This paper demonstrates the observation of wind shear and veer directly from the operational response of a wind turbine equipped with blade load sensors. Two independent neural-based observers, one for shear and one for veer, are first trained using a machine-learning approach and then used to produce estimates of these two wind characteristics from measured blade load harmonics. The study is based on a dataset collected at an experimental test site featuring a highly instrumented 8 MW wind turbine, an IEC-compliant (International Electrotechnical Commission) met mast, and a vertical profiling lidar reaching above the rotor top. The present study reports the first demonstration of the measurement of wind veer with this technology and the first validation of shear and veer with respect to lidar measurements spanning the whole rotor height. Results are presented in terms of correlations, exemplary time histories, and aggregated statistical metrics. Measurements of shear and veer produced by the observers are very similar to the ones obtained with the widely adopted profiling lidar while avoiding its complexity and associated costs.

The planning and realization of wind energy projects requires an as accurate and precise wind resource estimation as possible. Standard procedures combine shorter on-site measurements with the application of numerical models. The uncertainties of the numerical data generated from these models are, particularly in complex onshore terrain, not just rather high but typically not well quantified. In this article we propose a methodology for using a single scanning Doppler wind lidar device to calibrate the output data of a numerical flow model and with this not just quantify but potentially also reduce the uncertainties of the final wind resource estimate. The scanning lidar is configured to perform Plan Position Indicator (PPI) scans and the numerical flow data are projected onto this geometry. Deviations of the derived from the recorded line-of-sight wind speeds are used to identify deficiencies of the model and as starting point for an improvement and tuning. The developed methodology is demonstrated based on a study for a site in moderately complex terrain in central Germany and using two rather different types of numerical flow models. The findings suggest that the use of the methodology and the introduced scanning wind lidar technology offers a promising opportunity to control the uncertainty of the applied flow models, which can otherwise only be estimated very roughly.

The validation of aeroelastic load models used for load calculations on wind turbines substantially increases the confidence in the accuracy and correctness of these models. In this contribution, we introduce a framework for the validation of these models, integrating a normalized area metric as an objective, quantifiable validation metric that considers the entire statistical distribution of a model and a benchmark and additionally enables a comparison of model accuracy between sensors of different physical units. The framework is applied to test cases that evaluate varying synthetic turbulent wind fields. Two test cases with a focus on turbulence parameters and abnormal shear conditions based on comprehensive wind measurements at the Testfeld Bremerhaven are used to demonstrate the workflow with four different members using IEC-compliant and measurement-derived wind field parameters, respectively. Along with these measurements, an uncertainty model for synthetic wind fields is introduced to quantify propagated wind measurement uncertainties associated with the measured boundary conditions during a validation campaign. The framework is presented as a straightforward and concise methodology to not only find but also quantify mismatches of load models. Major mismatches are found for wind fields associated with larger uncertainties in the mean wind field due to a reduced spatial resolution of measurements.

This paper demonstrates the observation of wind shear and veer directly from the operational response of a wind turbine equipped with blade load sensors. Two independent neural-based observers, one for shear and one for veer, are first trained using a machine learning approach, and then used to produce estimates of these two wind characteristics from measured blade load harmonics. The study is based on a data set collected at an experimental test site, featuring a highly-instrumented 8 MW wind turbine, an IEC-compliant met mast, and a vertical profiling lidar reaching above the rotor top. The present study reports the first demonstration of the measurement of wind veer with this technology, and the first validation of shear and veer with respect to lidar measurements spanning the whole rotor height. Results are presented in terms of correlations, exemplary time histories and aggregated statistical metrics. Measurements of shear and veer produced by the observers are very similar to the ones obtained with the widely adopted profiling lidar, while avoiding its complexity and associated costs.