Statistical Analysis of Wind Resources at Darling for Energy Production

Statistical Analysis of Wind Resources at Darling for Energy Production

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.

___

  • J. Waewsak, C. Chancham, M. Landry and Y. Gagnon; “An Analysis of Wind Speed Distribution at Thasala, Nakhon Si Thammarat, Thailand”, 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, Greece”, WSEAS Transactions on Power Systems, vol 3, Issue: 8, August 2008
  • JP Hennesessey, “Some aspects of wind power statistics”, Journal of Applied Meteorology 16, pp. 119- , 1977.
  • GA Torres, JL Prieto, and EDE Francisco, “A “Fitting wind speed distribution: A case study”. Solar Energy (2): pp. 139-144, 1998.
  • OS. Ohunakin “Wind characteristics and Wind energy Assessment in Uyo, Nigeria”, Journal 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, Turkey”, 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 Oman”, 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 Nigeria”; Journal of Engineering and Applied Sciences, vol 5, Issue: 6, pp. 420-426, 2010
  • MR. Patel; “Wind and solar power systems, design, analysis and operation”, 2nd edition, CRC Press PLC, New York, U.S.A, 2006.
  • Sathyajith Mathew “Wind Energy Fundamentals, Resources Analysis and Economics”, Springer, 1st edition Germany, 2006
  • EW. Golding; “The Generation of Electricity by Wind Power”, London, 1955
  • 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 Elizabeth”, 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 Africa”, International Conference on Renewable Energy, Generation and Application, March RK Panda, TK Sarkar, and AK Bhattacharya “Stochastic study of wind energy potential in India”, Energy 15(10): pp. 921-930, 1990
  • GA Torres, JL Prieto, and EDE Francisco, “Fitting wind speed distribution: A case study”. Solar Energy (2): pp. 139-144, 1998.
  • GR Justus CG “Physical climatology of solar and wind energy”, Singapore: World Scientific. AWS Scientific, Inc. pp. 321-76, 1996
  • E. Scerri and R. Farrugia, “Wind data evaluation in the Maltese Islands”, Renewable Energy, (7), pp. 109- 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 available wind power at Basotu, Tanzania”, 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 China”, 978-1-4244-4702-2/09 ©2009 IEEE
  • ALF Jowder, “Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain”, 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 Turkey”, Renewable Energy, 29, pp. 604, 2003
International Journal Of Renewable Energy Research-Cover
  • ISSN: 1309-0127
  • Başlangıç: 2015
  • Yayıncı: İlhami ÇOLAK
Sayıdaki Diğer Makaleler

Nigerian Jatropha Curcas Oil Seeds: Prospect for Biodiesel Production in Nigeria

Elizabeth Funmilayo Aransiola, Michael Olawale Daramola, Tunde Victor Ojumu, Mujidat Omolara Aremu, Stephen kolawole Layokun, Bamidele Ogbe Solomon

Reliable Estimation of Density Distribution in Potential Wind Power Sites of Bangladesh

Apratim Roy

Identification of the Situation of Renewable Energy Alternatives in the Criteria known by private sector investors (Case study: Iran)

Alireza Aslani, Marja Naaranoja, Erkki Antila, Mostafa Golbaba

MATLAB/Simulink Based Modeling of Photovoltaic Cell

Tarak Salmi, Mounir Bouzguenda, Adel Gastli, Ahmed Masmoudi

Maximal Wind Energy Tracing of Brushless Doubly-Fed Generator under Flux Oriented Vector Control

Hicham Serhoud, Djilani Benattous

Wind energy potential assessment in Chalus County in Iran

Mojtaba Nedaei

Derivation of Surface Roughness and Capacity Factor from Wind Shear Characteristics

Apratim Roy

Present Scenario of Renewable Energy in Bangladesh and a Proposed Hybrid System to Minimize Power Crisis in Remote Areas

Nahid -UR-Rahman Chowdhury, Syed Enam Reza, Tofaeel Ahamed Nitol, Abd-Al-Fattah IBNE Mahabub

Statistical Analysis of Wind Resources at Darling for Energy Production

Zaccheus Olaniyi Olaofe, Komla A. Folly

Input Current Control of Boost Converters using Current-Mode Controller Integrated with Linear Quadratic Regulator

Majid Abdullateef Abdullah, Chee Wei Tan, Abdul Halim Yatim, M. R. Al-Mothafar, Saleh M. Radaideh