flasc.model_fitting.floris_tuning.sweep_deflection_parameter_for_total_uplift#
- flasc.model_fitting.floris_tuning.sweep_deflection_parameter_for_total_uplift(parameter, value_candidates, df_scada_baseline_in, df_scada_wakesteering_in, fm_in, ref_turbines, test_turbines, yaw_angles_baseline=None, yaw_angles_wakesteering=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]#
Sweep values of the deflection parameter in FLORIS and compare to SCADA.
Comparison is made wrt overall uplift.
- Parameters:
parameter (str) -- The parameter to sweep
value_candidates (list) -- The values to sweep
df_scada_baseline_in (DataFrame) -- The baseline SCADA data
df_scada_wakesteering_in (DataFrame) -- The wake steering SCADA data
fm_in (floris.tools.Floris) -- The FLORIS model
ref_turbines (list) -- The reference turbines
test_turbines (list) -- The test turbines
yaw_angles_baseline (np.ndarray) -- The yaw angles for the baseline
yaw_angles_wakesteering (np.ndarray) -- The yaw angles for the wake steering
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:
A typle (np.ndarray, np.ndarray) where the first element is the FLORIS total uplifts and the second element is the SCADA total uplifts