Publications

Abstract not provided.

Abstract not provided.

Sandia National Laboratories and the National Renewable Energy Laboratory conducted a field campaign at the Scaled Wind Farm Technology (SWiFT) Facility using a customized scanning lidar from the Technical University of Denmark. The results from this field campaign were used to assess the predictive capability of computational models to capture wake dissipation and wake trajectory downstream of a wind turbine. The present work used large-eddy simulations of the wind turbine wake and a virtual SpinnerLidar to quantify the uncertainty of wind turbine wake position due to the line-of-sight sampling and probe volume averaging effects of the lidar. The LES simulations were of the SWiFT wind turbine at both a 0° and 30° yaw offset with a stable inflow. The wake position extracted from the simulated lidar sampling had an uncertainty of 2.8 m and m as compared to the wake position extracted from the full velocity field with 0° and 30° yaw offset, respectively. The larger uncertainty in calculated wake position of the 30° yaw offset case was due to the increased angle of the wake position relative to the axial flow direction and the resulting decrease in the line-of-sight velocity relative the axial velocity.

A method of wake determination is proposed where a wind turbine rotor wake is defined as the downstream wind velocity deficit region surrounded by increased turbulence creating a local maxima ring of turbulence. This definition creates a non-arbitrary wake boundary and provides criteria for more consistent wake identification and tracking than other methods. The definitive boundary allows for the separation of atmospheric regions and wake regions, facilitating the characterization of wake flow as it differs from atmospheric flow even at high wind turbine yaw offsets and in unstable atmospheric conditions. This definition can also be used to evaluate the effect of wakes on downstream turbines or wake control technologies such as wake steering. The proposed wake tracking method was shown to be robust for measurements at different inflow conditions and matched well with simulated, known wake positions.

To reduce the levelized cost of wind energy, wind plant controllers are being developed to improve overall performance by increasing energy capture. Previous work has shown that increased energy capture is possible by steering the wake around downstream turbines; however, the impact this steering action has on the loading of the turbines continues to need further investigation with operational data to determine overall benefit. In this work, rotor loading data from a wind turbine operating a wake steering wind plant controller at the DOE/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) Facility is evaluated. Rotor loading was estimated from fiber optic strain sensors acquired with a state-of-the-art Micron Optics Hyperion interrogator mounted within the rotor and synchronized to the open-source SWiFT controller. A variety of ground and operational calibrations were performed to produce accurate measurements of rotor blade root strains. Time- and rotational-domain signal processing methods were used to estimate bending moment at the root of the rotor blade. Results indicate a correlation of wake steering angle with: one-perrevolution thrust moment amplitude, two-per-revolution torque phase, and three-perrevolution torque amplitude and phase. Future work is needed to fully explain the correlations observed in this work and study additional multi-variable relationships that may also exist.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Sandia National Laboratories and the National Renewable Energy Laboratory conducted a field campaign at the Scaled Wind Farm Technology (SWiFT) Facility using a customized scanning lidar from the Technical University of Denmark. The results from this field campaign will support the validation of computational models to predict wake dissipation and wake trajectory offset downstream of a stand-alone wind turbine. In particular, regarding the effect of changes in the atmospheric boundary layer inflow state and turbine yaw offset. A key step in this validation process involves quantifying, and reducing, the uncertainty in the wake measurements. The present work summarizes the process that was used to calibrate the alignment of the lidar in order to reduce this source of uncertainty in the experimental data from the SWiFT field test.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This report presents the objectives, configuration, procedures, reporting , roles , and responsibilities and subsequent results for the field demonstration of the Sandia Wake Imaging System (SWIS) at the Sandia Scaled Wind Farm Technology (SWiFT) facility near Lubbock, Texas in June and July 2015.

Abstract not provided.