flasc.model_fitting.floris_tuning.sweep_wd_std_for_er

flasc.model_fitting.floris_tuning.sweep_wd_std_for_er#

flasc.model_fitting.floris_tuning.sweep_wd_std_for_er(value_candidates, df_scada_in, df_approx_, ref_turbines, test_turbines, yaw_angles=None, wd_step=2.0, wd_min=0.0, wd_max=360.0, ws_step=1.0, ws_min=0.0, ws_max=50.0, bin_cols_in=['wd_bin', 'ws_bin'], weight_by='min', df_freq=None, remove_all_nulls=False)[source]#

Determine the best-fit wd_std for FLORIS by comparison with energy ratio plots.

TODO: Reimplement that comparison only takes place when FLORIS value is below some threshold

Parameters:

  • value_candidates (list) -- The values to sweep

  • df_scada_in (DataFrame) -- The SCADA data

  • df_approx (DataFrame) -- The FLORIS approximation data

  • ref_turbines (list) -- The reference turbines

  • test_turbines (list) -- The test turbines

  • yaw_angles (np.ndarray) -- The yaw angles

  • wd_step (float) -- The wind direction step

  • wd_min (float) -- The minimum wind direction

  • wd_max (float) -- The maximum wind direction

  • ws_step (float) -- The wind speed step

  • ws_min (float) -- The minimum wind speed

  • ws_max (float) -- The maximum wind speed

  • bin_cols_in (list) -- The bin columns

  • weight_by (str) -- The weight method. Can be 'min' or 'sum'. Default is 'min'.

  • df_freq (DataFrame) -- The frequency data

  • remove_all_nulls (bool) -- Remove all nulls. Default is False.

Returns:

The first element is the FLORIS energy ratio errors

and the second element is the dataframes.

Return type:

A tuple (np.ndarray, list)

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