Add to docstrings

Author: SabineHaas

windpowerlib/power_curves.py

@@ -1,6 +1,6 @@
 """
 The ``power_curves`` module contains functions for applying alterations like
-power curve smoohting or reducing power values by an efficiency to the power
+power curve smoothing or reducing power values by an efficiency to the power
 curve of a wind turbine, wind farm or wind turbine cluster.
 
 """
@@ -18,7 +18,7 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
                        standard_deviation_method='turbulence_intensity',
                        mean_gauss=0, **kwargs):
     r"""
-    Smoothes the input power curve values by using a Gauss distribution.
+    Smooths the input power curve values by using a Gauss distribution.
 
     Parameters
     ----------
@@ -29,8 +29,8 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
         Power curve values corresponding to wind speeds in
         `power_curve_wind_speeds`.
     block_width : float
-        Width between the wind speeds in the sum of the equation shown below in
-        Notes. Default: 0.5.
+        Width between the wind speeds in the sum of equation :eq:`power`.
+        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.
@@ -39,7 +39,7 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
         distribution. Options: 'turbulence_intensity', 'Staffell_Pfenninger'.
         Default: 'turbulence_intensity'.
     mean_gauss : float
-        Mean of the Gaus distribution in
+        Mean of the Gauss distribution in
         :py:func:`~.tools.gauss_distribution`. Default: 0.
 
     Other Parameters
@@ -58,18 +58,20 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
     -----
     The following equation is used to calculated the power curves values of the
     smoothed power curve [1]_:
+
     .. math:: P_{smoothed}(v_{std}) = \sum\limits_{v_i} \Delta v_i \cdot P(v_i)
         \cdot \frac{1}{\sigma \sqrt{2 \pi}}
         \exp \left[-\frac{(v_{std} - v_i -\mu)^2}{2 \sigma^2} \right]
+       :label: power
 
     with:
         P: power [W], v: wind speed [m/s],
-        :math:`\sigma`: standard deviation (Gauss), :math: `\mu`: mean (Gauss)
+        :math:`\sigma`: standard deviation (Gauss), :math:`\mu`: mean (Gauss)
 
         :math:`P_{smoothed}` is the smoothed power curve value,
         :math:`v_{std}` is the standard wind speed in the power curve,
         :math: `\Delta v_i` is the interval length between
-        :math: `$v_\text{i}$` and :math: `$v_\text{i+1}$`
+        :math: `v_\text{i}` and :math: `v_\text{i+1}`
 
     Power curve smoothing is applied to take account for the spatial
     distribution of wind speed. This way of smoothing power curves is also used

windpowerlib/tools.py

@@ -173,7 +173,7 @@ def gauss_distribution(function_variable, standard_deviation, mean=0):
 
 
 def estimate_turbulence_intensity(height, roughness_length):
-    """
+    r"""
     Estimate turbulence intensity by the roughness length.
 
     Parameters
@@ -188,7 +188,7 @@ def estimate_turbulence_intensity(height, roughness_length):
     -----
     The following equation is used [1]_:
 
-    .. math:: TI = \frac{1}{\ln\left( \frac{h}{z_0} \right)}
+    .. math:: TI = \frac{1}{\ln\left(\frac{h}{z_\text{0}}\right)}
 
     with:
         TI: turbulence intensity, h: height, :math:`z_{0}`: roughness length

windpowerlib/wake_losses.py

@@ -18,7 +18,7 @@
 
 
 def reduce_wind_speed(wind_speed, wind_efficiency_curve_name='dena_mean'):
-    """
+    r"""
     Reduces wind speed by a wind efficiency curve.
 
     The wind efficiency curves are provided in the windpowerlib and were
@@ -79,7 +79,7 @@ def get_wind_efficiency_curve(curve_name='dena_mean'):
     Notes
     -----
     The wind efficiency curves were generated in the "Dena Netzstudie" and in
-    the disseration of Kaspar Knorr. For more information see [1]_ and [2]_.
+    the dissertation of Kaspar Knorr. For more information see [1]_ and [2]_.
 
     References
     ----------
@@ -124,7 +124,7 @@ def display_wind_efficiency_curves():
     Notes
     -----
     The wind efficiency curves were generated in the "Dena Netzstudie" and in
-    the disseration of Kaspar Knorr. For more information see [1]_ and [2]_.
+    the dissertation of Kaspar Knorr. For more information see [1]_ and [2]_.
 
     References
     ----------
@@ -165,5 +165,6 @@ def display_wind_efficiency_curves():
         print(dena_df)
         print(knorr_df)
 
+
 if __name__ == "__main__":
     display_wind_efficiency_curves()

windpowerlib/wind_farm.py

@@ -172,6 +172,10 @@ def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
             Options: 'turbine_power_curves' (to the single turbine power
             curves), 'wind_farm_power_curves'.
             Default: 'wind_farm_power_curves'.
+        turbulence_intensity : float
+            Turbulence intensity at hub height of the wind farm for power curve
+            smoothing with 'turbulence_intensity' method. Can be calculated
+            from `roughness_length` instead. Default: None.
 
         Other Parameters
         ----------------

windpowerlib/wind_turbine_cluster.py

@@ -145,6 +145,11 @@ def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
             Options: 'turbine_power_curves' (to the single turbine power
             curves), 'wind_farm_power_curves'.
             Default: 'wind_farm_power_curves'.
+        turbulence_intensity : float
+            Turbulence intensity at hub height of the wind farm or
+            wind turbine cluster for power curve smoothing with
+            'turbulence_intensity' method. Can be calculated from
+            `roughness_length` instead. Default: None.
 
         Other Parameters
         ----------------