flasc.utilities.floris_tools.add_gaussian_blending_to_floris_approx_table

flasc.utilities.floris_tools.add_gaussian_blending_to_floris_approx_table#

flasc.utilities.floris_tools.add_gaussian_blending_to_floris_approx_table(df_fi_approx, wd_std=3.0, pdf_cutoff=0.995)[source]#

Add Gaussian blending to the precalculated FLORIS solutions.

This function applies a Gaussian blending across the wind direction for the predicted turbine power productions from FLORIS. This is a post-processing step and achieves the same result as evaluating FLORIS directly with the UncertainFlorisModel module. However, having this as a postprocess step allows for rapid generation of the FLORIS solutions for different values of wd_std without having to re-run FLORIS.

Parameters:

  • df_fi_approx (pd.DataFrame) -- Pandas DataFrame with precalculated FLORIS solutions, typically generated using flasc.utilities.floris_tools.calc_floris_approx_table().

  • wd_std (float, optional) -- Standard deviation of the Gaussian blur that is applied across the wind direction in degrees. Defaults to 3.0.

  • pdf_cutoff (float, optional) -- Cut-off point of the probability density function of the Gaussian curve. Defaults to 0.995 and thereby includes three standard deviations to the left and to the right of the evaluation.

Returns:

Pandas DataFrame with Gaussian-blurred precalculated

FLORIS solutions. The DataFrame typically has the columns "wd", "ws", "ti", and "pow_000" until "pow_{nturbs-1}", with nturbs being the number of turbines.

Return type:

pd.DataFrame

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