An important aspect in the design of wind farms and consequently in wind power production, is the sensitivity of wind turbines to wind gusts. This study aims at estimating the probability of occurrence of daily wind gusts for different atmospheric circulation regimes. The analysis is performed for a 30-year period (1979-2009) for the Hellinikon station in Athens, Greece. The proposed methodology estimates the probability of exceeding a wind gust speed value from the mean daily wind speed observations for a given atmospheric circulation pattern. The large-scale atmospheric circulation classification is based on a two-stage clustering approach, using Self-Organizing Maps as a clustering methodology. The classification is based on the Sea Level Pressure, the geopotential at 500hPa, the zonal and meridional wind components at 10m and at 850hPa, the specific humidity at 700hPa and the air and dew-point temperature at 2m. Following the gust factor method, different statistical models are trained for each of the eight identified atmospheric modes. The results demonstrate the suitability of the lognormal distribution for associating wind gusts and mean wind speeds. The adopted gust factor method provides accurate estimates of daily wind gust speeds and that the atmospheric circulation enhances the precision of the statistical models.
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