We propose a comprehensive methodology to incorporate filtering, interpolation and uncertainties estimation in the processing of scanning wind lidar data. A full-scale wake measurement campaign has been carried out at an 8-MW prototype wind turbine in Bremerhaven, Germany, to apply and demonstrate the procedure. The filtering and interpolation of the scanning lidar data results in an average scan that fully covers the turbine rotor swept area. Once the filtered scans are processed, all observations are clustered in a capture matrix, where each bin can be ensemble-averaged according to wind direction, atmospheric stability and turbulence intensity. The final bin-averaged results were compared to an engineering wake model projected onto the lidar’s beam directions, along with an uncertainty model which combines the contributions both from observations and simulation inputs. The results reveal the overall wake characteristics and the ability of the selected model to predict the wake under neutral conditions, with RMSE = 0.532 ms−1. Under stable conditions the model overestimates the wake deficit with greater RMSE = 1.108 ms−1. Nevertheless, we show that this post-processing methodology is effective and can be further applied in other long-range scanning lidar datasets, e.g., for offshore cluster wakes or blockage effect studies.
Scanning lidars are increasingly being used for new measurement applications, such as wake measurements. This type of measurement requires an uncertainty quantification, and the setup of such instruments should ideally be planned to minimize the overall measurement uncertainty. Based on a previous experimental study, we quantify the overall uncertainty for a scanning lidar wake measurement campaign by analyzing the pointing accuracy for distinct configuration scenarios. The selected scenarios are based on a single ground-based scanning lidar measuring the downstream flow, a scanning lidar mounted on the turbine’s nacelle and, finally, two scanning lidars measuring dual-Doppler points within the wake. The results for each configuration are presented in terms of the overall uncertainty and measurement outputs provided from these setups. In a case study using the experimental dataset to compare ground- and nacelle-based configurations, we find that – under slightly stable conditions at 5D downstream – the overall radial wind speed uncertainty, normalized by reference wind speed without counting the sample size uncertainty, decreases on average from 2.78% for the ground-based lidar to 1.33% for the nacelle-based setup. Another advantage of the nacelle-based setup is the potentially larger sample size, as it can measure multiple downstream distances at once. The dual-lidar configuration has advantages in providing horizontal wind speed and direction instead of radial wind speed components only, hence reducing uncertainties associated with wind direction. However, this comes with the cost of fewer measured points to be compared with an engineering wake model and less flexibility to position the lidars, since their relative spatial position impacts the uncertainty. In summary, the presented methodology can benefit the best selection of scanning lidar configuration for a particular application, here demonstrated for wake measurements, proving the user with an estimation of the overall measurement uncertainty and, most importantly, the sensitivity of each parameter.
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