 4.01 | 4.36 | 4.02 | 4.20 | |
|
||||||
March |
|
2.18 | 2.27 | 2.22 | 2.31 | 2.32 |
|
4.36 | 4.23 | 3.98 | 4.05 | 4.08 | |
|
||||||
April |
|
1.92 | 2.28 | 2.19 | 2.47 | 2.30 |
|
3.83 | 4.34 | 4.04 | 3.98 | 4.09 | |
|
||||||
May |
|
2.61 | 2.37 | 1.91 | 2.13 | 2.21 |
|
4.70 | 4.12 | 3.88 | 3.79 | 4.00 | |
|
||||||
June |
|
2.85 | 2.22 | 2.17 | 2.46 | 2.33 |
|
4.68 | 4.20 | 4.12 | 4.09 | 4.20 | |
|
||||||
July |
|
2.86 | 2.42 | 2.68 | 2.53 | 2.45 |
|
5.02 | 4.39 | 4.62 | 4.43 | 4.40 | |
|
||||||
August |
|
2.50 | 2.46 | 2.60 | 2.82 | 2.39 |
|
5.00 | 4.68 | 4.71 | 4.41 | 4.39 | |
|
||||||
September |
|
2.50 | 2.24 | 2.36 | 2.67 | 2.39 |
|
4.98 | 4.18 | 4.02 | 4.43 | 4.39 | |
|
||||||
October |
|
2.21 | 2.40 | 2.28 | 2.73 | 2.38 |
|
4.31 | 4.33 | 3.93 | 4.21 | 4.18 | |
|
||||||
November |
|
2.14 | 2.09 | 2.15 | 2.60 | 2.29 |
|
4.32 | 3.95 | 3.68 | 3.92 | 3.90 | |
|
||||||
December |
|
2.40 | 1.96 | 1.91 | 2.69 | 2.27 |
|
4.50 | 3.85 | 3.45 | 4.05 | 4.00 |
Figure
Plot of monthly variations in wind power densities at 10 m above ground level.
Plaisance
Vacoas
Table
Weibull parameters for yearly wind data at Plaisance and Vacoas at 10 m above ground level.
Year | Parameter | Plaisance | Vacoas |
---|---|---|---|
2003 |
|
2.19 | 2.24 |
|
4.45 | 4.37 | |
|
|||
2004 |
|
2.32 | 2.50 |
|
4.41 | 4.21 | |
|
|||
2005 |
|
2.10 | 2.44 |
|
4.71 | 4.07 | |
|
|||
2006 |
|
2.44 | 2.40 |
|
5.10 | 4.36 | |
|
|||
2007 |
|
2.14 | 2.19 |
|
5.26 | 4.16 | |
|
|||
2008 |
|
2.18 | 2.42 |
|
5.02 | 4.16 | |
|
|||
2009 |
|
2.05 | 2.51 |
|
4.84 | 4.07 | |
|
|||
2010 |
|
2.17 | 2.59 |
|
4.77 | 3.85 | |
|
|||
2011 |
|
2.00 | 2.51 |
|
4.25 | 3.82 | |
|
|||
2012 |
|
2.30 | 2.80 |
|
5.07 | 4.19 | |
|
|||
2013 |
|
1.97 | 2.17 |
|
4.71 | 3.88 |
Yearly variations in wind power densities at Plaisance and Vacoas at 10 m above ground level, from 2003 to 2013.
To have an estimate of the performance of wind turbines at Vacoas and Plaisance, six small commercial wind turbines were selected. These turbines were considered owing to their good performance and reasonable cost. The characteristic properties of these wind turbines are listed in Table
Main characteristics of six small commercial wind turbines.
Characteristics | Aeolos | Eddy GT | Exmok | SRM | WindSpot | |
---|---|---|---|---|---|---|
Rated power (kW) | 1 | 3 | 1 | 1.5 | 3 | 1.5 |
Hub height (m) | 9 | 9 | 12 | 12 | 12 | 12 |
Swept area (m2) | 8.1 | 19.6 | 4.62 | 8.1 | 12.6 | 8.9 |
Cut-in wind speed (m/s), |
3 | 3 | 3.5 | 2.5 | 3 | 3 |
Cut-off wind speed (m/s), |
25 | 25 | 20 | 17 | 20 | 20 |
Rated wind speed (m/s) | 10 | 10 | 12 | 9 | 10 | 12 |
Power curve of six commercial wind turbines operating in the range from 1 to 3 kW.
The energy output for the year 2013 was analyzed in this case for both sites. Figure
Yearly wind energy output of the six wind turbines at different heights above ground level.
Plaisance
Vacoas
In this study, the wind potential analysis was performed for two specific locations in Mauritius: Plaisance and Vacoas. The MLE method was employed to compute the Weibull
The authors declare no conflict of interests regarding the publication of this paper.
The authors would like to thank the Utah Climate Center for the meteorological data provision. Also gratitude is expressed to the University of Mauritius for providing facilities for research and the Mauritius Research Council for the funding of this project. Special thanks are extended to the HRDR funds, the Project Coordinator, Dr. G.K. Beeharry, Research Assistant Mr. Vinand Prayag, and SCAP consultants Michelle Willmers and Henry Trotter.