Wind and Structures

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Mathematical modeling of wind power estimation using multiple parameter Weibull distribution

  • Chalamcharla, Seshaiah C.V. (Department of Mathematics, Sri Ramakrishna Engineering College) ;
  • Doraiswamy, Indhumathy D. (Department of Mathematics, Sri Ramakrishna Engineering College)
  • Received : 2015.12.04
  • Accepted : 2016.08.13
  • Published : 2016.10.25
⟨ Previous Next ⟩

Abstract

Nowadays, wind energy is the most rapidly developing technology and energy source and it is reusable. Due to its cleanliness and reusability, there have been rapid developments made on transferring the wind energy systems to electric energy systems. Converting the wind energy to electrical energy can be done only with the wind turbines. So installing a wind turbine depends on the wind speed at that location. The expected wind power can be estimated using a perfect probability distribution. In this paper Weibull and Weibull distribution with multiple parameters has been used in deriving the mathematical expression for estimating the wind power. Statistically the parameters of Weibull and Weibull distribution are estimated using the maximum likelihood techniques. We derive a probability distribution for the power output of a wind turbine with given rated wind speeds for the regions where the wind speed histograms present a bimodal pdf and compute the first order moment of this distribution.

Keywords

Acknowledgement

Supported by : Sri Ramakrishna Engineering College

References

  1. Carta, J.A. and Ramirez, P. (2007), "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions", Renew. Energy, 32, 518-531. https://doi.org/10.1016/j.renene.2006.05.005
  2. Celik, A.N. (2004), "On the distribution parameters used in assessment of the suitability of wind speed probability density functions", Energy Convers. Management, 45(11-12), 1735-1747. https://doi.org/10.1016/j.enconman.2003.09.027
  3. Chang, T.J. and Tu, Y.L. (2007), "Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data : A case of Taiwan" , Renew. Energy, 32(12), 1999-2010. https://doi.org/10.1016/j.renene.2006.10.010
  4. Cook, N.J. (2001), "Discussion on modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis", J. Wind Eng. Ind. Aerod., 89(10), 867-869. https://doi.org/10.1016/S0167-6105(00)00088-X
  5. Garcia, A. and Torres, J.L. et al (1998), "Fitting wind speed distributions ; a case study", Sol Energy, 62, 139-144. https://doi.org/10.1016/S0038-092X(97)00116-3
  6. He, Y.C, Chan, P.W. and Li, Q.S. (2013), "Wind profiles of tropical cyclones as observed by Doppler Wind profiler and anemometer", Wind Struct., 17(4), 419-433. https://doi.org/10.12989/was.2013.17.4.419
  7. Hong, H.P. and Li, S.H. (2014), "Plotting positions and approximating first two moments of order statistics for Gumbel distribution : estimating quantiles of wind speed", Wind Struct., 19(4), 371-387. https://doi.org/10.12989/was.2014.19.4.371
  8. Jaramillo, O.A. and Borja, M.A. (2004), "Wind speed analysis in La Ventosa, Mexico : a bimodal probability distribution case", Renew Energy, 29, 1613-1630. https://doi.org/10.1016/j.renene.2004.02.001
  9. Jowder, F.A.L. (2009), "Wind power analysis and site matching of Wind turbine Generators in Kingdom of Bahrain", Appl. Energy., 86(4), 538-545. https://doi.org/10.1016/j.apenergy.2008.08.006
  10. Kolhe, M., Aghossou, K., Hamelin, J. and Bose, T.k. (2003), "Analytical model for predicting the performance of photovoltaic array coupled with wind turbine in a stand alone renewable energy system based on hydrogen", Renew Energy, 28, 727-742. https://doi.org/10.1016/S0960-1481(02)00107-6
  11. Park , J. (1981), "The wind power book", Cheshire Books .
  12. Patel, M.R. (2006), "Wind and solar power systems- design, analysis and operation", 2nd Ed., New York: CRC Press.
  13. Rehman, S. and Ahmad, A. ( 2004), "Assesment of wind energy and capacity factor calculations for two locations of Saudi Arabia", Proceedings of the second BSME-ASME international conference on thermal engineering, 2, Dhaka, Bangladesh , BUET , Jan ,906-911.
  14. Sedghi, M., Hannani, S.M. and Boroushaki, M. (2015), "Estimation of Weibull parameters for wind energy application in Iran", Wind Struct., 21(2), 203-221. https://doi.org/10.12989/was.2015.21.2.203
  15. Sen, Z. (2003), "A short physical note on a new wind power formulation", Renew Energy, 28, 2379-2382. https://doi.org/10.1016/S0960-1481(03)00132-0
  16. Seshaiah, C.V. and Sukkiramathi, K. (2016), "A Mathematical model to estimate the wind power using three parameter Weibull distribution", Wind Struct., 22(4), 393-408. https://doi.org/10.12989/was.2016.22.4.393

Cited by

  1. The new odd-burr rayleigh distribution for wind speed characterization vol.28, pp.6, 2019, https://doi.org/10.12989/was.2019.28.6.369
  2. Mathematical representation of Wind Turbine Power reliability vol.1921, pp.None, 2016, https://doi.org/10.1088/1742-6596/1921/1/012126