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)
def test_wake_radius_not_implemented():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wfm = PropagateDownwind(site, windTurbines, wake_deficitModel=NoWakeDeficit(),
turbulenceModel=GCLTurbulence())
with pytest.raises(NotImplementedError, match="wake_radius not implemented for NoWakeDeficit"):
wfm(x, y)
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 main():
if __name__ == '__main__':
from py_wake.examples.data.iea37._iea37 import IEA37Site
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines
from py_wake.turbulence_models.gcl import GCLTurbulence
import matplotlib.pyplot as plt
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = GCL(site, windTurbines)
wf_model_local = GCLLocal(site, windTurbines, turbulenceModel=GCLTurbulence())
# run wind farm simulation
sim_res = wf_model(x, y)
sim_res_local = wf_model_local(x, y)
# calculate AEP
aep = sim_res.aep().sum()
aep_local = sim_res_local.aep().sum()
# plot wake map
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4.5), tight_layout=True)
levels = np.arange(0, 10.5, 0.5)
print(wf_model)
flow_map = sim_res.flow_map(wd=30, ws=9.8)
flow_map.plot_wake_map(levels=levels, ax=ax1, plot_colorbar=False)
flow_map.plot_windturbines(ax=ax1)
ax1.set_title('Original Larsen, AEP: %.2f GWh' % aep)
# plot wake map
print(wf_model_local)
flow_map = sim_res_local.flow_map(wd=30, ws=9.8)
flow_map.plot_wake_map(levels=levels, ax=ax2, plot_colorbar=False)
flow_map.plot_windturbines(ax=ax2)
ax2.set_title('Local Larsen, AEP: %.2f GWh' % aep_local)
plt.figure()
flow_map.plot_ti_map(levels=np.arange(0, 1, .01))
plt.title('TI map for GCLLocal with GCL turbulence model')
plt.show()
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()
from py_wake.turbulence_models.crespo import CrespoHernandez
from py_wake.deficit_models.gaussian import BastankhahGaussian
import xarray as xr
@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),