Merge branch 'dev' into features/wind_farms_and_clusters

Author: SabineHaas

example/basic_example.ipynb

@@ -361,5 +361,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 0
 }

tests/test_modelchain.py

@@ -124,6 +124,11 @@ def test_wind_speed_hub(self):
         test_mc_3 = mc.ModelChain(
             wt.WindTurbine(**self.test_turbine),
             wind_speed_model='interpolation_extrapolation')
+        # Test modelchain with
+        # wind_speed_model='log_interpolation_extrapolation'
+        test_mc_4 = mc.ModelChain(
+            wt.WindTurbine(**self.test_turbine),
+            wind_speed_model='log_interpolation_extrapolation')
 
         # Parameters for tests
         wind_speed_8m = np.array([[4.0], [5.0]])
@@ -145,6 +150,8 @@ def test_wind_speed_hub(self):
         assert_series_equal(test_mc_2.wind_speed_hub(weather_df), v_wind_exp)
         v_wind_exp = pd.Series(data=[50.0, 74.0])
         assert_series_equal(test_mc_3.wind_speed_hub(weather_df), v_wind_exp)
+        v_wind_exp = pd.Series(data=[15.3188511585, 21.9782767378])
+        assert_series_equal(test_mc_4.wind_speed_hub(weather_df), v_wind_exp)
 
         # wind_speed is given at hub height
         weather_df.columns = [np.array(['wind_speed', 'wind_speed',

tests/test_tools.py

@@ -1,7 +1,8 @@
 import pandas as pd
 from pandas.util.testing import assert_series_equal
 
-from windpowerlib.tools import linear_interpolation_extrapolation
+from windpowerlib.tools import (linear_interpolation_extrapolation,
+                                logarithmic_interpolation_extrapolation)
 
 
 class TestTools:
@@ -39,3 +40,38 @@ def test_linear_interpolation_extrapolation(self):
         assert_series_equal(linear_interpolation_extrapolation(
             df, **parameters), exp_output)
 
+    def test_logarithmic_interpolation_extrapolation(self):
+        parameters = {'target_height': 80}
+        df = pd.DataFrame(data={10: [2.0, 2.0, 3.0],
+                                80: [4.0, 5.0, 6.0],
+                                200: [5.0, 8.0, 10.0]},
+                          index=[0, 1, 2])
+        # target_height is equal to height given in a column of the DataFrame
+        exp_output = pd.Series(data=[4.0, 5.0, 6.0])
+        assert_series_equal(logarithmic_interpolation_extrapolation(
+            df, **parameters), exp_output)
+        # target_height is between heights given in the columns of the
+        # DataFrame
+        exp_output = pd.Series(
+            data=[4.61074042165, 6.83222126494, 8.44296168659])
+        parameters['target_height'] = 140
+        assert_series_equal(logarithmic_interpolation_extrapolation(
+            df, **parameters), exp_output)
+        exp_output = pd.Series(
+            data=[4.11328333429, 5.16992500144, 6.16992500144])
+        parameters['target_height'] = 90
+        assert_series_equal(logarithmic_interpolation_extrapolation(
+            df, **parameters), exp_output)
+        # target_height is greater than the heights given in the columns of the
+        # DataFrame
+        exp_output = pd.Series(
+            data=[5.19897784672, 8.59693354015, 10.7959113869])
+        parameters['target_height'] = 240
+        assert_series_equal(logarithmic_interpolation_extrapolation(
+            df, **parameters), exp_output)
+        # target_height is smaller than the heights given in the columns of the
+        # DataFrame
+        exp_output = pd.Series(data=[1.33333333333, 1.0, 2.0])
+        parameters['target_height'] = 5
+        assert_series_equal(logarithmic_interpolation_extrapolation(
+            df, **parameters), exp_output)

windpowerlib/modelchain.py

@@ -23,7 +23,8 @@ class ModelChain(object):
     wind_speed_model : string
         Parameter to define which model to use to calculate the wind speed at
         hub height. Valid options are 'logarithmic', 'hellman' and
-        'interpolation_extrapolation'. Default: 'logarithmic'.
+        'interpolation_extrapolation', 'log_interpolation_extrapolation'.
+        Default: 'logarithmic'.
     temperature_model : string
         Parameter to define which model to use to calculate the temperature of
         air at hub height. Valid options are 'linear_gradient' and
@@ -55,7 +56,8 @@ class ModelChain(object):
     wind_speed_model : string
         Parameter to define which model to use to calculate the wind speed at
         hub height. Valid options are 'logarithmic', 'hellman' and
-        'interpolation_extrapolation'. Default: 'logarithmic'.
+        'interpolation_extrapolation', 'log_interpolation_extrapolation'.
+        Default: 'logarithmic'.
     temperature_model : string
         Parameter to define which model to use to calculate the temperature of
         air at hub height. Valid options are 'linear_gradient' and
@@ -300,6 +302,11 @@ def wind_speed_hub(self, weather_df):
                           'extrapolation.')
             wind_speed_hub = tools.linear_interpolation_extrapolation(
                 weather_df['wind_speed'], self.wind_turbine.hub_height)
+        elif self.wind_speed_model == 'log_interpolation_extrapolation':
+            logging.debug('Calculating wind speed using logarithmic inter- or '
+                          'extrapolation.')
+            wind_speed_hub = tools.logarithmic_interpolation_extrapolation(
+                weather_df['wind_speed'], self.wind_turbine.hub_height)
         else:
             raise ValueError("'{0}' is an invalid value. ".format(
                 self.wind_speed_model) + "`wind_speed_model` must be "

windpowerlib/tools.py

@@ -13,7 +13,7 @@
 
 def linear_interpolation_extrapolation(df, target_height):
     r"""
-    Inter- or extrapolates between the values of a data frame.
+    Linear inter- or extrapolates between the values of a data frame.
 
     This function can be used for the inter-/extrapolation of a parameter
     (e.g wind speed) available at two or more different heights, to approximate
@@ -73,6 +73,60 @@ def linear_interpolation_extrapolation(df, target_height):
             (target_height - heights_sorted[0]) + df[heights_sorted[0]])
 
 
+def logarithmic_interpolation_extrapolation(df, target_height):
+    r"""
+    Logarithmic inter- or extrapolates between the values of a data frame.
+
+    This function can be used for the inter-/extrapolation of the wind speed if
+    it is available at two or more different heights, to approximate
+    the value at hub height. The function is carried out when the parameter
+    `wind_speed_model` :class:`~.modelchain.ModelChain` class is
+    'log_interpolation_extrapolation'.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+        DataFrame with time series for parameter that is to be interpolated or
+        extrapolated. The columns of the DataFrame are the different heights
+        for which the parameter is available. If more than two heights are
+        given, the two closest heights are used. See example in
+        :py:func:`~.linear_interpolation_extrapolation` on how the
+        DataFrame should look like and how the function can be used.
+    target_height : float
+        Height for which the parameter is approximated (e.g. hub height).
+
+    Returns
+    -------
+    pandas.Series
+        Result of the inter-/extrapolation (e.g. wind speed at hub height).
+
+    Notes
+    -----
+
+    For the logarithmic inter- and extrapolation the following equation is
+    used [1]_:
+
+    .. math:: f(x) = \frac{\ln(x) \cdot (f(x_2) - f(x_1)) - f(x_2) \cdot \ln(x_1) + f(x_1) \cdot \ln(x_2)}{\ln(x_2) - \ln(x_1)}
+
+    References
+    ----------
+    .. [1] Knorr, K.: "Modellierung von raum-zeitlichen Eigenschaften der
+             Windenergieeinspeisung für wetterdatenbasierte
+             Windleistungssimulationen". Universität Kassel, Diss., 2016,
+             p. 83
+
+    """
+    # find closest heights
+    heights_sorted = df.columns[
+        sorted(range(len(df.columns)),
+               key=lambda i: abs(df.columns[i] - target_height))]
+    return ((np.log(target_height) *
+             (df[heights_sorted[1]] - df[heights_sorted[0]]) -
+             df[heights_sorted[1]] * np.log(heights_sorted[0]) +
+             df[heights_sorted[0]] * np.log(heights_sorted[1])) /
+            (np.log(heights_sorted[1]) - np.log(heights_sorted[0])))
+
+
 def gaussian_distribution(function_variable, standard_deviation, mean=0):
     r"""
     Normal distribution or gaussian distribution.
@@ -105,7 +159,7 @@ def gaussian_distribution(function_variable, standard_deviation, mean=0):
     ----------
     .. [1] Berendsen, H.: "A Student's Guide to Data and Error Analysis".
              New York, Cambridge University Press, 2011, p. 37
-    
+
     # TODO: add references
 
     """
@@ -118,6 +172,29 @@ def estimate_turbulence_intensity(height, roughness_length):
     """
     Calculate turbulence intensity.
 
+    Parameters
+    ----------
+    height : Float
+        Height above ground in m at which the turbulence intensity is
+        calculated.
+    roughness_length : pandas.Series or numpy.array or float
+        Roughness length.
+
+    Notes
+    -----
+    The following equation is used [1]_:
+
+    .. math:: TI = \frac{1}{ln \left(\frac{h}{z_\text{0}} \right)}
+
+    with:
+        TI: turbulence intensity, h: height, :math:`z_{0}`: roughness length
+
+    References
+    ----------
+    .. [1] Knorr, K.: "Modellierung von raum-zeitlichen Eigenschaften der
+             Windenergieeinspeisung für wetterdatenbasierte
+             Windleistungssimulationen". Universität Kassel, Diss., 2016,
+             p. 88
+
     """
-    # TODO: Search other possibilities for TI.
     return 1 / (np.log(height / roughness_length))

windpowerlib/wind_speed.py

@@ -42,7 +42,7 @@ def logarithmic_profile(wind_speed, wind_speed_height, hub_height,
 
     Notes
     -----
-    The following equation is used [1]_:
+    The following equation is used [1]_, [2]_, [3]_:
 
     .. math:: v_{wind,hub}=v_{wind,data}\cdot
         \frac{\ln\left(\frac{h_{hub}-d}{z_{0}}\right)}{\ln\left(