Abstract
Abstract. An automated wind turbine wake characterization algorithm has been developed and applied to a data set of over 19 000 scans measured by a ground-based scanning Doppler lidar at Perdigão, Portugal, over the period January to June 2017. Potential wake cases are identified by wind speed, direction and availability of a retrieved free-stream wind speed. The algorithm correctly identifies the wake centre position in 62 % of possible wake cases, with 46 % having a clear and well-defined wake centre surrounded by a coherent area of lower wind speeds while 16 % have split centres or multiple lobes where the lower wind speed volumes are no longer in coherent areas but present as two or more distinct areas or lobes. Only 5 % of cases are not detected; the remaining 33 % could not be categorized either by the algorithm or subjectively, mainly due to the complexity of the background flow. Average wake centre heights categorized by inflow wind speeds are shown to be initially lofted (to two rotor diameters, D, downstream) except when the inflow wind speeds exceed 12 ms−1. Even under low wind speeds, by 3.5 D downstream of the wind turbine, the mean wake centre position is below the initial wind turbine hub height and descends broadly following the terrain slope. However, this behaviour is strongly linked to the hour of the day and atmospheric stability. Overnight and in stable conditions, the average height of the wake centre is 10 m higher than in unstable conditions at 2 D downstream from the wind turbine and 17 m higher at 4.5 D downstream.
Highlights
1.1 Motivation and objectivesTemporal and spatial inhomogeneity of the flow in complex terrain (Kaimal and Finnigan, 1994) increases uncertainty in modelling and measurements of wind speed, turbulence intensity, etc., for wind resource assessment and turbine operating conditions (Sanz Rodrigo et al, 2017)
The Perdigão region was selected for the field experiment in part due to the presence of two ridges of approximately equal height and for the prevailing bimodal wind direction, which means that the flow is frequently oriented perpendicular to the ridges
Higher wind speeds are observed during March and April, but the high frequency of northeasterly winds during April meant that relatively few wakes could be observed, despite the prevalence of wind turbine operating wind speeds
Summary
Temporal and spatial inhomogeneity of the flow in complex terrain (Kaimal and Finnigan, 1994) increases uncertainty in modelling and measurements of wind speed, turbulence intensity, etc., for wind resource assessment and turbine operating conditions (Sanz Rodrigo et al, 2017). They have implications for wind turbine wake generation and propagation (i.e. formation and recovery of the volume of disturbed air that passes through the wind turbine rotor; Barthelmie et al, 2013). The objectives of this paper are to (i) describe observational challenges of detection and characteri-
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