Statistical analysis of wind in two wind farms in Rio Grande do Norte (Brazil)

The state of Rio Grande do Norte is a leader in wind power generation in the country with more than 150 wind farms in operation and 4 GW of installed capacity. Although the wind energy industry continues to grow, there are still very few papers that analyze wind behavior at heights above 80 m, which is typically the height of a wind turbine rotor. In this paper, a 95 m wind behavior analysis was carried out in two wind farms in the city of Parazinho-RN on monthly, seasonal and hourly scale, with the goal of identifying which periods of 2017 were more favorable for wind power production in the region. The wind speed and direction data used in this study were collected by a Thies First Class high precision cup anemometer and a Thies Compact wind direction indicator, corresponding to the hourly average of the measurements performed every second and integrated in intervals of 10 minutes in the period from January 1 to December 31, 2017. According with the results presented in this paper, it was verified that spring is the season of 2017 that presents the highest average monthly wind speeds, with maximum peak in the month of September, while lower speeds vary during the March-April-May quarter. The diurnal period between 10:00 am and 5:00 pm local time is the one with the most frequently wind speed ≥ 10 m.s^-1. Wind direction varies from east to south, with predominance of east and southeast directions, in about 80% of the time. During the quarters that correspond to summer and fall, Weibull distributions are more concentrated around 6.5 and 7 m.s^-1, indicating that in this period of the year there is a greater probability of occurrence of lower average speeds, implying in lower productivity for wind energy. The opposite is observed in winter and spring, with the exception of June, where the distributions are more concentrated around speeds ≥ 8 m.s^-1 and there is a higher probability of higher average speeds to occur, showing that this was the best period of the year 2017 for wind power generation. (author)