Probabilistic identification of turbines facing high and low wind speeds in a wind farm

Due to growing number of wind turbines (WT) within a wind farm (WF) scheduling of maintenance of turbines within the WF becomes challenging issue. Also, in instances when wind energy curtailments are required it can be difficult to decide which turbines should be shut down first. In this paper, a probabilistic methodology is presented to identify WT in the WF that face higher and lower wind speeds during the year. Since this calculation is dependant on WF layout, location of WF and position of WTs inside the WF probabilistic site analysis is performed, along with turbine clustering, after determining wind speed approaching each turbine by using a detailed wake effect model. The approach presented can be applied to a wind farm of any size and layout at any location.