Journal of Wind Engineering and Industrial Aerodynamics
Extreme value frequency analysis of wind data from Isfahan, Iran
Introduction
Extreme wind speed frequency estimation is usually important in many fields of environmental studies such as climatology, hydrology, developing wind energy facilities, agricultural management and structure designing (Lopez, 1998; Gomes et al., 2003). Many investigators have tried to fit different frequency distributions to wind data. The families of extreme value distributions are also good candidates for extreme wind frequency analysis (Rohan and Dale, 1987). Celik (2003) used Weibull distribution to estimate wind energy output of large- and small-scale turbines. Recently, Pandey et al. (2003) fitted Generalized Pareto distribution to peak-over-threshold extreme wind speed through bootstrapping. Holmes and Moriarty (1999) also suggested generalized Pareto distribution to fit to extreme wind speed in Australia. This study aims to find the best frequency distribution to the annual maximum wind speed of Isfahan station in Central Iran and merging wind speed and duration with wind frequency.
Section snippets
Wind speed duration
The recorded graph at each station contains monthly graphs that show wind speed and duration. To extract wind intensity (speed) duration from these graphs, one has to use especial rulers that have the same width of 32 mm as the wind graphs. The smallest extractable duration on these graphs is 1 h. Because we have to derive durations smaller than 1 h (for example 30 and 15 min) for practical reasons, we used simple rulers to derive smaller wind durations from wind graphs. As the wind speeds are
Preliminary processing of data
It would be reasonable to check the data for randomness, outliers, homogeneity and independency. A number of non-parametric tests were applied, such as Run Test for randomness, Grubbs and Beck test for outliers, Wald–Wolfowitz test for independent and Mann–Whitney U test for homogeneity. The annual maximum wind speed time series passed all the above tests successfully in 95% significant level.
Frequency analysis
In this study, we apply GEV distribution using FREQ program in the software package MATLAB (1999)
Conclusion
In this study, GEV distribution was fitted to annual maximum wind speed and extreme value Type I distribution or Gumbel distribution, with k=0, was found to fit to data series better than other GEV distribution. Plotting the predicted wind speed quantiles at different return periods against averaging wind duration give a plot that can be used to estimate wind speed at different durations and return periods. This will help the planners and designers to derive desire wind speed and duration for
Acknowledgment
The authors gratefully acknowledge Prof. Khaled Hamed from Cairo University for providing computer program used for frequency analysis.
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