Move density correction to _p_curve_density_corr()

Author: SabineHaas

windpowerlib/modelchain.py

@@ -265,39 +265,20 @@ def turbine_power_output(self, wind_speed, density):
                 raise TypeError("Cp values of " +
                                 self.wind_turbine.turbine_name +
                                 " are missing.")
-            if self.density_corr is False:
-                logging.debug('Calculating power output using cp curve.')
-                output = power_output.power_coefficient_curve(
-                    wind_speed, density, self.wind_turbine.rotor_diameter,
-                    self.wind_turbine.cp_values)
-            elif self.density_corr is True:
-                logging.debug('Calculating power output using density ' +
-                              'corrected cp curve.')
-                output = power_output.cp_curve_density_corr(
-                    wind_speed, density, self.wind_turbine.rotor_diameter,
-                    self.wind_turbine.cp_values)
-            else:
-                raise TypeError("'{0}' is an invalid type.".format(type(
-                                self.density_corr)) + "`density_corr` must " +
-                                "be Boolean (True or False).")
+            logging.debug('Calculating power output using cp curve.')
+            output = power_output.power_coefficient_curve(
+                wind_speed, self.wind_turbine.cp_values,
+                self.wind_turbine.rotor_diameter, density,
+                self.density_corr)
         elif self.power_output_model == 'p_values':
             if self.wind_turbine.p_values is None:
                 raise TypeError("P values of " +
                                 self.wind_turbine.turbine_name +
                                 " are missing.")
-            if self.density_corr is False:
-                logging.debug('Calculating power output using power curve.')
-                output = power_output.power_curve(wind_speed,
-                                                  self.wind_turbine.p_values)
-            elif self.density_corr is True:
-                logging.debug('Calculating power output using density ' +
-                              'corrected power curve.')
-                output = power_output.p_curve_density_corr(
-                    wind_speed, density, self.wind_turbine.p_values)
-            else:
-                raise TypeError("'{0}' is an invalid type.".format(type(
-                                self.density_corr)) + "`density_corr` must " +
-                                "be Boolean (True or False).")
+            logging.debug('Calculating power output using power curve.')
+            output = power_output.power_curve(
+                wind_speed, self.wind_turbine.p_values, density,
+                self.density_corr)
         else:
             raise ValueError("'{0}' is an invalid value.".format(
                              self.power_output_model) +

windpowerlib/power_output.py

@@ -11,25 +11,30 @@
 import pandas as pd
 
 
-def power_coefficient_curve(wind_speed, density, rotor_diameter, cp_values):
+def power_coefficient_curve(wind_speed, cp_values,
+                            rotor_diameter, density, density_corr=False):
     r"""
     Calculates the turbine power output using a power coefficient curve.
 
     This function is carried out when the parameter `power_output_model` of an
-    instance of the :class:`~.modelchain.ModelChain` class is 'cp_values' and
-    the parameter `density_corr` is False.
+    instance of the :class:`~.modelchain.ModelChain` class is 'cp_values'. If
+    the parameter `density_corr` is True the density corrected power curve
+    (:py:func:`~._p_curve_density_corr`) is used.
 
     Parameters
     ----------
     wind_speed : pandas.Series or numpy.array
         Wind speed at hub height in m/s.
-    density : pandas.Series or numpy.array
-        Density of air at hub height in kg/m³.
-    rotor_diameter : float
-        Rotor diameter in m.
     cp_values : pandas.Series
         Power coefficient curve of the wind turbine.
         Indices are the wind speeds of the power coefficient curve in m/s.
+    rotor_diameter : float
+        Rotor diameter in m.
+    density : pandas.Series or numpy.array
+        Density of air at hub height in kg/m³.
+    density_corr : boolean
+        If the parameter is True the density corrected power curve is used for
+        the calculation of the turbine power output. Default: False.
 
     Returns
     -------
@@ -39,7 +44,8 @@ def power_coefficient_curve(wind_speed, density, rotor_diameter, cp_values):
 
     Notes
     -----
-    The following equation is used [1]_, [2]_:
+    The following equation is used if the parameter `density_corr` is False
+    [1]_, [2]_:
 
     .. math:: P=\frac{1}{8}\cdot\rho_{hub}\cdot d_{rotor}^{2}
         \cdot\pi\cdot v_{wind}^{3}\cdot cp\left(v_{wind}\right)
@@ -60,71 +66,38 @@ def power_coefficient_curve(wind_speed, density, rotor_diameter, cp_values):
             Wirtschaftlichkeit". 4. Auflage, Springer-Verlag, 2008, p. 542
 
     """
-    cp_time_series = np.interp(wind_speed, cp_values.index, cp_values,
-                               left=0, right=0)
-    # Convert density to np.array if wind_speed is np.array
-    if isinstance(wind_speed, np.ndarray) and isinstance(density, pd.Series):
-        density = np.array(density)
-    power_output = (1 / 8 * density * rotor_diameter ** 2 * np.pi
-                    * np.power(wind_speed, 3) * cp_time_series)
-    # Power_output as pd.Series if v_wind is pd.Series
+    if density_corr is False:
+        cp_time_series = np.interp(wind_speed, cp_values.index, cp_values,
+                                   left=0, right=0)
+        power_output = (1 / 8 * density * rotor_diameter ** 2 * np.pi
+                        * np.power(wind_speed, 3) * cp_time_series)
+    elif density_corr is True:
+        p_values = (1 / 8 * 1.225 * rotor_diameter ** 2 * np.pi *
+                    np.power(cp_values.index, 3) * cp_values)
+        p_values = pd.Series(np.array(p_values), index=cp_values.index)
+        power_output = _p_curve_density_corr(wind_speed, density, p_values)
+    else:
+        raise TypeError("'{0}' is an invalid type.".format(type(
+                        density_corr)) + "`density_corr` must be Boolean " +
+                        "(True or False).")
+
+    # Power_output as pd.Series if wind_speed is pd.Series (else: np.array)
     if isinstance(wind_speed, pd.Series):
         power_output = pd.Series(data=power_output, index=wind_speed.index,
                                  name='feedin_wind_turbine')
+    else:
+        power_output = np.array(power_output)
     return power_output
 
 
-def cp_curve_density_corr(wind_speed, density, rotor_diameter, cp_values):
-    r"""
-    Calculates the turbine power output using a density corrected power
-    coefficient curve.
-
-    This function is carried out when the parameter `power_output_model` of an
-    instance of the :class:`~.modelchain.ModelChain` class
-    is 'cp_values' and the parameter `density_corr` is True.
-
-    Parameters
-    ----------
-    wind_speed : pandas.Series or numpy.array
-        Wind speed at hub height in m/s.
-    density : pandas.Series or numpy.array
-        Density of air at hub height in kg/m³.
-    rotor_diameter : float
-        Rotor diameter in m.
-    cp_values : pandas.Series
-        Power coefficient curve of the wind turbine.
-        Indices are the wind speeds of the power coefficient curve in m/s.
-
-    Returns
-    -------
-    pandas.Series or numpy.array
-        Electrical power of the wind turbine in W.
-        Data type depends on type of `wind_speed`.
-
-    Notes
-    -----
-    See :py:func:`cp_curve` for further information on how the power values
-    are calculated and :py:func:`p_curve_density_corr` for further
-    information on how the density correction is implemented.
-
-    It is assumed that the power output for wind speeds above the maximum
-    and below the minimum wind speed given in the power coefficient curve is
-    zero.
-
-    """
-    p_values = (1 / 8 * 1.225 * rotor_diameter ** 2 * np.pi *
-                np.power(cp_values.index, 3) * cp_values)
-    p_values = pd.Series(np.array(p_values), index=cp_values.index)
-    return p_curve_density_corr(wind_speed, density, p_values)
-
-
-def power_curve(wind_speed, p_values):
+def power_curve(wind_speed, p_values, density=None, density_corr=False):
     r"""
     Calculates the turbine power output using a power curve.
 
     This function is carried out when the parameter `power_output_model` of an
-    instance of the :class:`~.modelchain.ModelChain` class is 'p_values' and
-    the parameter `density_corr` is False.
+    instance of the :class:`~.modelchain.ModelChain` class is 'p_values'. If
+    the parameter `density_corr` is True the density corrected power curve
+    (:py:func:`~._p_curve_density_corr`) is used.
 
     Parameters
     ----------
@@ -133,6 +106,13 @@ def power_curve(wind_speed, p_values):
     p_values : pandas.Series
         Power curve of the wind turbine.
         Indices are the wind speeds of the power curve in m/s.
+    density : pandas.Series or numpy.array
+        Density of air at hub height in kg/m³. This parameter is needed
+        if `density_corr` is True. Default: None.
+    density_corr : boolean
+        If the parameter is True the density corrected power curve is used for
+        the calculation of the turbine power output. In this case `density`
+        cannot be None. Default: False.
 
     Returns
     -------
@@ -146,16 +126,25 @@ def power_curve(wind_speed, p_values):
     and below the minimum wind speed given in the power curve is zero.
 
     """
-    power_output = np.interp(wind_speed, p_values.index, p_values,
-                             left=0, right=0)
-    # Power_output as pd.Series if wind_speed is pd.Series
+    if density_corr is False:
+        power_output = np.interp(wind_speed, p_values.index, p_values,
+                                 left=0, right=0)
+    elif density_corr is True:
+        power_output = _p_curve_density_corr(wind_speed, p_values, density)
+    else:
+        raise TypeError("'{0}' is an invalid type.".format(type(
+                        density_corr)) + "`density_corr` must be Boolean " +
+                        "(True or False).")
+    # Power_output as pd.Series if wind_speed is pd.Series (else: np.array)
     if isinstance(wind_speed, pd.Series):
         power_output = pd.Series(data=power_output, index=wind_speed.index,
                                  name='feedin_wind_turbine')
+    else:
+        power_output = np.array(power_output)
     return power_output
 
 
-def p_curve_density_corr(wind_speed, density, p_values):
+def _p_curve_density_corr(wind_speed, p_values, density):
     r"""
     Calculates the turbine power output using a density corrected power curve.
 
@@ -167,17 +156,16 @@ def p_curve_density_corr(wind_speed, density, p_values):
     ----------
     wind_speed : pandas.Series or numpy.array
         Wind speed time series at hub height in m/s.
-    density : pandas.Series or numpy.array
-        Density of air at hub height in kg/m³.
     p_values : pandas.Series
         Power curve of the wind turbine.
         Indices are the wind speeds of the power curve in m/s.
+    density : pandas.Series or numpy.array
+        Density of air at hub height in kg/m³.
 
     Returns
     -------
-    pandas.Series or numpy.array
+    list
         Electrical power output of the wind turbine in W.
-        Data type depends on type of `wind_speed`.
 
     Notes
     -----
@@ -219,19 +207,12 @@ def p_curve_density_corr(wind_speed, density, p_values):
             at Reiner Lemoine Institute, 2014, p. 13
 
     """
-    # Convert density to np.array if wind_speed is np.array
-    if isinstance(wind_speed, np.ndarray) and isinstance(density, pd.Series):
-        density = np.array(density)
-    power_output = [(np.interp(wind_speed[i],
-                               p_values.index * (1.225 / density[i])**(
-                                   np.interp(p_values.index,
-                                             [7.5, 12.5], [1/3, 2/3])),
-                               p_values, left=0, right=0))
-                    for i in range(len(wind_speed))]
-    # Power_output as pd.Series if wind_speed is pd.Series
-    if isinstance(wind_speed, pd.Series):
-        power_output = pd.Series(data=power_output, index=wind_speed.index,
-                                 name='feedin_wind_turbine')
-    else:
-        power_output = np.array(power_output)
-    return power_output
+    if density is None:
+        raise TypeError("`density` is None. For the calculation with a " +
+                        "density corrected power curve density at hub height" +
+                        "is needed.")
+    return[(np.interp(wind_speed[i],
+                      p_values.index * (1.225 / density[i])**(
+                          np.interp(p_values.index, [7.5, 12.5], [1/3, 2/3])),
+                      p_values, left=0, right=0))
+           for i in range(len(wind_speed))]