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)