windpowerlib.power_curves.smooth_power_curve¶
-
windpowerlib.power_curves.
smooth_power_curve
(power_curve_wind_speeds, power_curve_values, block_width=0.5, wind_speed_range=15.0, standard_deviation_method='turbulence_intensity', mean_gauss=0, **kwargs)[source]¶ Smooths the input power curve values by using a Gauss distribution.
The smoothing serves for taking the distribution of wind speeds over space into account.
Parameters: - power_curve_wind_speeds (pandas.Series or numpy.array) – Wind speeds in m/s for which the power curve values are provided in power_curve_values.
- power_curve_values (pandas.Series or numpy.array) – Power curve values corresponding to wind speeds in power_curve_wind_speeds.
- block_width (float) – Width between the wind speeds in the sum of equation (1). Default: 0.5.
- wind_speed_range (float) – The sum in the equation below is taken for this wind speed range below and above the power curve wind speed. Default: 15.0.
- standard_deviation_method (string) – Method for calculating the standard deviation for the Gauss distribution. Options: ‘turbulence_intensity’, ‘Staffell_Pfenninger’. Default: ‘turbulence_intensity’.
- mean_gauss (float) – Mean of the Gauss distribution in
gauss_distribution()
. Default: 0.
Other Parameters: turbulence intensity (float, optional) – Turbulence intensity at hub height of the wind turbine, wind farm or wind turbine cluster the power curve is smoothed for.
Returns: smoothed_power_curve_df – Smoothed power curve. DataFrame has ‘wind_speed’ and ‘value’ columns with wind speeds in m/s and the corresponding power curve value in W.
Return type: pd.DataFrame
Notes
The following equation is used to calculated the power curves values of the smoothed power curve [1]:
(1)¶
- with:
P: power [W], v: wind speed [m/s], : standard deviation (Gauss), : mean (Gauss)
is the smoothed power curve value, is the standard wind speed in the power curve, is the interval length between and
Power curve smoothing is applied to take account for the spatial distribution of wind speed. This way of smoothing power curves is also used in [2] and [3].
The standard deviation of the above equation can be calculated by the following methods.
‘turbulence_intensity’ [2]:
- with:
- TI: turbulence intensity
‘Staffell_Pfenninger’ [4]:
References
[1] Knorr, K.: “Modellierung von raum-zeitlichen Eigenschaften der Windenergieeinspeisung für wetterdatenbasierte Windleistungssimulationen”. Universität Kassel, Diss., 2016, p. 106 [2] (1, 2) Nørgaard, P. and Holttinen, H.: “A Multi-Turbine and Power Curve Approach”. Nordic Wind Power Conference, 1.–2.3.2004, 2000, p. 5 [3] Kohler, S. and Agricola, A.-Cl. and Seidl, H.: “dena-Netzstudie II. Integration erneuerbarer Energien in die deutsche Stromversorgung im Zeitraum 2015 – 2020 mit Ausblick 2025”. Technical report, 2010. [4] Staffell, I. and Pfenninger, S.: “Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output”. 2005, p. 11