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Tytuł artykułu

Evaluation of wind energy potential for four sites in Ireland using the Weibull distribution model

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Wind speed is receiving greater attention in the design and study of wind energy conversion systems (WECS). Using meteorological data, this paper studies the availability of wind energy potential at four sites in Ireland: Malin Head, Dublin Airport, Belmullet and Mullingar. An analysis is made of mean wind speed data collected at a height of 50 m above ground level at each site over a period of seven years. A two parameter Weibull distribution model is used to analyze wind speed pattern variations. Weibull parameters are calculated by the Least Squares Method (LSM). The results relating to wind energy potential are given in terms of the density function. Analysis shows that coastal sites of Ireland such as Malin Head, Dublin Airport and Belmullet have good wind power potential.
Rocznik
Strony
48--53
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
  • Department of Electrical Engineering, PVG’s College of Engineering and Technology, Pune, Maharashtra, India
  • Department of Physics, Nowrosjee Wadia College, Pune, Maharashtra, India
Bibliografia
  • [1] M. Tingem, M. Rivington, S. A. Ali, J. Colls, Assessment of the climgen stochastic weather generator at cameroon sites, African Journal of Environmental Science and Technology 1 (4) (2007) 86-92.
  • [2] S. C. Pryor, R. J. Barthelmie, Climate change impacts on wind energy: a review, Renewable and Sustainable Energy Reviews 14 (2010) 430-437.
  • [3] V. Akhmatov, Influence of wind direction on intense power fluctuations in large offshore wind farms in the north sea, Wind Eng. 3 (1) (2007) 59-64.
  • [4] R. C. Bansal, A. F. Zobaa, R. K. Saket, Some issues related to power generation using wind energy conversion systems: An overview, International Journal Emerging Electrical Power System 3 (2) (2005) 1-19.
  • [5] C. Wan, Z. Xu, P. Pinson, Z. Y. Dong, K. P. Wong, Probabilistic forecasting of wind power generation using extreme learning machine, IEEE Transactions on Power Systems 29 (3) (2014) 1033-1044.
  • [6] S. M.Weekes, A. S. Tomlin, Low-cost wind resource assessment for small-scale turbine installations using site pre-screening and short-term wind measurements, Renewable Power Generation, IET 8 (4) (2014) 348–358.
  • [7] T. J. Chang, Y. L. Tu, Evaluation of monthly capacity factor of wecs using chronological and probabilistic wind speed data: A case study of taiwan, Renewable Energy 32 (2) (2007) 1999-2010.
  • [8] S. J. Huang, H. H. Wan, Enhancement of matching turbine generators with wind regime using capacity factor curves stratergies, IEEE Transaction Energy Conversion 24 (2) (2009) 551-553.
  • [9] J. W. Taylor, P. E. McSharry, R. Buizza, Wind power density forecasting using ensemble predictions and time series models, IEEE Trans. Energy Convers. 24 (3) (2009) 775-782.
  • [10] A. Khosravi, S. Nahavandi, D. Creighton, Prediction intervals for short-term wind farm power generation forecasts, IEEE Trans. Sustain. Energy 24 (3) (2013) 602-610.
  • [11] R. D. Prasad, R. C. Bansal, M. Sauturaga, Wind energy analysis for vadravadra site in fiji islands: A case study, IEEE Transaction Energy Conversion 24 (3) (24) 71537-1543.
  • [12] J. A. Carta, P. Ramiez, Influence of the data sampling interval in the estimation of the parameters of the weibull wind speed probability density distribution: a case study, Energy Conversion Management 46 (15) (2005) 2419 – 2438.
  • [13] M. A. Matos, R. J. Bessa, Setting the operating reserve usi1ng probabilistic wind power forecasts, IEEE Trans. Power Syst. 26 (2) (2011) 594-603.
  • [14] R. J. Bessa, V. Miranda, A. Botterud, Z. Zhou, J. Wang, Timeadaptive quantile copula for wind power probabilistic forecasting, Renew. Energy 40 (1) (2012) 29-39.
  • [15] G. Sideratos, N. D. Hatziargyriou, Probabilistic wind power forecasting using radial basis function neural networks, IEEE Trans. Power Syst. 27 (4) (2012) 1788-1796.
  • [16] H. Bludszuweit, J. A. Dominguez-Navarro, A. Llombart, Statistical analysis of wind power forecast error, IEEE Trans. Power Syst. 23 (3) (2008) 983-991.
  • [17] K. Bhaskar, S. N. Singh, Awnn-assisted wind power forecasting using feed-forward neural network, IEEE Trans. Sustain. Energy 3 (2) (2012) 306-315.
  • [18] A. N. Celick, A statistical analysis of wind density based on the weibull and rayleigh models at the southern region of turkey, Energy Conversion Management 29 (4) (2004) 593-604.
  • [19] S. G. Jamdade, P. G. Jamdade, Extreme value distribution model for analysis of wind speed data for four locations in ireland, International Journal of Advanced Renewable Energy Research 1 (5) (2012) 254-259.
  • [20] S. G. Jamdade, P. G. Jamdade, Analysis of wind speed data for four locations in ireland based on weibull distribution’s linear regression model, International Journal of Renewable Energy Research 2 (3) (2012) 451-455.
  • [21] R. J. Bessa, V. Miranda, A. Botterud, J. Wang, E. M. Constantinescu, Time adaptive conditional kernel density estimation for wind power forecasting, IEEE Trans. Sustain. Energy 3 (4) (2012) 660-669.
  • [22] A. M. Foley, P. Leahy, A. Marvuglia, E. J. McKeogh, Current methods and advances in forecasting of wind power generation, Renewable Energy 37 (1) (2012) 1-8.
  • [23] P. Pinson, G. Kariniotakis, Conditional prediction intervals of wind power generation, IEEE Trans. Power Syst. 25 (4) (2012) 1845-1856.
  • [24] A. Khosravi, S. Nahavandi, D. Creighton, Prediction intervals for short-term wind farm power generation forecasts, IEEE Trans. Sustain. Energy 4 (3) (2013) 602-610.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-dd51a10f-6714-49fa-800c-5bcbcf386b79
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