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.superposition_models import LinearSum
from py_wake.wind_farm_models import All2AllIterative
from py_wake.deficit_models.no_wake import NoWakeDeficit
import matplotlib.pyplot as plt
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
vd = VortexDipole()
plt.figure()
noj_vd = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=vd)
flow_map = noj_vd(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
clevels = np.array([
.6, .7, .8, .9, .95, .98, .99, .995, .998, .999, 1., 1.01, 1.02
]) * 10.
flow_map.plot_wake_map(levels=clevels)
plt.title('Vortex Dipole')
plt.ylabel("Crosswind distance [y/R]")
plt.xlabel("Downwind distance [x/R]")
plt.show()
# run wind farm simulation
sim_res = noj_vd(x, y, wd=[0, 30, 45, 60, 90], ws=[5, 10, 15])
# calculate AEP
aep = sim_res.aep().sum()
# plot wake map
plt.figure()
print(noj_vd)
flow_map = sim_res.flow_map(wd=0, ws=10)
flow_map.plot_wake_map(levels=clevels, plot_colorbar=False)
plt.title('Vortex Dipole model, AEP: %.3f GWh' % aep)
plt.show()
def test_wake_map():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wake_model = NOJ(windTurbines)
aep = AEPCalculator(site, windTurbines, wake_model)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
X, Y, Z = aep.wake_map(x_j, y_j, 110, x, y, wd=[0], ws=[9])
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z) # , np.arange(2, 10, .01))
plt.colorbar(c)
windTurbines.plot(x, y)
plt.show()
ref = [3.27, 3.27, 9.0, 7.46, 7.46, 7.46, 7.46, 7.31, 7.31, 7.31, 7.31, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3]
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def test_IEA37SimpleBastankhahGaussian_all_ex():
"""check SBG matches IEA37 value in file"""
for n_wt in [9, 16, 36, 64]:
x, y, aep_ref = read_iea37_windfarm(iea37_path + 'iea37-ex%d.yaml' % n_wt)
if 0:
plt.plot(x, y, '2k')
for i, (x_, y_) in enumerate(zip(x, y)):
plt.annotate(i, (x_, y_))
plt.axis('equal')
plt.show()
site = IEA37Site(n_wt)
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
aep_ilk = wake_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
# test that the result is equal to results provided for IEA task 37
npt.assert_almost_equal(aep_ref[0], aep_MW_l.sum(), 5)
npt.assert_array_almost_equal(aep_ref[1], aep_MW_l, 5)
def test_models_with_BastankhahGaussian(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 [
BastankhahGaussian(site,
windTurbines,
turbulenceModel=turbulence_model)
]:
res = wake_model(x, y)
# print(turbulence_model.__class__.__name__, np.round(res.TI_eff_ilk[:, 0, 0], 3).tolist())
if 0:
res.flow_map(wd=0).plot_ti_map()
plt.show()
npt.assert_array_almost_equal(res.TI_eff_ilk[:, 0, 0], ref_ti, 3)
def test_aep():
site = UniformSite([1], ti=0)
windTurbines = IEA37_WindTurbines()
wfm = NOJ(site, windTurbines)
sim_res = wfm([0], [0], wd=270)
npt.assert_almost_equal(sim_res.aep().sum(), 3.35 * 24 * 365 / 1000)
npt.assert_almost_equal(
sim_res.aep(normalize_probabilities=True).sum(),
3.35 * 24 * 365 / 1000)
npt.assert_equal(sim_res.aep().data.sum(), wfm.aep([0], [0], wd=270))
npt.assert_almost_equal(
sim_res.aep(normalize_probabilities=True).sum(),
wfm.aep([0], [0], wd=270, normalize_probabilities=True))
npt.assert_almost_equal(
sim_res.aep(with_wake_loss=False).sum(),
wfm.aep([0], [0], wd=270, with_wake_loss=False))
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 main():
if __name__ == '__main__':
from py_wake import Fuga
from py_wake.examples.data.iea37._iea37 import IEA37Site, IEA37_WindTurbines
site = IEA37Site(16)
x, y = [0, 600, 1200], [0, 0, 0] # site.initial_position[:2].T
windTurbines = IEA37_WindTurbines()
from py_wake.tests.test_files import tfp
path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+0/'
noj = Fuga(path,
site,
windTurbines,
deflectionModel=JimenezWakeDeflection())
yaw_ilk = np.zeros((len(x), 1, 1))
yaw_ilk[:, 0, 0] = [-30, 30, 0]
noj(x, y, yaw_ilk=yaw_ilk, wd=270, ws=10).flow_map().plot_wake_map()
import matplotlib.pyplot as plt
plt.show()
pass
def test_save_load():
site = UniformSite([1], ti=0)
windTurbines = IEA37_WindTurbines()
wfm = NOJ(site, windTurbines)
sim_res1 = wfm([0], [0], wd=270)
sim_res1.save(tfp + "tmp.nc")
sim_res2 = SimulationResult.load(tfp + 'tmp.nc', wfm)
for sim_res in [sim_res1, sim_res2]:
npt.assert_almost_equal(sim_res.aep().sum(), 3.35 * 24 * 365 / 1000)
npt.assert_almost_equal(sim_res.aep(normalize_probabilities=True).sum(), 3.35 * 24 * 365 / 1000)
npt.assert_equal(sim_res.aep().data.sum(), wfm.aep([0], [0], wd=270))
npt.assert_almost_equal(sim_res.aep(normalize_probabilities=True).sum(),
wfm.aep([0], [0], wd=270, normalize_probabilities=True))
npt.assert_almost_equal(sim_res.aep(with_wake_loss=False).sum(),
wfm.aep([0], [0], wd=270, with_wake_loss=False))
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),
]:
res = wake_model(x, y)
# print(turbulence_model.__class__.__name__, np.round(res.TI_eff_ilk[:, 0, 0], 3).tolist())
if 0:
res.flow_map(wd=0).plot_ti_map()
plt.show()
npt.assert_array_almost_equal(res.TI_eff_ilk[:, 0, 0], ref_ti, 3)
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_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_IEA37SimpleBastankhahGaussian_wake_map():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wake_model(x, y, wd=0, ws=9).flow_map(HorizontalGrid(x_j, y_j))
X, Y, Z = flow_map.X, flow_map.Y, flow_map.WS_eff_xylk[:, :, 0, 0]
# test that the result is equal to last run (no evidens that these number are correct)
ref = [
3.32, 4.86, 7.0, 8.1, 7.8, 7.23, 6.86, 6.9, 7.3, 7.82, 8.11, 8.04,
7.87, 7.79, 7.85, 8.04, 8.28
]
if 0:
flow_map.plot_wake_map()
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-')
plt.show()
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()
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 main():
if __name__ == '__main__':
for n_wt in [9, 16, 36, 64]:
x, y, aep_ref = read_iea37_windfarm(iea37_path +
'iea37-ex%d.yaml' % n_wt)
if 0:
import matplotlib.pyplot as plt
plt.plot(x, y, '2k')
for i, (x_, y_) in enumerate(zip(x, y)):
plt.annotate(i, (x_, y_))
plt.axis('equal')
plt.show()
site = IEA37Site(n_wt)
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
aep = wake_model(x, y, wd=np.arange(0, 360, 22.5), ws=[9.8]).aep()
# Compare to reference results provided for IEA task 37
print(n_wt, aep_ref[0] * 1e-3, aep)
def test_IEA37SimpleBastankhahGaussian_ex16():
_, _, freq = read_iea37_windrose(iea37_path + "iea37-windrose.yaml")
n_wt = 16
x, y, aep_ref = read_iea37_windfarm(iea37_path + 'iea37-ex%d.yaml' % n_wt)
if 0:
import matplotlib.pyplot as plt
plt.plot(x, y, '2k')
for i, (x_, y_) in enumerate(zip(x, y)):
plt.annotate(i, (x_, y_))
plt.axis('equal')
plt.show()
site = UniformSite(freq, ti=0.75)
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(windTurbines)
aep = AEPCalculator(site, windTurbines, wake_model)
aep_ilk = aep.calculate_AEP(x, y, wd=np.arange(0, 360, 22.5), ws=[9.8])
aep_MW_l = aep_ilk.sum((0, 2)) * 1000
# test that the result is equal to results provided for IEA task 37
npt.assert_almost_equal(aep_ref[0], aep_MW_l.sum(), 5)
npt.assert_array_almost_equal(aep_ref[1], aep_MW_l, 5)
def test_aep_map():
site = IEA37Site(16)
x = [0, 0]
y = [0, 200]
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(windTurbines)
aep = AEPCalculator(site, windTurbines, wake_model)
# print(aep.calculate_AEP([0], [0]).sum())
x_j = np.arange(-150, 150, 20)
y_j = np.arange(-250, 250, 20)
X, Y, Z = aep.aep_map(x_j, y_j, 0, x, y, wd=[0], ws=np.arange(3, 25))
# print(y_j)
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z, 100) # , np.arange(2, 10, .01))
plt.colorbar(c)
windTurbines.plot(x, y)
plt.show()
# print(np.round(Z[17], 2).tolist())
ref = [21.5, 21.4, 21.02, 20.34, 18.95, 16.54, 13.17, 10.17, 10.17, 13.17, 16.54, 18.95, 20.34, 21.02, 21.4]
npt.assert_array_almost_equal(Z[17], ref, 2)
def test_wake_map():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = NOJ(site, windTurbines)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wake_model(x, y, wd=[0],
ws=[9]).flow_map(HorizontalGrid(x_j, y_j, 110))
Z = flow_map.WS_eff_xylk.mean((2, 3))
if 0:
flow_map.plot_wake_map()
plt.show()
ref = [
3.27, 3.27, 9.0, 7.46, 7.46, 7.46, 7.46, 7.31, 7.31, 7.31, 7.31, 8.3,
8.3, 8.3, 8.3, 8.3, 8.3
]
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
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 import NOJ
# setup site, turbines and flow model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
import matplotlib.pyplot as plt
_, (ax1, ax2) = plt.subplots(1, 2)
for ax, wf_model, lbl in [(ax1, NOJ(site, windTurbines, turbulenceModel=STF2005TurbulenceModel()), 'STF2005'),
(ax2, NOJ(site, windTurbines, turbulenceModel=STF2017TurbulenceModel()), 'STF2017')]:
sim_res = wf_model(x, y)
print(sim_res.TI_eff_ilk[:, 0])
# plot wake map
flow_map = sim_res.flow_map(wd=0, ws=9.8)
c = flow_map.plot_ti_map(ax=ax)
ax.set_title('Turbulence intensity calculated by %s' % lbl)
plt.show()
def test_set_gradients():
wt = IEA37_WindTurbines()
wt.set_gradient_funcs(lambda ws: np.where((ws > 4) & (ws <= 9.8),
100000 * ws, # not the right gradient, but similar to the reference
0), lambda ws: 0)
with use_autograd_in([WindTurbines, iea37_reader]):
ws_lst = np.arange(3, 25, .1)
plt.plot(ws_lst, wt.power(ws_lst))
ws_pts = np.array([3., 6., 9., 12.])
dpdu_lst = np.diag(autograd(wt.power)(ws_pts))
if 0:
for dpdu, ws in zip(dpdu_lst, ws_pts):
plot_gradients(wt.power(ws), dpdu, ws, "", 1)
plt.show()
dpdu_ref = np.where((ws_pts > 4) & (ws_pts <= 9.8),
100000 * ws_pts,
0)
npt.assert_array_almost_equal(dpdu_lst, dpdu_ref)
def test_dAEP_2wt():
site = Hornsrev1Site()
iea37_site = IEA37Site(16)
wt = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site, wt)
x, y = iea37_site.initial_position[np.array([0, 2, 5, 8, 14])].T
# plot 2 wt case
x, y = np.array([[0, 130 * 4], [0, 0]], dtype=np.float)
x_lst = np.array([0., 1.]) * np.arange(1, 600, 10)[:, na]
kwargs = {'ws': [10], 'wd': [270]}
_, (ax1, ax2) = plt.subplots(1, 2, sharey=False)
ax1.plot(x_lst[:, 1], [wfm.aep(x_, y, **kwargs) for x_ in x_lst])
ax1.set_xlabel('Downwind distance [m]')
ax1.set_ylabel('AEP [GWh]')
x_ = x_lst[20]
ax1.set_title("Center line")
for grad in [fd, cs, autograd]:
dAEPdx = wfm.dAEPdn(0, grad)(x_, y, **kwargs)[1]
npt.assert_almost_equal(dAEPdx / 360, 3.976975605364392e-06, (10, 5)[grad == fd])
plot_gradients(wfm.aep(x_, y, **kwargs), dAEPdx, x_[1], grad.__name__, step=100, ax=ax1)
y_lst = np.array([0, 1.]) * np.arange(-100, 100, 5)[:, na]
ax2.plot(y_lst[:, 1], [wfm.aep(x, y_, **kwargs) for y_ in y_lst])
ax2.set_xlabel('Crosswind distance [m]')
ax2.set_ylabel('AEP [GWh]')
y_ = y_lst[25]
ax2.set_title("%d m downstream" % x[1])
for grad in [fd, cs, autograd]:
dAEPdy = wfm.dAEPdn(1, grad)(x, y_, **kwargs)[1]
plot_gradients(wfm.aep(x, y_, **kwargs), dAEPdy, y_[1], grad.__name__, step=50, ax=ax2)
npt.assert_almost_equal(dAEPdy / 360, 3.794435973860448e-05, (10, 5)[grad == fd])
if 0:
plt.legend()
plt.show()
plt.close()
def test_BastankhahGaussian_ex16():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = BastankhahGaussian(site, windTurbines)
if 0:
windTurbines.plot(x, y)
plt.show()
aep_ilk = wake_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
# test that the result is equal to last run (no evidens that these number are correct)
aep_ref = (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
])
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_deficitModel_wake_map_convection_all2all(deficitModel, ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site,
windTurbines,
wake_deficitModel=deficitModel,
superpositionModel=WeightedSum(),
blockage_deficitModel=VortexDipole(),
turbulenceModel=STF2017TurbulenceModel())
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_deficitModel_wake_map(deficitModel, 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())
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])
plt.plot(ref, label='ref')
plt.plot(mean_ref, label='Mean ref')
plt.legend()
plt.show()
npt.assert_array_almost_equal(ref, Z[49, 100:133:2], 2)
# check that ref is reasonable
npt.assert_allclose(ref[2:], mean_ref[2:], atol=2.6)
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()
def test_gradients():
wt = IEA37_WindTurbines()
wt.enable_autograd()
ws_lst = np.arange(3, 25, .1)
ws_pts = np.array([3., 6., 9., 12.])
dpdu_lst = autograd(wt.power)(ws_pts)
if 0:
plt.plot(ws_lst, wt.power(ws_lst))
for dpdu, ws in zip(dpdu_lst, ws_pts):
plot_gradients(wt.power(ws), dpdu, ws, "", 1)
plt.show()
dpdu_ref = np.where((ws_pts > 4) & (ws_pts <= 9.8),
3 * 3350000 * (ws_pts - 4)**2 / (9.8 - 4)**3, 0)
npt.assert_array_almost_equal(dpdu_lst, dpdu_ref)
fd_dpdu_lst = fd(wt.power)(ws_pts)
npt.assert_array_almost_equal(fd_dpdu_lst, dpdu_ref, 0)
cs_dpdu_lst = cs(wt.power)(ws_pts)
npt.assert_array_almost_equal(cs_dpdu_lst, dpdu_ref)
def test_BastankhahGaussian_wake_map():
site = IEA37_Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = BastankhahGaussian(windTurbines)
aep = AEPCalculator(site, windTurbines, wake_model)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
X, Y, Z = aep.wake_map(x_j, y_j, 110, x, y, wd=[0], ws=[9])
# test that the result is equal to last run (no evidens that these number are correct)
ref = [
0.18, 3.6, 7.27, 8.32, 7.61, 6.64, 5.96, 6.04, 6.8, 7.69, 8.08, 7.87,
7.59, 7.46, 7.55, 7.84, 8.19
]
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z, np.arange(-.25, 9.1, .01))
plt.colorbar(c)
windTurbines.plot(x, y)
plt.show()
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def test_IEA37SimpleBastankhahGaussian_wake_map():
site = IEA37_Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(windTurbines)
aep = AEPCalculator(site, windTurbines, wake_model)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
X, Y, Z = aep.wake_map(x_j, y_j, 110, x, y, wd=[0], ws=[9])
# test that the result is equal to last run (no evidens that these number are correct)
ref = [
3.32, 4.86, 7.0, 8.1, 7.8, 7.23, 6.86, 6.9, 7.3, 7.82, 8.11, 8.04,
7.87, 7.79, 7.85, 8.04, 8.28
]
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z, np.arange(2, 9.1, .01))
plt.colorbar(c)
site.plot_windturbines(x, y)
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '-.')
plt.show()
def test_wake_map():
_, _, freq = read_iea37_windrose(iea37_path + "iea37-windrose.yaml")
n_wt = 16
x, y, _ = read_iea37_windfarm(iea37_path + 'iea37-ex%d.yaml' % n_wt)
site = UniformSite(freq, ti=0.75)
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = NOJ(windTurbines)
aep = AEPCalculator(site, windTurbines, wake_model)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
X, Y, Z = aep.wake_map(x_j, y_j, 110, x, y, wd=[0], ws=[9])
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z) # , np.arange(2, 10, .01))
plt.colorbar(c)
site.plot_windturbines(x, y)
plt.show()
ref = [3.27, 3.27, 9.0, 7.46, 7.46, 7.46, 7.46, 7.31, 7.31, 7.31, 7.31, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3]
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
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 import NOJ
import matplotlib.pyplot as plt
# setup site, turbines and wakemodel
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = NOJ(site, windTurbines, turbulenceModel=GCLTurbulence())
# calculate AEP
sim_res = wf_model(x, y)
print(sim_res.TI_eff_ilk.flatten())
# plot wake mape
aep = sim_res.aep().sum()
flow_map = sim_res.flow_map(wd=0, ws=9.8)
flow_map.plot_ti_map()
plt.title('AEP: %.2f GWh' % aep)
plt.show()
def test_BastankhahGaussian_wake_map():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = BastankhahGaussian(site, windTurbines)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wake_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]
# test that the result is equal to last run (no evidens that these number are correct)
ref = [
0.18, 3.6, 7.27, 8.32, 7.61, 6.64, 5.96, 6.04, 6.8, 7.69, 8.08, 7.87,
7.59, 7.46, 7.55, 7.84, 8.19
]
if 0:
flow_map.plot_wake_map()
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-')
windTurbines.plot(x, y)
plt.show()
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
