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Statistical analysis of wind data and assessment of wind potential in Burkina Faso

This paper reports the assessment of Burkina Faso wind potential. To achieve this, measurements were made over a period of 11 years (2006-2016) by Burkina Faso National Meteorological Agency (ANAM) using an anemometer established on a 10m mat above the ground. Weibull distribution was used to model the average monthly and annual wind speed. A comparative study by Weibull on three parameter estimation methods (shape and scale parameter) is presented in order to minimize errors in estimating the power and energy density available on ten sites in Burkina Faso. These include the standard deviation method (empirical method), the energy pattern factor and the maximum likelihood. The results of the study show that the average monthly wind speed at 10 m above the ground varies from a minimum of 0.61 ms-1 at Dori (September) to a maximum of 3.57 m.s-1 in Bobo (May). The minimum and maximum annual average speeds are recorded at Dori, 1.06 m.s-1 and Bobo-Dioulasso, 3.02 m.s-1, respectively. The standard deviation method and the maximum likelihood method give the best overall adjustment of the actual wind data distribution. Weibull parameter estimation results show that the shape parameter varies between 1.47 at Dédougou (June) and 5.11 in Bobo-Dioulasso (January) while the scale parameter varies between 0.68 m.s-1 at Dori (October) and 3.90 m.s-1 (May) in Bobo-Dioulasso. The average annual value of the scale parameter varies from 1.19 m.s-1 (Dori) to 3.34 m.s-1 (Bobo-Dioulasso) while the average annual value of the shape parameter varies from 1.66 (Dori) to 3.77 (Bobo). The results of the average monthly power density show that the minimum value of 0.3390 W / m2 is recorded at Dori (November) while the maximum value of 34.5070 W / m2 is recorded in Bobo-Dioulasso (May). On an annual scale, the results of the average annual power density vary from a minimum value of 1.794 W / m2 on the Boromo site to a maximum value of 22.529 W/m2 for that of Bobo-Dioulasso. Statistical indicators show that the maximum likelihood method and the standard deviation method best adjust the real wind data with a determination coefficient ( 2R ) and an average squared error (RMSE) higher than 0.95 and less than 1.5, respectively for all study sites.

Auteur(s) : Drissa BORO, Willy Magloire NKOUNGA, Mouhamadou Falilou NDIAYE, MamadouLamine NDIAYE, Florent P. KIENO & Joseph D. BATHIEBO
Année de publication : 2019
Revue : International Journal of Engineering Sciences & Research Technology
Type : Article
Mise en ligne par : NDIAYE Mouhamadou Falilou