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SabineHaasSabineHaas
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Add turbine cluster test
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‎tests/test_turbine_cluster_modelchain.py‎

Lines changed: 116 additions & 25 deletions
Original file line numberDiff line numberDiff line change
@@ -5,35 +5,21 @@
55

66
import windpowerlib.wind_farm as wf
77
import windpowerlib.wind_turbine as wt
8+
import windpowerlib.wind_turbine_cluster as wtc
89
import windpowerlib.turbine_cluster_modelchain as tc_mc
910

1011

1112
class TestTurbineClusterModelChain:
1213

1314
@classmethod
1415
def setup_class(self):
15-
self.test_turbine = {'hub_height': 100,
16-
'rotor_diameter': 80,
17-
'name': 'ENERCON E 126 7500',
18-
'fetch_curve': 'power_curve'}
19-
self.test_farm = {'name': 'test farm',
20-
'wind_turbine_fleet': [
21-
{'wind_turbine':
22-
wt.WindTurbine(**self.test_turbine),
23-
'number_of_turbines': 3}]}
24-
25-
def test_run_model(self):
26-
parameters = {'wake_losses_model': 'dena_mean',
27-
'smoothing': False,
28-
'standard_deviation_method': 'turbulence_intensity',
29-
'smoothing_order': 'wind_farm_power_curves'}
3016
temperature_2m = np.array([[267], [268]])
3117
temperature_10m = np.array([[267], [266]])
3218
pressure_0m = np.array([[101125], [101000]])
3319
wind_speed_8m = np.array([[4.0], [5.0]])
3420
wind_speed_10m = np.array([[5.0], [6.5]])
3521
roughness_length = np.array([[0.15], [0.15]])
36-
weather_df = pd.DataFrame(np.hstack((temperature_2m,
22+
self.weather_df = pd.DataFrame(np.hstack((temperature_2m,
3723
temperature_10m,
3824
pressure_0m,
3925
wind_speed_8m,
@@ -47,14 +33,44 @@ def test_run_model(self):
4733
'wind_speed',
4834
'roughness_length']),
4935
np.array([2, 10, 0, 8, 10, 0])])
36+
self.test_turbine = {'hub_height': 100,
37+
'rotor_diameter': 80,
38+
'name': 'ENERCON E 126 7500',
39+
'fetch_curve': 'power_curve'}
40+
self.test_turbine_2 = {'hub_height': 90,
41+
'rotor_diameter': 60,
42+
'name': 'VESTAS V 90 1800',
43+
'fetch_curve': 'power_curve'}
44+
self.test_farm = {'name': 'test farm',
45+
'wind_turbine_fleet': [
46+
{'wind_turbine':
47+
wt.WindTurbine(**self.test_turbine),
48+
'number_of_turbines': 3}]}
49+
self.test_farm_2 = {'name': 'test farm',
50+
'wind_turbine_fleet':
51+
[{'wind_turbine':
52+
wt.WindTurbine(**self.test_turbine),
53+
'number_of_turbines': 3},
54+
{'wind_turbine':
55+
wt.WindTurbine(**self.test_turbine_2),
56+
'number_of_turbines': 3}]}
57+
self.test_cluster = {'name': 'example_cluster',
58+
'wind_farms': [wf.WindFarm(**self.test_farm),
59+
wf.WindFarm(**self.test_farm_2)]}
60+
61+
def test_run_model(self):
62+
parameters = {'wake_losses_model': 'dena_mean',
63+
'smoothing': False,
64+
'standard_deviation_method': 'turbulence_intensity',
65+
'smoothing_order': 'wind_farm_power_curves'}
5066

5167
# Test modelchain with default values
5268
power_output_exp = pd.Series(data=[4409211.803349806,
5369
10212484.219845157],
5470
name='feedin_power_plant')
5571
test_tc_mc = tc_mc.TurbineClusterModelChain(
5672
power_plant=wf.WindFarm(**self.test_farm), **parameters)
57-
test_tc_mc.run_model(weather_df)
73+
test_tc_mc.run_model(self.weather_df)
5874
assert_series_equal(test_tc_mc.power_output, power_output_exp)
5975

6076
# Test constant efficiency
@@ -66,7 +82,7 @@ def test_run_model(self):
6682
name='feedin_power_plant')
6783
test_tc_mc = tc_mc.TurbineClusterModelChain(
6884
power_plant=test_wind_farm, **parameters)
69-
test_tc_mc.run_model(weather_df)
85+
test_tc_mc.run_model(self.weather_df)
7086
assert_series_equal(test_tc_mc.power_output, power_output_exp)
7187

7288
# TODO: Test power efficiency curve?
@@ -80,20 +96,95 @@ def test_run_model(self):
8096
name='feedin_power_plant')
8197
test_tc_mc = tc_mc.TurbineClusterModelChain(
8298
power_plant=test_wind_farm, **parameters)
83-
test_tc_mc.run_model(weather_df)
99+
test_tc_mc.run_model(self.weather_df)
100+
assert_series_equal(test_tc_mc.power_output, power_output_exp)
101+
102+
# Test wind farm with different turbine types (smoothing)
103+
test_wind_farm = wf.WindFarm(**self.test_farm_2)
104+
test_wind_farm.efficiency = 0.9
105+
power_output_exp = pd.Series(data=[7035990.555719288,
106+
14104709.373232642],
107+
name='feedin_power_plant')
108+
test_tc_mc = tc_mc.TurbineClusterModelChain(
109+
power_plant=test_wind_farm, **parameters)
110+
test_tc_mc.run_model(self.weather_df)
84111
assert_series_equal(test_tc_mc.power_output, power_output_exp)
85112

86-
# Test other smoothing order # TODO: Test different wind turbines
113+
# Test other smoothing order
87114
parameters['smoothing_order'] = 'turbine_power_curves'
88-
test_wind_farm = wf.WindFarm(**self.test_farm)
115+
test_wind_farm = wf.WindFarm(**self.test_farm_2)
89116
test_wind_farm.efficiency = 0.9
90-
power_output_exp = pd.Series(data=[5015168.554748144,
91-
10389592.995632712],
117+
power_output_exp = pd.Series(data=[7067892.325652927,
118+
14103159.481573664],
92119
name='feedin_power_plant')
93120
test_tc_mc = tc_mc.TurbineClusterModelChain(
94121
power_plant=test_wind_farm, **parameters)
95-
test_tc_mc.run_model(weather_df)
122+
test_tc_mc.run_model(self.weather_df)
123+
assert_series_equal(test_tc_mc.power_output, power_output_exp)
124+
125+
def test_run_model_turbine_cluster(self):
126+
parameters = {'wake_losses_model': 'dena_mean',
127+
'smoothing': False,
128+
'standard_deviation_method': 'turbulence_intensity',
129+
'smoothing_order': 'wind_farm_power_curves'}
130+
131+
# Test modelchain with default values
132+
power_output_exp = pd.Series(data=[10683892.759175435,
133+
24399645.35183305],
134+
name='feedin_power_plant')
135+
test_tc_mc = tc_mc.TurbineClusterModelChain(
136+
power_plant=wtc.WindTurbineCluster(**self.test_cluster),
137+
**parameters)
138+
test_tc_mc.run_model(self.weather_df)
139+
assert_series_equal(test_tc_mc.power_output, power_output_exp)
140+
141+
# Test constant efficiency
142+
parameters['wake_losses_model'] = 'constant_efficiency'
143+
test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
144+
for farm in test_cluster.wind_farms:
145+
farm.efficiency = 0.9
146+
power_output_exp = pd.Series(data=[11333638.728757974,
147+
24561046.12113034],
148+
name='feedin_power_plant')
149+
test_tc_mc = tc_mc.TurbineClusterModelChain(
150+
power_plant=test_cluster, **parameters)
151+
test_tc_mc.run_model(self.weather_df)
96152
assert_series_equal(test_tc_mc.power_output, power_output_exp)
97153

154+
# Test smoothing
155+
parameters['smoothing'] = 'True'
156+
test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
157+
for farm in test_cluster.wind_farms:
158+
farm.efficiency = 0.9
159+
power_output_exp = pd.Series(data=[12055848.06813206,
160+
24494381.45222553],
161+
name='feedin_power_plant')
162+
test_tc_mc = tc_mc.TurbineClusterModelChain(
163+
power_plant=test_cluster, **parameters)
164+
test_tc_mc.run_model(self.weather_df)
165+
assert_series_equal(test_tc_mc.power_output, power_output_exp)
98166

99-
# TODO: test turbine cluster
167+
# Test wind farm with different turbine types (smoothing)
168+
test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
169+
for farm in test_cluster.wind_farms:
170+
farm.efficiency = 0.9
171+
power_output_exp = pd.Series(data=[12055848.06813206,
172+
24494381.45222553],
173+
name='feedin_power_plant')
174+
test_tc_mc = tc_mc.TurbineClusterModelChain(
175+
power_plant=test_cluster, **parameters)
176+
test_tc_mc.run_model(self.weather_df)
177+
assert_series_equal(test_tc_mc.power_output, power_output_exp)
178+
179+
# Test other smoothing order
180+
parameters['smoothing_order'] = 'turbine_power_curves'
181+
test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
182+
for farm in test_cluster.wind_farms:
183+
farm.efficiency = 0.9
184+
power_output_exp = pd.Series(data=[12086527.665961245,
185+
24492153.631181397],
186+
name='feedin_power_plant')
187+
test_tc_mc = tc_mc.TurbineClusterModelChain(
188+
power_plant=test_cluster, **parameters)
189+
test_tc_mc.run_model(self.weather_df)
190+
assert_series_equal(test_tc_mc.power_output, power_output_exp)

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