def test_IEA37_ex16(deficitModel, aep_ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = PropagateDownwind(site,
windTurbines,
wake_deficitModel=deficitModel,
superpositionModel=SquaredSum(),
turbulenceModel=GCLTurbulence())
aep_ilk = wf_model(x, y, wd=np.arange(0, 360, 22.5),
ws=[9.8]).aep_ilk(normalize_probabilities=True)
aep_MW_l = aep_ilk.sum((0, 2)) * 1000
# check if ref is reasonable
aep_est = 16 * 3.35 * 24 * 365 * .8 # n_wt * P_rated * hours_pr_year - 20% wake loss = 375628.8
npt.assert_allclose(aep_ref[0], aep_est, rtol=.11)
npt.assert_allclose(aep_ref[1], [
9500, 8700, 11500, 14300, 21300, 25900, 39600, 44300, 23900, 13900,
15200, 33000, 72100, 18300, 12500, 8000
],
rtol=.15)
npt.assert_almost_equal(aep_MW_l.sum(), aep_ref[0], 5)
npt.assert_array_almost_equal(aep_MW_l, aep_ref[1], 5)
def test_wake_radius_not_implemented():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wfm = PropagateDownwind(site,
windTurbines,
wake_deficitModel=SelfSimilarityDeficit(),
turbulenceModel=GCLTurbulence())
with pytest.raises(
NotImplementedError,
match="wake_radius not implemented for SelfSimilarityDeficit"):
wfm(x, y)
def test_wake_radius(deficitModel, wake_radius_ref):
mean_ref = [105, 68, 135, 93, 123]
# check that ref is reasonable
npt.assert_allclose(wake_radius_ref, mean_ref, rtol=.5)
npt.assert_array_almost_equal(
deficitModel.wake_radius(D_src_il=np.reshape([100, 50, 100, 100, 100],
(5, 1)),
dw_ijlk=np.reshape([500, 500, 1000, 500, 500],
(5, 1, 1, 1)),
ct_ilk=np.reshape([.8, .8, .8, .4, .8],
(5, 1, 1)),
TI_ilk=np.reshape([.1, .1, .1, .1, .05],
(5, 1, 1)),
TI_eff_ilk=np.reshape([.1, .1, .1, .1, .05],
(5, 1, 1)))[:, 0, 0, 0],
wake_radius_ref)
# Check that it works when called from WindFarmModel
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wfm = PropagateDownwind(site,
windTurbines,
wake_deficitModel=deficitModel,
turbulenceModel=GCLTurbulence())
wfm(x=[0, 500], y=[0, 0], wd=[30], ws=[10])
if 0:
sim_res = wfm([0], [0], wd=[270], ws=10)
sim_res.flow_map(
HorizontalGrid(x=np.arange(-100, 1500, 10))).WS_eff.plot()
x = np.arange(0, 1500, 10)
wr = deficitModel.wake_radius(D_src_il=np.reshape([130], (1, 1)),
dw_ijlk=np.reshape(x, (1, len(x), 1, 1)),
ct_ilk=sim_res.CT.values,
TI_ilk=np.reshape(
sim_res.TI.values, (1, 1, 1)),
TI_eff_ilk=sim_res.TI_eff.values)[0, :,
0, 0]
plt.title(deficitModel.__class__.__name__)
plt.plot(x, wr)
plt.plot(x, -wr)
plt.axis('equal')
plt.show()
def test_wake_radius_not_implemented():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
class MyDeficitModel(WakeDeficitModel):
args4deficit = ['WS_ilk', 'dw_ijlk', 'cw_ijlk']
def calc_deficit(self, WS_ilk, dw_ijlk, cw_ijlk, **_):
# 10% deficit in downstream triangle
ws_10pct_ijlk = 0.1 * WS_ilk[:, na]
triangle_ijlk = ((.2 * dw_ijlk) > cw_ijlk)
return ws_10pct_ijlk * triangle_ijlk
wfm = PropagateDownwind(site,
windTurbines,
wake_deficitModel=MyDeficitModel(),
turbulenceModel=GCLTurbulence())
with pytest.raises(NotImplementedError,
match="wake_radius not implemented for MyDeficitModel"):
wfm(x, y)
def test_deficitModel_wake_map_convection(deficitModel, ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = PropagateDownwind(site,
windTurbines,
wake_deficitModel=deficitModel,
superpositionModel=WeightedSum(),
turbulenceModel=GCLTurbulence())
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wf_model(x, y, wd=0, ws=9).flow_map(HorizontalGrid(x_j, y_j))
X, Y = flow_map.X, flow_map.Y
Z = flow_map.WS_eff_xylk[:, :, 0, 0]
mean_ref = [
3.2, 4.9, 8., 8.2, 7.9, 7.4, 7., 7., 7.4, 7.9, 8.1, 8.1, 8., 7.8, 7.9,
8.1, 8.4
]
if 0:
flow_map.plot_wake_map()
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-')
windTurbines.plot(x, y)
plt.figure()
plt.plot(Z[49, 100:133:2], label='Actual')
plt.plot(ref, label='Reference')
plt.plot(mean_ref, label='Mean ref')
plt.legend()
plt.show()
# check that ref is reasonable
npt.assert_allclose(ref[2:], mean_ref[2:], atol=2.6)
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
_module = loader.find_module(module_name).load_module(module_name)
for n in dir(_module):
v = _module.__dict__[n]
if inspect.isclass(v) and issubclass(v, TurbulenceModel) and \
v not in [TurbulenceModel]:
all_turbulence_modules.append(v)
return all_turbulence_modules
@pytest.mark.parametrize('turbulence_model,ref_ti', [
(STF2005TurbulenceModel(), [0.075, 0.075, 0.075, 0.104, 0.167, 0.124, 0.075, 0.075,
0.075, 0.075, 0.075, 0.075, 0.104, 0.136, 0.098, 0.104]),
(STF2017TurbulenceModel(), [0.075, 0.075, 0.075, 0.114, 0.197, 0.142, 0.075, 0.075,
0.075, 0.075, 0.075, 0.075, 0.115, 0.158, 0.108, 0.115]),
(GCLTurbulence(), [0.075, 0.075, 0.075, 0.117, 0.151, 0.135, 0.075, 0.075, 0.075,
0.075, 0.075, 0.075, 0.128, 0.127, 0.117, 0.128]),
(CrespoHernandez(), [0.075, 0.075, 0.075, 0.129, 0.17, 0.151, 0.075,
0.075, 0.075, 0.075, 0.075, 0.075, 0.143, 0.141, 0.13, 0.143])
])
def test_models_with_noj(turbulence_model, ref_ti):
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
for wake_model in [NOJ(site, windTurbines, turbulenceModel=turbulence_model),
All2AllIterative(site, windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
turbulenceModel=turbulence_model),