Presentations

Abstract

In complex terrain, flow distortion and spatial averaging effects introduce additional uncertainties for lidar measurements of wind speed and turbulence intensity (TI). Flow models and new wind field reconstruction techniques can be used to reduce these uncertainties. In this research, we show how three different classes of flow models can be used during different phases of energy yield assessment (EYA) to quantify and reduce measurement uncertainty and to support bankable lidar measurement campaigns in complex terrain.   This work is based on four measurement campaigns carried out in the United Kingdom at sites with different terrain characteristics. Each site was equipped with a vertically profiling lidar and a co-located meteorological mast. The first flow model is a CFD-surrogate model to roughly quantify lidar uncertainty due to terrain complexity throughout the project areas before installation. The model is used to determine where the lidar should be installed, and how the lidar data should be processed: no correction, simplified linear correction, or correction using a commercial CFD flow model. The predicted errors from the surrogate model agree within 2% of the observed biases.   At each site, the simplified flow model is applied and compared to the co-located reference mast. For all four sites, at all heights, the linear flow model reduces wind speed biases from a range of -2.11% to -0.77% to -0.2% to 0.8%. Applying a full CFD model to the lidar measurements further reduces the error to between -0.3% to 0.6%. For each approach, the uncertainty of the correction technique is derived following the IEC 61400-15-2 CDV, yielding additional model uncertainties of approximately 0.5% applied to the corrected data.   When combining the linear or full CFD corrections with a new physics-based TI reconstruction method, the TI bias decreased from 16.3% to 5%, while the mean absolute error was reduced from 18% to 11%. The resulting Characteristic TI curves show excellent agreement with the mast measurements, and the TI distributions agree more closely with the reference. The share of wind speed bins meeting the DNV-RP 0661 acceptance criteria for TI increased from 11% to 96%  This research demonstrates that complex terrain workflows leveraging appropriate flow models at different phases of development can reliably predict and reduce uncertainties for lidar-based wind speed and TI measurements.