1B.3 An Error Reduction Algorithm to Improve Lidar Turbulence Estimates for Wind Energy

Monday, 20 June 2016: 9:00 AM
Bryce (Sheraton Salt Lake City Hotel)
Jennifer F. Newman, National Renewable Energy Laboratory, Golden, CO; and A. Clifton

Currently, cup anemometers on meteorological (met) towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability. However, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install met towers at potential sites. As a result, remote sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. While lidars can accurately estimate mean wind speeds and wind directions, there is still a large amount of uncertainty surrounding the measurement of turbulence with lidars. This uncertainty in lidar turbulence measurements is one of the key roadblocks that must be overcome in order to replace met towers with lidars for wind energy applications.

In this talk, a model for reducing errors in lidar turbulence estimates is presented. Techniques for reducing errors from instrument noise, volume averaging, and variance contamination are combined in the model to produce a corrected value of the turbulence intensity (TI), a commonly used parameter in wind energy. In the next step of the model, machine learning techniques are used to further decrease the error in lidar TI estimates.

The model was tested on pulsed Doppler lidar measurements from sites in Oklahoma and Colorado and output from the model was compared to TI estimates from instruments on collocated met towers at the sites. The model performed well for the majority of atmospheric conditions, with the exception of strongly unstable periods, when variance contamination created large errors in the initial lidar TI estimates. As a result, different techniques for reducing variance contamination in lidar data were explored to determine the best way to address variance contamination in the model. Techniques include spectral fitting and the use of Taylor's frozen turbulence hypothesis to estimate the change in vertical velocity across the lidar scanning circle.

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