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In several papers, the importance of the atmospheric flow in the wake development of wind turbines (WT) has been pointed out, making it clear that it is necessary to have long-term on-field observations for an appropriate description of the wake development, its structure and dynamics. This work presents a statistical approach to wake meandering, y w , and the relationship that this behavior has with the incoming wind conditions and neighboring wakes. The work was developed in the framework of the French project SMARTEOLE. The study is based on a 7-month measurement campaign in which a pulsed scanning LiDAR system was used. The ground based LiDAR, measures the flow field in a segment such that the wake of two wind turbines can be captured quasi-horizontally. The analysis filters the incoming wind conditions according to the thermal stability, wind direction and wind velocity at hub height; therefore, the wakes that are developed in periods with similar wind conditions are expected to be analogous, hence meandering can be tracked and statistically analyzed. A well-defined wake evolution was found and the uncertainty analysis made on the wake meandering uncovered some interesting characteristics, including the number of samples required to reach a statistical uncertainty on the mean wake position between 2 × 10-2 D and 8 × 10-2 D for a confidence interval of 95%.

Wake meandering and its relationship with the incoming wind characteristics: a statistical approach applied to long-term on-field observations