def test_own_turbulence_is_zero(turbulenceModel):
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines, wake_deficitModel=IEA37SimpleBastankhahGaussianDeficit(),
turbulenceModel=turbulenceModel())
sim_res = wf_model([0], [0])
npt.assert_array_equal(sim_res.TI_eff, sim_res.TI.broadcast_like(sim_res.TI_eff))
def test_IEA37WindTurbines():
wt = IEA37WindTurbines()
site = IEA37Site(16)
x, y = site.initial_position.T
for wfm in get_wfms(wt, site, IEA37SimpleBastankhahGaussianDeficit(), SquaredSum()):
sim_res = wfm(x, y, ws=9.8, wd=np.arange(0, 360, 22.5))
npt.assert_almost_equal(sim_res.aep(normalize_probabilities=True).sum() * 1e3, 366941.57116, 5)
def test_double_wind_farm_model():
"""Check that a new wind farm model does not change results of previous"""
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wfm = PropagateDownwind(site, windTurbines, wake_deficitModel=IEA37SimpleBastankhahGaussianDeficit())
aep_ref = wfm(x, y).aep().sum()
PropagateDownwind(site, windTurbines, wake_deficitModel=NoWakeDeficit())
aep = wfm(x, y).aep().sum()
npt.assert_array_equal(aep, aep_ref)
def test_double_wind_farm_model_All2AllIterative():
"""Check that a new wind farm model does not change results of previous"""
site = IEA37Site(64)
x, y = site.initial_position.T
x, y = wt_x, wt_y
windTurbines = IEA37_WindTurbines()
wfm = All2AllIterative(site, windTurbines, wake_deficitModel=IEA37SimpleBastankhahGaussianDeficit())
aep_ref = wfm(x, y).aep().sum()
All2AllIterative(site, windTurbines, wake_deficitModel=NoWakeDeficit())(x, y)
aep = wfm(x, y).aep().sum()
npt.assert_array_equal(aep, aep_ref)
def test_RotorGridAvg_deficit():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines)
flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100)))
plt.plot(flow_map.Y[:, 0], flow_map.WS_eff_xylk[:, 0, 0, 0])
R = windTurbines.diameter() / 2
for name, rotorAvgModel, ref1 in [
('RotorCenter', RotorCenter(), 7.172723970425709),
('RotorGrid2', EqGridRotorAvg(2), 7.495889360682771),
('RotorGrid3', EqGridRotorAvg(3), 7.633415167369133),
('RotorGrid4', EqGridRotorAvg(4), 7.710215921858325),
('RotorGrid100', EqGridRotorAvg(100), 7.820762402628349),
('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 7.826105012683896),
('RotorCGI4', CGIRotorAvg(4), 7.848406907726826),
('RotorCGI4', CGIRotorAvg(7), 7.819900693605533),
('RotorCGI4', CGIRotorAvg(9), 7.82149363932618),
('RotorCGI4', CGIRotorAvg(21), 7.821558905416136)]:
# test with PropagateDownwind
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
rotorAvgModel=rotorAvgModel)
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.WS_eff_ilk[1, 0, 0], ref1)
# test with All2AllIterative
wfm = All2AllIterative(site, windTurbines,
IEA37SimpleBastankhahGaussianDeficit(),
rotorAvgModel=rotorAvgModel,
superpositionModel=SquaredSum())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.WS_eff_ilk[1, 0, 0], ref1)
plt.plot([-R, R], [sim_res.WS_eff_ilk[1, 0, 0]] * 2, label=name)
if 0:
plt.legend()
plt.show()
plt.close('all')
def test_RotorGridAvg_ti():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel())
flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100)))
plt.plot(flow_map.Y[:, 0], flow_map.TI_eff_xylk[:, 0, 0, 0])
R = windTurbines.diameter() / 2
for name, rotorAvgModel, ref1 in [
('RotorCenter', RotorCenter(), 0.22292190804089568),
('RotorGrid2', EqGridRotorAvg(2), 0.2111162769995657),
('RotorGrid3', EqGridRotorAvg(3), 0.2058616982653193),
('RotorGrid4', EqGridRotorAvg(4), 0.2028701990648858),
('RotorGrid100', EqGridRotorAvg(100), 0.1985255601976247),
('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 0.1982984399750206)]:
# test with PropagateDownwind
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
rotorAvgModel=rotorAvgModel,
turbulenceModel=STF2017TurbulenceModel())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1)
# test with All2AllIterative
wfm = All2AllIterative(site, windTurbines,
IEA37SimpleBastankhahGaussianDeficit(),
rotorAvgModel=rotorAvgModel,
superpositionModel=SquaredSum(),
turbulenceModel=STF2017TurbulenceModel())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1)
plt.plot([-R, R], [sim_res.TI_eff_ilk[1, 0, 0]] * 2, label=name)
if 0:
plt.legend()
plt.show()
plt.close('all')
def test_RotorAvg_deficit():
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel())
flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100)))
plt.plot(flow_map.Y[:, 0], flow_map.TI_eff_xylk[:, 0, 0, 0])
R = windTurbines.diameter() / 2
for name, rotorAvgModel, ref1 in [
('None', None, 0.22292190804089568),
('RotorCenter', RotorCenter(), 0.22292190804089568),
('RotorGrid100', EqGridRotorAvg(100), 0.1989725533174574),
('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 0.19874837617113356),
('RotorCGI4', CGIRotorAvg(4), 0.19822024411411204),
('RotorCGI4', CGIRotorAvg(21), 0.1989414764606653)]:
# test with PropagateDownwind
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel(rotorAvgModel=rotorAvgModel))
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1, err_msg=name)
# test with All2AllIterative
wfm = All2AllIterative(site, windTurbines,
IEA37SimpleBastankhahGaussianDeficit(),
turbulenceModel=STF2017TurbulenceModel(rotorAvgModel=rotorAvgModel),
superpositionModel=SquaredSum())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1)
plt.plot([-R, R], [sim_res.WS_eff_ilk[1, 0, 0]] * 2, label=name)
if 0:
plt.legend()
plt.show()
plt.close()
14285.22744, 31751.29488, 75140.15677, 17597.10319, 11721.21226,
7838.84383
])),
(NOJLocalDeficit(), (335151.6404628441, [
8355.71335, 7605.92379, 10654.172, 13047.6971, 19181.46408,
23558.34198, 36738.52415, 38663.44595, 21056.39764, 12042.79324,
13813.46269, 30999.42279, 63947.61202, 17180.40299, 11334.12323,
6972.14345
])),
(BastankhahGaussianDeficit(), (355971.9717035484, [
9143.74048, 8156.71681, 11311.92915, 13955.06316, 19807.65346,
25196.64182, 39006.65223, 41463.31044, 23042.22602, 12978.30551,
14899.26913, 32320.21637, 67039.04091, 17912.40907, 12225.04134,
7513.75582
])),
(IEA37SimpleBastankhahGaussianDeficit(),
read_iea37_windfarm(iea37_path + 'iea37-ex16.yaml')[2]),
(FugaDeficit(LUT_path=tfp +
'fuga/2MW/Z0=0.00408599Zi=00400Zeta0=0.00E+00/'),
(404441.6306021485, [
9912.33731, 9762.05717, 12510.14066, 15396.76584, 23017.66483,
27799.7161, 43138.41606, 49623.79059, 24979.09001, 15460.45923,
16723.02619, 35694.35526, 77969.14805, 19782.41376, 13721.45739,
8950.79218
])),
(GCLDeficit(), (370863.6246093183, [
9385.75387, 8768.52105, 11450.13309, 14262.42186, 21178.74926,
25751.59502, 39483.21753, 44573.31533, 23652.09976, 13924.58752,
15106.11692, 32840.02909, 71830.22035, 18200.49805, 12394.7626,
8061.6033
])),
wf_model = All2AllIterative(site,
windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
blockage_deficitModel=SelfSimilarityDeficit(),
deflectionModel=JimenezWakeDeflection(),
turbulenceModel=STF2005TurbulenceModel())
assert str(
wf_model
) == "All2AllIterative(EngineeringWindFarmModel, NOJDeficit-wake, SelfSimilarityDeficit-blockage, RotorCenter-rotor-average, SquaredSum-superposition, JimenezWakeDeflection-deflection, STF2005TurbulenceModel-turbulence)"
@pytest.mark.parametrize(
'wake_deficitModel,deflectionModel,superpositionModel',
[(NOJDeficit(), None, SquaredSum()),
(IEA37SimpleBastankhahGaussianDeficit(), JimenezWakeDeflection(),
WeightedSum())])
def test_huge_flow_map(wake_deficitModel, deflectionModel, superpositionModel):
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wake_model = PropagateDownwind(site,
windTurbines,
wake_deficitModel=wake_deficitModel,
superpositionModel=superpositionModel,
deflectionModel=deflectionModel,
turbulenceModel=STF2005TurbulenceModel())
n_wt = 2
flow_map = wake_model(*site.initial_position[:n_wt].T,
wd=[0, 90]).flow_map(HorizontalGrid(resolution=1000))
# check that deficit matrix > 10MB (i.e. it enters the memory saving loop)
assert (np.prod(flow_map.WS_eff_xylk.shape) * n_wt * 8 / 1024**2) > 10
# test that the result is equal to last run (no evidens that these number are correct)
[(NOJDeficit(), (367205.0846866496, [9833.86287, 8416.99088, 10820.37673, 13976.26422, 22169.66036,
25234.9215, 37311.64388, 42786.37028, 24781.33444, 13539.82115,
14285.22744, 31751.29488, 75140.15677, 17597.10319, 11721.21226,
7838.84383])),
(NOJLocalDeficit(), (335151.6404628441, [8355.71335, 7605.92379, 10654.172, 13047.6971, 19181.46408,
23558.34198, 36738.52415, 38663.44595, 21056.39764, 12042.79324,
13813.46269, 30999.42279, 63947.61202, 17180.40299, 11334.12323,
6972.14345])),
(BastankhahGaussianDeficit(), (355971.9717035484,
[9143.74048, 8156.71681, 11311.92915, 13955.06316, 19807.65346,
25196.64182, 39006.65223, 41463.31044, 23042.22602, 12978.30551,
14899.26913, 32320.21637, 67039.04091, 17912.40907, 12225.04134,
7513.75582])),
(IEA37SimpleBastankhahGaussianDeficit(), read_iea37_windfarm(iea37_path + 'iea37-ex16.yaml')[2]),
(FugaDeficit(LUT_path=tfp + 'fuga/2MW/Z0=0.00014617Zi=00399Zeta0=0.00E+0/'),
(398938.8941139709, [9632.92248, 9733.98766, 12462.98413, 15332.30502, 22199.91899,
27683.32851, 42975.80734, 49481.10395, 24274.96466, 15416.63681,
16681.20957, 35508.27583, 75263.59612, 19679.2854, 13687.14632,
8925.42131])),
(GCLDeficit(), (370863.6246093183,
[9385.75387, 8768.52105, 11450.13309, 14262.42186, 21178.74926,
25751.59502, 39483.21753, 44573.31533, 23652.09976, 13924.58752,
15106.11692, 32840.02909, 71830.22035, 18200.49805, 12394.7626,
8061.6033])),
(GCLLocalDeficit(), (381187.36105425097,
[9678.85358, 9003.65526, 11775.06899, 14632.42259, 21915.85495,
26419.65189, 40603.68618, 45768.58091, 24390.71103, 14567.43106,
15197.82861, 32985.67922, 75062.92788, 18281.21981, 12470.01322,
8433.77587])),
def test_str():
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
blockage_deficitModel=SelfSimilarityDeficit(),
deflectionModel=JimenezWakeDeflection(),
turbulenceModel=STF2005TurbulenceModel())
assert str(wf_model) == "All2AllIterative(EngineeringWindFarmModel, NOJDeficit-wake, SelfSimilarityDeficit-blockage, RotorCenter-rotor-average, SquaredSum-superposition, JimenezWakeDeflection-deflection, STF2005TurbulenceModel-turbulence)"
@pytest.mark.parametrize('wake_deficitModel,deflectionModel,superpositionModel',
[(NOJDeficit(), None, SquaredSum()),
(IEA37SimpleBastankhahGaussianDeficit(), JimenezWakeDeflection(), WeightedSum())])
def test_huge_flow_map(wake_deficitModel, deflectionModel, superpositionModel):
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wake_model = PropagateDownwind(site, windTurbines, wake_deficitModel=wake_deficitModel,
superpositionModel=superpositionModel, deflectionModel=deflectionModel,
turbulenceModel=STF2005TurbulenceModel())
n_wt = 2
flow_map = wake_model(*site.initial_position[:n_wt].T, wd=0).flow_map(HorizontalGrid(resolution=1000))
# check that deficit matrix > 10MB (i.e. it enters the memory saving loop)
assert (np.prod(flow_map.WS_eff_xylk.shape) * n_wt * 8 / 1024**2) > 10
assert flow_map.WS_eff_xylk.shape == (1000, 1000, 1, 1)
def test_aep():
site = UniformSite([1], ti=0)