Numerical Study of the Power Range of Wind Devices Adapted to the Wind Potential of the Coastal Region of Cameroon

SOSSO MAYI OLIVIER THIERRY, Daniel Alain ANYOUZO, A.NGNISSOUN NDACHIGAM

Abstract


Profitable exploitation of wind energy requires a good estimation of the wind's potential. This preliminary study phase represents a key factor in the implementation of a wind project. In this perspective, this article assesses the wind energetic potential of the coastal region of Cameroon and offers a range of suitable wind turbines. The wind analysis is carried out using a Weibull model with two parameters, the form factor k and the scale factor C. The determination of the Weibull’s parameters is then carried out by the Maximum Likelihood Method (MLM), using measured wind data from the meteorological station of the multinational company COTCO-Cameroon for the period of 2009 to 2019.The study reveals that the average annual wind speed varied between 2.64 m/s in 2009 and 4.36 m/s in 2019 at 10 m from ground level. By contrast, the annual average power density varies between 35.9 W/m2 to 195.3 W/m2, respectively from 10m to 100m from ground level. The range of wind turbines retained is that of small wind turbines, whose powers are included between 1kW to 100 kW, with starting speeds and nominal speeds respectively in the intervals (1.5-3m/s) and (10-12 m/s).


Keywords


Wind potential, Cameroon coastline, Weibull distribution, range of wind turbines, Small Wind turbines

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References


Nkongho Ayuketang Arreyndip, Ebobenow Joseph, Wind energy potential assessment of Cameroon’s coastal regions for the installation of an onshore wind farm, Heliyon 2 (2016) e00187.

D. K. Kidmo, B. Bogno, D. Raidandi, M. Aillerie, S.D. Yamigno, O. Hamandjoda, and Beda Tibi, Assessment of wind energy potential and cost estimation of wind-generated electricity at hilltops surrounding the city of Maroua in Cameroon, American Institute of Physics, 1758, 020012 (2016).

Rene Tchinda, Joseph Kendjio, Ernest Kaptouom, Donation Njomo, Estimation of mean wind energy available in Far North Cameroon.

F.Abanda, Renewable energy sources in Cameroon: Potentials, benefits and enabling environment, Renewable and Sustainable Energy Reviews 16 (2012) 4557–4562.

D. K. Kaoga, S. Doka Yamigno, D. Raidandi, Noël Djongyang, Performance analysis of Weibull methods for estimation of wind speed distributions in the adamaoua region of Cameroon, International Journal of Basic and Applied Sciences, 3 (3) ,2014, pp 298-306.

Kidmo Kaoga D. Raidandi D. Yamigno Doka S.DjongyangN, Performance analysis of two parameter Weibull distribution methods for wind energy applications in the district of Maroua, Cameroon. Journal of Fundamental and Applied Sciences, 2014, 6(2),pp 155-176.

D.K. Kidmo, R. Danwe, S.Y. Doka ,and N. Djongyang, Statistical analysis of wind speed distribution based on six Weibull Methods for wind power evaluation in Garoua Cameroon, Revue des Energies Renouvelables Vol. 18, N°1, 2015, pp 105-125

D. K. Kidmo, Kodji Deli, D. Raidandi, Serge Doka Yamigno, Wind energy for electricity generation in the Far North region of Cameroon,Energy Procedia 93, 2016, pp 66 -73.

D. Afungchui and C.E. Aban, Analysis of wind regimes for energy estimation in Bamenda, of the North West Region of Cameroon, based on the Weibull distribution, Revue des Energies Renouvelables Vol. 17 N°1 (2014) 137-147.

Prem Kumar Chaurasiya, Siraj Ahmed, Vilas Warudkar, Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument, Alexandria Engineering Journal (2018)57, 2299–2311.

Udo NA, Oluleye A. and Ishola KA, Investigation of Wind Power Potential over Some Selected Coastal Cities in Nigeria,Innovative Energy & Research,2017, 6:1

Olayinka S. Ohunakin, Olaolu O. Akinnawonu, Assessment of wind energy potential and the economics of wind power generation inJos, Plateau State, Nigeria, Energy for Sustainable Development 16 (2012) 78–83.

B. S. Premono, D. D. D. P. Tjahjana, and S. Hadi, Wind energy potential assessment to estimate performance of selected wind turbine in northern coastal region of Semarang-Indonesia, International Conference on Engineering, Science and Nanotechnology 2016.

Fathi Ben Amar, Mustapha Elamouri, Rachid Dhifaoui, Evaluation of Wind Characteristics and Wind Turbines Characteristics in Sidi Daoud Farm, Tunisia, Ecologic Vehicles Renewable Energies, Monaco,march 2009.

Rachid Maouedj, Souad Bousalem, Boumediene Benyoucef, Étude des performances d’un système éolien. Application pour des sites algériens. JITH 2007, Aug 2007, Albi, France. 5p. hal-00163923.

E. Dokur, and M. Kurban, Wind Speed Potential Analysis Based on Weibull Distribution,Balkan Journal Of Electrical & Computer Engineering, Vol.3, No4, 2015,pp 231-235

Khouloud Bedoud, Mahieddine Ali-Rachedi, Rabah Lakel, Assessment and analysis of wind energy generation and power control of wind turbine system, Rev. Sci. Technol., Synthèse 32: 147-162 (2016)

Nkongho Ayuketang Arreyndip, Ebobenow Joseph, Small 500 kW onshore wind farm project in Kribi, Cameroon: Sizing and checkers layout optimization model, Energy Reports 4 (2018) 528–535.

M. B. Kumar, Saravanan B., Padmanaban S. and J.B. Holm-Nielsen, Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India,Energies 2019, 12, 2158.

D. Afungchui, Rayleigh distribution-based model for prediction of wind energy potential of Cameroon, Energie Review, Vol. 1 N°2 (2014) 26-43.

María de los Ãngeles Pinto C., Jennyffer Katerine Moreno .C at al.,Technical and Economic Evaluation of a Small-Scale Wind Power System Located in Berlin, Colombia, tecciencia, 2018.




DOI (PDF): https://doi.org/10.20508/ijrer.v10i3.11089.g8021

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