Merge branch 'dev' of github.com:wind-python/windpowerlib into dev

Author: Birgit Schachler

windpowerlib/data/cp_curves.csv

@@ -42,7 +42,9 @@ def temperature_gradient(temp_air, temp_height, hub_height):
     with:
         T: temperature [K], h: height [m]
 
-    :math:`h_{T,data}` is the height in which the temperature is measured.
+    :math:`h_{T,data}` is the height in which the temperature :math:`T_{air}`
+    is measured and :math:`T_{hub}` is the temperature at hub height
+    :math:`h_{hub}` of the wind turbine.
 
     Assumptions:
 
@@ -143,7 +145,7 @@ def rho_barometric(pressure, pressure_height, hub_height, temp_hub):
     :math:`h_{p,data}` is the height of the measurement or model data for
     pressure, :math:`p_0` the ambient air pressure, :math:`\rho_0` the ambient
     density of air, :math:`T_0` the ambient temperature and :math:`T_{hub}` the
-    temperature at hub height.
+    temperature at hub height :math:`h_{hub}`.
 
     Assumptions:
 
@@ -198,8 +200,10 @@ def rho_ideal_gas(pressure, pressure_height, hub_height, temp_hub):
     with:
         T: temperature [K], :math:`\rho`: density [kg/m³], p: pressure [Pa]
 
-    :math:`R_s` is the specific gas constant of dry air (287.058 J/(kg*K)) and
-    :math:`p_{hub}` is the pressure at hub height.
+    :math:`h_{p,data}` is the height of the measurement or model data for
+    pressure, :math:`R_s` is the specific gas constant of dry air
+    (287.058 J/(kg*K)) and :math:`p_{hub}` is the pressure at hub height
+    :math:`h_{hub}`.
 
     References
     ----------

windpowerlib/density.py

@@ -133,14 +133,14 @@ def p_curve(p_values, v_wind):
     p_values : pandas.DataFrame
         Power curve of the wind turbine.
         Indices are the wind speeds of the power curve in m/s, the
-        corresponding power values in W are in the column 'P'.
+        corresponding power values are in the column 'P'.
     v_wind : pandas.Series or array
         Wind speed at hub height in m/s.
 
     Returns
     -------
     power_output : pandas.Series
-        Electrical power output of the wind turbine in W.
+        Electrical power output of the wind turbine in the unit of `p_values`.
 
     Notes
     -------
@@ -178,13 +178,13 @@ def p_curve_density_corr(v_wind, rho_hub, p_values):
         Density of air at hub height in kg/m³.
     p_values : pandas.DataFrame
         Power curve of the wind turbine.
-        The indices are the corresponding wind speeds of the power curve, the
-        power values containing column is called 'P'.
+        Indices are the wind speeds of the power curve in m/s, the
+        corresponding power values are in the column 'P'.
 
     Returns
     -------
     power_output : pandas.Series
-        Electrical power output of the wind turbine in W.
+        Electrical power output of the wind turbine in the unit of `p_values`.
 
     Notes
     -----
@@ -204,9 +204,11 @@ def p_curve_density_corr(v_wind, rho_hub, p_values):
         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_{std}`, :math:`P_{std}`),
     :math:`v_{site}` is the density corrected wind speed for the power curve
-    (:math:`v_{site}`, :math:`P_{std}`).
+    (: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
     wind speed given in the power curve is zero.

windpowerlib/power_output.py

@@ -91,14 +91,14 @@ def test_rho_hub(self):
 
     def test_run_model(self):
         # Test with default parameters of modelchain (power curve)
-        power_output_exp = pd.Series(data=[1731887.39768, 3820152.27489])
+        power_output_exp = pd.Series(data=[1731.8874, 3820.15227])
         test_mc = mc.ModelChain(self.test_wt)
         test_mc.run_model(self.weather, self.data_height)
         assert_series_equal(test_mc.power_output, power_output_exp)
 
     def test_different_models(self):
         # Test with density corrected power curve
-        power_output_exp = pd.Series(data=[1430312.76771, 3746075.21279])
+        power_output_exp = pd.Series(data=[1430.31277, 3746.07521])
         self.test_modelchain['density_corr'] = True
         test_wt = wt.WindTurbine(**self.test_turbine)
         test_mc = mc.ModelChain(test_wt, **self.test_modelchain)

windpowerlib/tests/modelchain_tests.py

@@ -57,8 +57,9 @@ def logarithmic_wind_profile(v_wind, v_wind_height, hub_height, z_0,
     .. math:: v_{wind,hub}=v_{wind,data}\cdot\frac{\ln\left(\frac{h_{hub}}
         {z_{0}}\right)}{\ln\left(\frac{h_{data}}{z_{0}}\right)}
 
-    :math:`h_{data}` is the height at which the wind speed
-    :math:`v_{wind,data}` is measured.
+    :math:`h_{data}` is the height in which the wind speed
+    :math:`v_{wind,data}` is measured and :math:`v_{wind,hub}` is the wind
+    speed at hub height :math:`h_{hub}` of the wind turbine.
 
     Parameters `v_wind_height`, `z_0`, `hub_height` and `obstacle_height` have
     to be of the same unit.
@@ -114,8 +115,7 @@ def v_wind_hellman(v_wind, v_wind_height, hub_height, hellman_exp=None,
 
     Notes
     -----
-    The following equation is used [1]_,
-    [2]_, [3]_:
+    The following equation is used [1]_, [2]_, [3]_:
 
     .. math:: v_{wind,hub}=v_{wind,data}\cdot \left(\frac{h_{hub}}{h_{data}}
         \right)^\alpha
@@ -124,8 +124,8 @@ def v_wind_hellman(v_wind, v_wind_height, hub_height, hellman_exp=None,
         v: wind speed, h: height, :math:`\alpha`: Hellman exponent
 
     :math:`h_{data}` is the height in which the wind speed
-    :math:`v_{wind,data}` is measured and :math:`h_{hub}` is the hub height of
-    the wind turbine.
+    :math:`v_{wind,data}` is measured and :math:`v_{wind,hub}` is the wind
+    speed at hub height :math:`h_{hub}` of the wind turbine.
 
     For the Hellman exponent :math:`\alpha` many studies use a value of 1/7 for
     onshore and a value of 1/9 for offshore. The Hellman exponent can also

windpowerlib/wind_speed.py

@@ -163,12 +163,12 @@ def restructure_data():
             data = np.delete(data, 0, 0)
             df = pd.DataFrame(data, columns=['v_wind', self.fetch_curve])
             df.set_index('v_wind', drop=True, inplace=True)
-            nominal_power = wpp_df['p_nom'].iloc[0] * 1000
+            nominal_power = wpp_df['p_nom'].iloc[0]
             return df, nominal_power
         if self.fetch_curve == 'P':
             filename = 'P_curves.csv'
             p_values, p_nom = restructure_data()
-            self.p_values = p_values * 1000
+            self.p_values = p_values
         else:
             filename = 'cp_curves.csv'
             self.cp_values, p_nom = restructure_data()