Publications

Visualizing Wind Farm Wakes Using SCADA Data

Martin, Shawn; Westergaard, Carsten W.; White, Jonathan; Karlson, Benjamin K.

As wind farms scale to include more and more turbines, questions about turbine wake interactions become increasingly important. Turbine wakes reduce wind speed and downwind turbines suffer decreased performance. The cumulative effect of the wakes throughout a wind farm will therefore decrease the performance of the entire farm. These interactions are dynamic and complicated, and it is difficult to quantify the overall effect of the wakes. This problem has attracted some attention in terms of computational modelling for siting turbines on new farms, but less attention in terms of empirical studies and performance validation of existing farms. In this report, Supervisory Control and Data Acquisition (SCADA) data from an existing wind farm is analyzed in order to explore methods for documenting wake interactions. Visualization techniques are proposed and used to analyze wakes in a 67 turbine farm. The visualizations are based on directional analysis using power measurements, and can be considered to be normalized capacity factors below rated power. Wind speed measurements are not used in the analysis except for data pre-processing. Four wake effects are observed; including wake deficit, channel speed up, and two potentially new effects, single and multiple shear point speed up. In addition, an attempt is made to quantify wake losses using the same SCADA data. Power losses for the specific wind farm investigated are relatively low, estimated to be in the range of 3-5%. Finally, a simple model based on the wind farm geometrical layout is proposed. Key parameters for the model have been estimated by comparing wake profiles at different ranges and making some ad hoc assumptions. A preliminary comparison of six selected profiles shows excellent agreement with the model. Where discrepancies are observed, reasonable explanations can be found in multi-turbine speedup effects and landscape features, which are yet to be modelled.