Statistical Analysis of Wind Resources at Darling for Energy Production

Zaccheus Olaniyi Olaofe, Komla A. Folly

Abstract


This paper presents a statistical analysis of wind resources at the Darling site for wind energy assessment and evaluation. Three statistical distribution functions were fitted to a collection of wind speed data at 10, 50 and 70m hub heights to determine the best distribution function to be used for modeling of the wind speed at these hub heights. Results show that the Rayleigh function modeled the wind speed best at these hub heights as compared to the other functions. Accuracy test was conducted using an independent wind dataset, collected on 40m hub height to validate the goodness of fit of these statistical functions. The Rayleigh function proved to be accurate for modeling the wind speed at various hub heights. The choice of Rayleigh function is based on the accuracy of the function modeling the wind speed at various heights and the testing criteria. Furthermore, the wind resources were mapped with the wind power densities as the annual mean wind power densities were estimated at 289 W/m² and 333 W/m², and the annual mean wind speed were estimated at 6.19 m/s and 6.49m/s on 50m and 70m heights respectively.


Keywords


Wind Data, Maximum Likelihood Estimation (MLE), Air Density, Wind Distributions, Wind Power Density

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References


J. Waewsak, C. Chancham, M. Landry and Y. Gagnon; “An Analysis of Wind Speed Distribution at Thasala, Nakhon Si Thammarat, Thailandâ€, Journal of Sustainable Energy & Environment 2, p51-55, 2011

I. Fyrippis, PJ. Axaopoulos, G. Panayiotou, “ Analysis of wind Potential and Energy Production in Naxos Island, Greeceâ€, WSEAS Transactions on Power Systems, vol 3, Issue: 8, August 2008

JP Hennesessey, “Some aspects of wind power statisticsâ€, Journal of Applied Meteorology 16, pp. 119-128, 1977.

GA Torres, JL Prieto, and EDE Francisco, “A “Fitting wind speed distribution: A case studyâ€. Solar Energy 62(2): pp. 139-144, 1998.

OS. Ohunakin “Wind characteristics and Wind energy Assessment in Uyo, Nigeriaâ€, Jouranl of Engineering and Applied Sciences 6 (2), pp. 141-146, 2011

EK Akpinar, and S. Akpinar; “Statistical Analysis of Wind Energy Potentials on the basis of Weibull and Rayleigh Distributions for Agin-Elazig, Turkeyâ€, Journal of Power and Energy, Vol. 218, pp. 557-565, 2004.

M. H. Albadi, EF. El-Saadany, and H. A. Albadi; “Wind to Power a New City in Omanâ€, International Conference on Communication, Computer, And Power (ICCP’09), Muscat, February 2009

J. Aidan, J.C. Ododo; “Wind Speed Distributions and Power Densities of Some Cities in Northern Nigeriaâ€; Journal of Engineering and Applied Sciences, vol 5, Issue: 6, pp. 420-426, 2010

MR. Patel; “Wind and solar power systems, design, analysis and operationâ€, 2nd edition, CRC Press PLC, New York, U.S.A, 2006.

Sathyajith Mathew “Wind Energy Fundamentals, Resources Analysis and Economicsâ€, Springer, 1st edition Germany, 2006

T. Ackermann, Wind power in power systems, Wiley 2005, John Wiley & Sons, 2005.

T.R. Ayodele, A.A. Jimoh, J.L Munda and J.T. Agee, “Empirical modeling of wind speed in wind energy applications: the case study of Port Elizabethâ€, Southern African Universities Power Engineering Conference, SAUPEC 13-15th July, 2011

ZO. Olaofe, and K.A. Folly; “Wind Energy Analysis on the basis of Rayleigh Distribution for Darling City, South Africaâ€, International Conference on Renewable Energy, Generation and Application, March 2012

RK Panda, TK Sarkar, and AK Bhattacharya “Stochastic study of wind energy potential in Indiaâ€, Energy 15(10): pp. 921-930, 1990

GA Torres, JL Prieto, and EDE Francisco, “Fitting wind speed distribution: A case studyâ€. Solar Energy 62(2): pp. 139-144, 1998.

GR Justus CG “Physical climatology of solar and wind energyâ€, Singapore: World Scientific. AWS Scientific, Inc. pp. 321-76, 1996

E. Scerri and R. Farrugia, “Wind data evaluation in the Maltese Islandsâ€, Renewable Energy, (7), pp. 109-114. 1996

K. Krishnamoorthy., Handbook of Statistic, University of Louisiana at Lafayette, by Taylor & Francis Group, LLC, U.S.A. c 2006

Matlab R2010a, version 7.10.0.499.

RMR Kainkwa, “Wind speed pattern and the aavailable wind power at Basotu, Tanzaniaâ€, Renewable energy 21, pp. 289-95,2000

J Liu, Y Jiang; “A Statistical Analysis of Wind Power Density Based on the Weibull Models for Fujian Province in Chinaâ€, 978-1-4244-4702-2/09 ©2009 IEEE 2009

ALF Jowder, “Wind power analysis and site matching of wind turbine generatorsin Kingdom of Bahrainâ€, Applied Energy 86, pp. 538–545, 2009

AN Celik, “A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkeyâ€, Renewable Energy, 29, pp. 593–604, 2003

http://www.dnr.mo.gov/energy/renewables/wind-energy.




DOI (PDF): https://doi.org/10.20508/ijrer.v2i2.176.g111

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