An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)

The Weibull probability density function (PDF) has mostly been used to fit wind speed distributions for wind energy applications. The goodness of fit of the results depends on the estimation method that was used and the wind type of the analyzed area. In this paper, a study on a particular area (Galicia) was performed to test the performance of several fitting methods. The goodness of fit was evaluated by well-known indicators that use the wind speed or the available wind power density. However, energy production must be a critical parameter in wind energy applications. Hence, a fitting method that accounts for the power density distribution is proposed. To highlight the usefulness of this method, indicators that use energy production values are also presented.

[1]  Ssu-yuan Hu,et al.  Performance evaluation of pairing between sites and wind turbines , 2007 .

[2]  S. Mathew.  Wind Energy: Fundamentals, Resource Analysis and Economics , 2006 .

[3]  Carla Freitas de Andrade,et al.  Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil , 2012 .

[4]  Tariq Muneer,et al.  Critical evaluation of wind speed frequency distribution functions , 2010 .

[5]  Paul Giorsetto,et al.  Development of a New Procedure for Reliability Modeling of Wind Turbine Generators , 1983, IEEE Transactions on Power Apparatus and Systems.

[6]  J. Cidras,et al.  Wind speed simulation in wind farms for steady-state security assessment of electrical power systems , 1999 .

[7]  K. P. Pandey,et al.  Analysis of wind regimes for energy estimation , 2002 .

[8]  A. Brett,et al.  The Characteristics of Wind Velocity that Favor the Fitting of a Weibull Distribution in Wind Speed Analysis , 1984 .

[9]  J. C. Pidre,et al.  Probabilistic model for mechanical power fluctuations in asynchronous wind parks , 2003 .

[10]  A. Celik.  Assessing the suitability of wind speed probabilty distribution functions based on wind power density , 2003 .

[11]  P. Edwards,et al.  Level-crossing statistics of the horizontal wind speed in the planetary surface boundary layer. , 2001, Chaos.

[12]  Torbjörn Thiringer,et al.  Control by variable rotor speed of a fixed-pitch wind turbine operating in a wide speed range , 1993 .

[13]  Shyh-Jier Huang,et al.  Enhancement of Matching Turbine Generators With Wind Regime Using Capacity Factor Curves Strategy , 2009, IEEE Transactions on Energy Conversion.

[14]  S. Parsa,et al.  Wind power statistics and an evaluation of wind energy density , 1995 .

[15]  J. Cidrás,et al.  THE SOTAVENTO EXPERIMENTAL WIND PARK , 2022 .

[16]  José A. Carta,et al.  Influence of the level of fit of a density probability function to wind-speed data on the WECS mean power output estimation , 2008 .

[17]  J. Cidras,et al.  Control Algorithm for Coordinated Reactive Power Compensation in a Wind Park , 2008, IEEE Transactions on Energy Conversion.

[18]  V. Pérez-Muñuzuri,et al.  Comparative analysis between operational weather prediction models and QuikSCAT wind data near the Galician coast , 2008 .

[19]  S. H. Jangamshetti,et al.  Normalized Power Curves as a Tool for Identification of Optimum Wind Turbine Generator Parameters , 2001, IEEE Power Engineering Review.

[20]  J. Cidrás,et al.  Review of power curve modelling for wind turbines , 2013 .

[21]  Yeliz Mert Kantar,et al.  Analysis of some flexible families of distributions for estimation of wind speed distributions , 2012 .

[22]  J. Cidrás,et al.  PSCAD / EMTDC-Based Modeling and Flicker Estimation for Wind Turbines , 2009 .

[23]  E.F. El-Saadany,et al.  Wind Turbines Capacity Factor Modeling—A Novel Approach , 2009, IEEE Transactions on Power Systems.

[24]  T. Chang.  Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application , 2011 .

[25]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[26]  Ervin Bossanyi,et al.  Wind Energy Handbook , 2001 .

[27]  Erik Lundtang Petersen,et al.  The European Wind Atlas , 1985 .

[28]  T. W. Lambert,et al.  Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis , 2000 .

[29]  J. Montávez,et al.  The influence of the Weibull assumption in monthly wind energy estimation , 2008 .

[30]  Seyit Ahmet Akdağ,et al.  A new method to estimate Weibull parameters for wind energy applications , 2009 .

[31]  J. Cidras,et al.  Power fluctuations in an isolated wind plant , 2004, IEEE Transactions on Energy Conversion.

[32]  J. A. Carta,et al.  A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands , 2009 .

[33]  W. R. Hargraves,et al.  Nationwide assessment of potential output from wind-powered generators , 1976 .

[34]  M. J. Stevens,et al.  The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes , 1979 .