Wind speed generation for dynamic analysis

A simple model to generate large band wind speed time sequences, especially easy to implement with a very reduced number of parameters, is presented. It is based on the calculation of a low-frequency and a high-frequency components. Low-frequency component with 1 h sample time is obtained from a random process based on a conditional probability density function. Using real data from two different wind farms in two different months of the year, it has been found that Weibull distribution centered in the current hourly mean value seems to represent well the 1 h conditional PDF in all cases, and the standard deviation of this conditional Weibull is more or less in the range 1–1.3 m s−1 independently of the season of the year or the location. Regarding to high-frequency component, low-frequency samples are used as initial and final values and, between them, the turbulence component values are inserted. For that, it has been used a stochastic process based on a Beta probability function and a simple rescaling procedure with two non-linear parameters, calculated in a recursive way. Unlike the usual modelling procedures presented in the literature, spectral power density functions are not used. This simplifies the implementation significantly. Ten second sample-time real speed wind data from two different wind farms have been used to validate the proposed high-frequency model, obtaining excellent results. A thorough revision of the main models found in the literature to produce wind speed time sequences for dynamic analysis is performed in the paper. Copyright © 2017 John Wiley & Sons, Ltd.

[1]  G. Strbac,et al.  Value of Bulk Energy Storage for Managing Wind Power Fluctuations , 2007, IEEE Transactions on Energy Conversion.

[2]  D. Weisser,et al.  A wind energy analysis of Grenada: an estimation using the 'Weibull' density function , 2003 .

[3]  Masanobu Shinozuka,et al.  Stochastic Methods in Wind Engineering , 1989 .

[4]  T. Ekelund.  Speed control of wind turbines in the stall region , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[5]  H. Polinder,et al.  General Model for Representing Variable-Speed Wind Turbines in Power System Dynamics Simulations , 2002, IEEE Power Engineering Review.

[6]  J. Kaimal,et al.  Spectral Characteristics of Surface-Layer Turbulence , 1972 .

[7]  M. Shinozuka,et al.  Digital simulation of random processes and its applications , 1972 .

[8]  Masanobu Shinozuka,et al.  Parametric Study of Wind Loading on Structures , 1973 .

[9]  R. Billinton,et al.  A simplified wind power generation model for reliability evaluation , 2006, IEEE Transactions on Energy Conversion.

[10]  W. E. Leithead,et al.  Role and objectives of control for wind turbines , 1991 .

[11]  A. Fabbri,et al.  Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market , 2005, IEEE Transactions on Power Systems.

[12]  A. Davenport.  The spectrum of horizontal gustiness near the ground in high winds , 1961 .

[13]  Zuwei Yu,et al.  Fractional Weibull wind speed modeling for wind power production estimation , 2009, 2009 International Conference on Sustainable Power Generation and Supply.

[14]  T. Kármán.  Progress in the Statistical Theory of Turbulence , 1948 .

[15]  J.A.P. Lopes,et al.  On the optimization of the daily operation of a wind-hydro power plant , 2004, IEEE Transactions on Power Systems.

[16]  E. Welfonder,et al.  Development and experimental identification of dynamic models for wind turbines , 1997 .

[17]  Anjan Bose,et al.  Stability Simulation Of Wind Turbine Systems , 1983, IEEE Transactions on Power Apparatus and Systems.

[18]  C. Nichita,et al.  Large band simulation of the wind speed for real time wind turbine simulators , 2002 .

[19]  I. V. D. Hoven.  POWER SPECTRUM OF HORIZONTAL WIND SPEED IN THE FREQUENCY RANGE FROM 0.0007 TO 900 CYCLES PER HOUR , 1957 .

[20]  Daniel S. Kirschen,et al.  Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration , 2009, IEEE Transactions on Power Systems.

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