Merge branch 'features/wind_farms_and_clusters' into dev

Author: SabineHaas

windpowerlib/data/wind_efficiency_curves.csv

@@ -310,8 +310,8 @@ def wind_speed_hub(self, weather_df):
         else:
             raise ValueError("'{0}' is an invalid value. ".format(
                 self.wind_speed_model) + "`wind_speed_model` must be "
-                "'logarithmic', 'hellman' or "
-                "'interpolation_extrapolation'.")
+                "'logarithmic', 'hellman', 'interpolation_extrapolation' " +
+                "or 'log_interpolation_extrapolation'.")
         return wind_speed_hub
 
     def turbine_power_output(self, wind_speed_hub, density_hub):

windpowerlib/modelchain.py

@@ -10,13 +10,13 @@
 import numpy as np
 import pandas as pd
 from windpowerlib import tools
+import os
 
 
 def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
-                       block_width=0.5,
+                       block_width=0.5, wind_speed_range=15.0,
                        standard_deviation_method='turbulence_intensity',
                        mean_gauss=0, **kwargs):
-    # TODO: All functions in this module have to work without pandas
     r"""
     Smoothes the input power curve values by using a gaussian distribution.
 
@@ -32,7 +32,7 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
         Width of the moving block. Default: 0.5.
     standard_deviation_method : String
         Method for calculating the standard deviation for the gaussian
-        distribution. Options: 'turbulence_intensity', 'Norgaard', 'Staffell'.
+        distribution. Options: 'turbulence_intensity', 'Norgaard', 'Staffell_Pfenninger'.
         Default: 'turbulence_intensity'.
 
     Other Parameters
@@ -70,30 +70,28 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
             raise ValueError("Turbulence intensity must be defined for " +
                              "using 'turbulence_intensity' as " +
                              "`standard_deviation_method`")
-    elif standard_deviation_method == 'Norgaard':
-        pass # TODO add
-    elif standard_deviation_method == 'Staffell':
+    elif standard_deviation_method == 'Staffell_Pfenninger':
         normalized_standard_deviation = 0.2
     # Initialize list for power curve values
     smoothed_power_curve_values = []
-    # Step of power curve wind speeds
-    step = power_curve_wind_speeds.iloc[-5] - power_curve_wind_speeds.iloc[-6]
-    # Append wind speeds to `power_curve_wind_speeds` until 40 m/s
-    while (power_curve_wind_speeds.values[-1] < 40.0):
+    # Append wind speeds to `power_curve_wind_speeds` until last value + range
+    maximum_value = power_curve_wind_speeds.values[-1] + wind_speed_range
+    while (power_curve_wind_speeds.values[-1] < maximum_value):
         power_curve_wind_speeds = power_curve_wind_speeds.append(
-            pd.Series(power_curve_wind_speeds.iloc[-1] + step,
+            pd.Series(power_curve_wind_speeds.iloc[-1] + 0.5,
                       index=[power_curve_wind_speeds.index[-1] + 1]))
         power_curve_values = power_curve_values.append(
             pd.Series(0.0, index=[power_curve_values.index[-1] + 1]))
     for power_curve_wind_speed in power_curve_wind_speeds:
         # Create array of wind speeds for the moving block
-        wind_speeds_block = (
-            np.arange(-15.0, 15.0 + block_width, block_width) +
-            power_curve_wind_speed)
+        wind_speeds_block = (np.arange(-wind_speed_range,
+                                       wind_speed_range + block_width,
+                                       block_width) +
+                             power_curve_wind_speed)
         # Get standard deviation for gaussian filter
         standard_deviation = (
             (power_curve_wind_speed * normalized_standard_deviation + 0.6)
-            if standard_deviation_method is 'Staffell'
+            if standard_deviation_method is 'Staffell_Pfenninger'
             else power_curve_wind_speed * normalized_standard_deviation)
         # Get the smoothed value of the power output
         smoothed_value = sum(
@@ -113,20 +111,20 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
               smoothed_power_curve_values]).transpose()
     # Rename columns of DataFrame
     smoothed_power_curve_df.columns = ['wind_speed', 'power']
-#    # Plot power curves
-#    fig = plt.figure()
-#    plt.plot(power_curve_wind_speeds.values, power_curve_values.values)
-#    plt.plot(power_curve_wind_speeds.values, smoothed_power_curve_values)
-#    fig.savefig(os.path.abspath(os.path.join(
-#        os.path.dirname(__file__), '../Plots/power_curves',
-#        '{0}_{1}_{2}.png'.format(kwargs['object_name'],
-#                                 standard_deviation_method, block_width))))
-#    plt.close() # TODO: delete plot later
+    # # Plot power curves
+    # fig = plt.figure()
+    # plt.plot(power_curve_wind_speeds.values, power_curve_values.values)
+    # plt.plot(power_curve_wind_speeds.values, smoothed_power_curve_values)
+    # fig.savefig(os.path.abspath(os.path.join(
+    #     os.path.dirname(__file__), '../Plots/power_curves',
+    #     '{0}_{1}_{2}.png'.format(kwargs['object_name'],
+    #                              standard_deviation_method, block_width))))
+    # plt.close() # TODO: delete plot later
     return smoothed_power_curve_df
 
 
 def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
-                               wake_losses_method='constant_efficiency',
+                               wake_losses_method='wind_efficiency_curve',
                                wind_farm_efficiency=None):
     r"""
     Applies wake losses depending on the method to a power curve.
@@ -141,7 +139,8 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
         `power_curve_wind_speeds`.
     wake_losses_method : String
         Defines the method for talking wake losses within the farm into
-        consideration. Default: 'constant_efficiency'.
+        consideration. Options: 'wind_efficiency_curve', 'constant_efficiency'.
+        Default: 'wind_efficiency_curve'.
     wind_farm_efficiency : Float or pd.DataFrame or Dictionary
         Efficiency of the wind farm. Either constant (float) or wind efficiency
         curve (pd.DataFrame or Dictionary) contianing 'wind_speed' and
@@ -151,8 +150,9 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
     Returns
     -------
     power_curve_df : pd.DataFrame
-        With wind farm efficiency reduced power curve. DataFrame power curve
-        values in W with the corresponding wind speeds in m/s.
+        With wind farm efficiency reduced power curve. DataFrame has 
+        'wind_speed' and 'power' columns with wind speeds in m/s and the
+        corresponding power curve value in W.
 
     Notes
     -----
@@ -188,7 +188,85 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
         power_curve_df.columns = ['wind_speed', 'power']
     else:
         raise ValueError(
-            "`wake_losses_method` is {0} but should be None, ".format(
+            "`wake_losses_method` is {0} but should be ".format(
                 wake_losses_method) +
             "'constant_efficiency' or 'wind_efficiency_curve'")
     return power_curve_df
+
+
+def density_correct_power_curve(density, power_curve_wind_speeds,
+                                power_curve_values):
+    r"""
+    Applies a density correction to a power curve.
+
+    As site specific density a mean density has to be given as parameter.
+
+    Parameters
+    ----------
+    density : float
+        Mean density of air at hub height in kg/m³.
+    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`.
+
+    Returns
+    -------
+    power_curve_df : pd.DataFrame
+        Density corrected power curve. DataFrame has 'wind_speed' and 'power'
+        columns with wind speeds in m/s and the corresponding power curve
+        value in W.
+
+    Notes
+    -----
+    The following equation is used for the site specific power curve wind
+    speeds [1]_, [2]_, [3]_:
+
+    .. math:: v_{site}=v_{std}\cdot\left(\frac{\rho_0}
+                       {\rho_{site}}\right)^{p(v)}
+
+    with:
+        .. math:: p=\begin{cases}
+                      \frac{1}{3} & v_{std} \leq 7.5\text{ m/s}\\
+                      \frac{1}{15}\cdot v_{std}-\frac{1}{6} & 7.5
+                      \text{ m/s}<v_{std}<12.5\text{ m/s}\\
+                      \frac{2}{3} & \geq 12.5 \text{ m/s}
+                    \end{cases},
+        v: wind speed [m/s], :math:`\rho`: density [kg/m³]
+
+    :math:`v_{std}` is the standard wind speed in the power curve
+    (:math:`v_{std}`, :math:`P_{std}`),
+    :math:`v_{site}` is the density corrected wind speed for the power curve
+    (:math:`v_{site}`, :math:`P_{std}`),
+    :math:`\rho_0` is the ambient density (1.225 kg/m³)
+    and :math:`\rho_{site}` the density at site conditions (and hub height).
+
+    It is assumed that the power output for wind speeds above the maximum
+    and below the minimum wind speed given in the power curve is zero.
+
+    References
+    ----------
+    .. [1] Svenningsen, L.: "Power Curve Air Density Correction And Other
+            Power Curve Options in WindPRO". 1st edition, Aalborg,
+            EMD International A/S , 2010, p. 4
+    .. [2] Svenningsen, L.: "Proposal of an Improved Power Curve Correction".
+            EMD International A/S , 2010
+    .. [3] Biank, M.: "Methodology, Implementation and Validation of a
+            Variable Scale Simulation Model for Windpower based on the
+            Georeferenced Installation Register of Germany". Master's Thesis
+            at Reiner Lemoine Institute, 2014, p. 13
+
+    """
+    if density is None:
+        raise TypeError("`density` is None. For the calculation with a " +
+                        "density corrected power curve mean density at hub " +
+                        "height is needed.")
+    corrected_power_curve_wind_speeds = (
+        power_curve_wind_speeds * (1.225 / density) ** (
+            np.interp(power_curve_wind_speeds, [7.5, 12.5], [1/3, 2/3])))
+    power_curve_df = pd.DataFrame([corrected_power_curve_wind_speeds,
+                                   power_curve_values]).transpose()
+    power_curve_df.columns = ['wind_speed', 'power']
+    return power_curve_df

windpowerlib/power_curves.py

@@ -188,8 +188,8 @@ def power_curve_density_correction(wind_speed, power_curve_wind_speeds,
 
     Notes
     -----
-    The following equation is used for the wind speed at site
-    [1]_, [2]_, [3]_:
+    The following equation is used for the site specific power curve wind
+    speeds [1]_, [2]_, [3]_:
 
     .. math:: v_{site}=v_{std}\cdot\left(\frac{\rho_0}
                        {\rho_{site}}\right)^{p(v)}

windpowerlib/power_output.py

@@ -27,7 +27,8 @@ class TurbineClusterModelChain(object):
         before the summation. Default: False.
     wake_losses_method : String
         Defines the method for talking wake losses within the farm into
-        consideration. Default: 'constant_efficiency'.
+        consideration. Options: 'wind_efficiency_curve', 'constant_efficiency'
+        or None. Default: 'wind_efficiency_curve'.
     smoothing : Boolean
         If True the power curves will be smoothed before the summation.
         Default: True.
@@ -36,7 +37,8 @@ class TurbineClusterModelChain(object):
         Default in :py:func:`~.power_curves.smooth_power_curve`: 0.5.
     standard_deviation_method : String, optional
         Method for calculating the standard deviation for the gaussian
-        distribution. Options: 'turbulence_intensity', 'Norgaard', 'Staffell'.
+        distribution. Options: 'turbulence_intensity', 'Norgaard',
+        'Staffell_Pfenninger'.
         Default in :py:func:`~.power_curves.smooth_power_curve`:
         'turbulence_intensity'.
     density_correction_order : String
@@ -61,7 +63,8 @@ class TurbineClusterModelChain(object):
         before the summation. Default: False.
     wake_losses_method : String
         Defines the method for talking wake losses within the farm into
-        consideration. Default: 'constant_efficiency'.
+        consideration. Options: 'wind_efficiency_curve', 'constant_efficiency'
+        or None. Default: 'wind_efficiency_curve'.
     smoothing : Boolean
         If True the power curves will be smoothed before the summation.
         Default: True.
@@ -70,7 +73,8 @@ class TurbineClusterModelChain(object):
         Default in :py:func:`~.power_curves.smooth_power_curve`: 0.5.
     standard_deviation_method : String, optional
         Method for calculating the standard deviation for the gaussian
-        distribution. Options: 'turbulence_intensity', 'Norgaard', 'Staffell'.
+        distribution. Options: 'turbulence_intensity', 'Norgaard',
+        'Staffell_Pfenninger'.
         Default in :py:func:`~.power_curves.smooth_power_curve`:
         'turbulence_intensity'.
     power_output : pandas.Series
@@ -88,7 +92,7 @@ class TurbineClusterModelChain(object):
 
     """
     def __init__(self, wind_object, density_correction=False,
-                 wake_losses_method='constant_efficiency', smoothing=True,
+                 wake_losses_method='wind_efficiency_curve', smoothing=True,
                  block_width=0.5,
                  standard_deviation_method='turbulence_intensity',
                  density_correction_order='wind_farm_power_curves',
@@ -114,9 +118,6 @@ def __init__(self, wind_object, density_correction=False,
                              "cluster but `wind_object` is an object of the " +
                              "class WindFarm.")
 
-# TODO: if a wind turbine of wind farm does not have a power curve but a cp curve:
-    # calculate power curve from cp curve
-
     def wind_farm_power_curve(self, wind_farm, **kwargs):
         r"""
         Caluclates the power curve of a wind farm.
@@ -125,6 +126,20 @@ def wind_farm_power_curve(self, wind_farm, **kwargs):
         and/or density corrected before or after the summation and/or a wind
         farm efficiency is applied after the summation. TODO: check entry
 
+        Parameters
+        ----------
+        wind_farm : WindFarm
+            A :class:`~.wind_farm.WindFarm` object representing the wind farm.
+
+#        density_df : pandas.DataFrame
+#            DataFrame with time series for density `density` in kg/m³.
+#            The columns of the DataFrame are a MultiIndex where the first level
+#            contains the variable name (density) and the second level
+#            contains the height at which it applies (e.g. 10, if it was
+#            measured at a height of 10 m).
+#            See :py:func:`~.run_model()` for an example on how to
+#            create the density_df DataFrame.
+
         Other Parameters
         ----------------
         roughness_length : Float, optional.
@@ -157,11 +172,12 @@ def wind_farm_power_curve(self, wind_farm, **kwargs):
                         raise ValueError(
                             "`roughness_length` must be defined for using " +
                             "'turbulence_intensity' as " +
-                            "`standard_deviation_method`")
-            if self. density_correction:
-                pass # TODO: restrictions (density needed)
+                            "`standard_deviation_method` if " +
+                            "`turbulence_intensity` is not given")
+#            if self. density_correction:
+#                pass # TODO: restrictions (density needed)
             if self.wake_losses_method is not None:
-                if self.wind_object.efficiency is None: # TODO if not...
+                if wind_farm.efficiency is None:
                     raise KeyError(
                         "wind_farm_efficiency is needed if " +
                         "`wake_losses_method´ is '{0}', but ".format(
@@ -173,13 +189,17 @@ def wind_farm_power_curve(self, wind_farm, **kwargs):
             power_curve = pd.DataFrame(
                 turbine_type_dict['wind_turbine'].power_curve)
             # Editions to power curve before the summation
-            if (self.density_correction and
-                    self.density_correction_order == 'turbine_power_curves'):
-                pass # TODO: add density correction
+#            if (self.density_correction and
+#                    self.density_correction_order == 'turbine_power_curves'):
+#                power_curve = power_curves.density_correct_power_curve(
+#                    density_df, power_curve['wind_speed'], # Note: density at hub height has to be passed
+#                    power_curve['power'])
             if (self.smoothing and
                     self.smoothing_order == 'turbine_power_curves'):
                 power_curve = power_curves.smooth_power_curve(
-                    power_curve['wind_speed'], power_curve['power'], **kwargs)
+                    power_curve['wind_speed'], power_curve['power'],
+                    standard_deviation_method=self.standard_deviation_method,
+                    **kwargs)
             # Add power curves of all turbines of same type to data frame after
             # renaming columns
             power_curve.columns = ['wind_speed', turbine_type_dict[
@@ -199,14 +219,19 @@ def wind_farm_power_curve(self, wind_farm, **kwargs):
                   list(summarized_power_curve['power'].values)]).transpose()
         summarized_power_curve_df.columns = ['wind_speed', 'power']
         # Editions to power curve after the summation
-        if (self.density_correction and
-                self.density_correction_order == 'wind_farm_power_curves'):
-            pass # TODO: add density correction
+#        if (self.density_correction and
+#                self.density_correction_order == 'wind_farm_power_curves'):
+#            summarized_power_curve_df = (
+#                power_curves.density_correct_power_curve(
+#                    density_df, summarized_power_curve_df['wind_speed'],
+#                    summarized_power_curve_df['power'])) # Note: density at hub height has to be passed
         if (self.smoothing and
                 self.smoothing_order == 'wind_farm_power_curves'):
             summarized_power_curve_df = power_curves.smooth_power_curve(
                 summarized_power_curve_df['wind_speed'],
-                summarized_power_curve_df['power'], **kwargs)
+                summarized_power_curve_df['power'],
+                standard_deviation_method=self.standard_deviation_method,
+                **kwargs)
         if (self.wake_losses_method == 'constant_efficiency' or
                 self.wake_losses_method == 'wind_efficiency_curve'):
             summarized_power_curve_df = (
@@ -406,8 +431,12 @@ def run_model(self, weather_df, **kwargs):
         modelchain_data = self.get_modelchain_data(**kwargs)
         # Density correction to cluster power curve # TODO test
         if (self.density_correction and
-                self.density_correction_order == 'cluster_power_curve'):
+                self.density_correction_order == 'wind_farm_power_curves'):
+            # Note actually for 'cluster_power_curve'. however, probably the
+            # density correcton will always be done at the end
             modelchain_data['density_correction'] = True
+        else:
+            modelchain_data['density_correction'] = False
         # Run modelchain
         mc = modelchain.ModelChain(
             self.wind_object, **modelchain_data).run_model(weather_df)