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

Author: SabineHaas

tests/test_modelchain.py

@@ -304,8 +304,26 @@ def test_missing_p_values(self):
                                     **test_modelchain)
             test_mc.run_model(self.weather_df)
 
+    def test_modelchain_with_power_curve_as_dict(self):
+        """Test power curves as dict"""
+        my_turbine = {'nominal_power': 3e6, 'hub_height': 105,
+                      'rotor_diameter': 70,
+                      'power_curve': {
+                          'value': [p * 1000 for p in [
+                              0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]],
+                          'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]},
+                      'power_coefficient_curve': {
+                          'value': [0.0, 0.43, 0.45, 0.35, 0.12, 0.03],
+                          'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]}}
+        power_output_exp = pd.Series(data=[919055.54840,
+                                           1541786.60559],
+                                     name='feedin_power_plant')
+        test_mc = mc.ModelChain(wt.WindTurbine(**my_turbine))
+        test_mc.run_model(self.weather_df)
+        assert_series_equal(test_mc.power_output, power_output_exp)
+
     def test_modelchain_with_power_coefficient_curve_as_dict(self):
-        """Test power (coefficient) curves as dict"""
+        """Test power coefficient curves as dict"""
         my_turbine = {'nominal_power': 3e6, 'hub_height': 105,
                       'rotor_diameter': 70,
                       'power_curve': {
@@ -315,9 +333,10 @@ def test_modelchain_with_power_coefficient_curve_as_dict(self):
                       'power_coefficient_curve': {
                           'value': [0.0, 0.43, 0.45, 0.35, 0.12, 0.03],
                           'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]}}
-        # run model with power curve
-        mc.ModelChain(wt.WindTurbine(**my_turbine)).run_model(self.weather_df)
-        # run model with power coefficient curve
-        mc.ModelChain(wt.WindTurbine(**my_turbine),
-                      power_output_model='power_coefficient_curve').run_model(
-            self.weather_df)
+        power_output_exp = pd.Series(data=[469518.35104,
+                                           901794.28532],
+                                     name='feedin_power_plant')
+        test_mc = mc.ModelChain(wt.WindTurbine(**my_turbine),
+                                power_output_model='power_coefficient_curve')
+        test_mc.run_model(self.weather_df)
+        assert_series_equal(test_mc.power_output, power_output_exp)

tests/test_turbine_cluster_modelchain.py

@@ -259,6 +259,11 @@ def test_tc_modelchain_with_power_curve_as_dict(self):
                          'number_of_turbines': 3}]}
         my_cluster =  {'wind_farms': [wf.WindFarm(**my_farm),
                                       wf.WindFarm(**self.test_farm)]}
+        power_output_exp = pd.Series(data=[10853277.966972714,
+                                           21731814.593688786],
+                                     name='feedin_power_plant')
         # run model with my_cluster
-        tc_mc.TurbineClusterModelChain(power_plant=wtc.WindTurbineCluster(
-            **my_cluster)).run_model(self.weather_df)
+        test_tc_mc = tc_mc.TurbineClusterModelChain(
+            power_plant=wtc.WindTurbineCluster(**my_cluster))
+        test_tc_mc.run_model(self.weather_df)
+        assert_series_equal(test_tc_mc.power_output, power_output_exp)

windpowerlib/wind_farm.py

@@ -361,7 +361,7 @@ def assign_power_curve(self, wake_losses_model='wind_farm_efficiency',
                 if power_curve.iloc[0]['wind_speed'] != 0.0:
                     power_curve = pd.concat(
                         [pd.DataFrame(data={
-                            'wind_speed': [0.0], 'value': [0.0]}),
+                            'value': [0.0], 'wind_speed': [0.0]}),
                             power_curve], join='inner')
                 if power_curve.iloc[-1]['value'] != 0.0:
                     power_curve = pd.concat(