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_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_blockage_map(setup):
site, windTurbines, ss = setup
wm = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=ss)
flow_map = wm(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
X_j, Y_j = flow_map.XY
WS_eff = flow_map.WS_eff_xylk[:, :, 0, 0]
if 0:
plt.contourf(X_j, Y_j, WS_eff)
plt.plot(X_j[200, ::50], Y_j[200, ::50], '.-')
plt.plot(X_j[250, ::50], Y_j[250, ::50], '.-')
print(list(np.round(WS_eff[200, ::50], 6)))
print(list(np.round(WS_eff[250, ::50], 6)))
ss.windTurbines.plot([0], [0], wd=[270])
plt.show()
npt.assert_array_almost_equal(WS_eff[200, ::50], [
9.940967, 9.911659, 9.855934, 9.736016, 9.44199, 10.0, 10.0, 10.0,
10.0, 10.0
])
npt.assert_array_almost_equal(WS_eff[250, ::50], [
9.937601, 9.90397, 9.834701, 9.659045, 9.049764, 10.0, 10.0, 10.0,
10.0, 10.0
])
def test_wake_and_blockage(setup):
site, windTurbines, ss = setup
noj_ss = All2AllIterative(site,
windTurbines,
wake_deficitModel=NOJDeficit(),
blockage_deficitModel=ss,
superpositionModel=LinearSum())
flow_map = noj_ss(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
X_j, Y_j = flow_map.XY
WS_eff = flow_map.WS_eff_xylk[:, :, 0, 0]
npt.assert_array_almost_equal(WS_eff[200, ::50], [
9.940967, 9.911659, 9.855934, 9.736016, 9.44199, 4.560631, 5.505472,
6.223921, 6.782925, 7.226399
])
npt.assert_array_almost_equal(WS_eff[250, ::50], [
9.937601, 9.90397, 9.834701, 9.659045, 9.049764, 4.560631, 5.505472,
6.223921, 6.782925, 7.226399
])
if 0:
plt.contourf(X_j, Y_j, WS_eff)
plt.plot(X_j[200, ::50], Y_j[200, ::50], '.-')
plt.plot(X_j[250, ::50], Y_j[250, ::50], '.-')
print(list(np.round(WS_eff[200, ::50], 6)))
print(list(np.round(WS_eff[250, ::50], 6)))
ss.windTurbines.plot([0], [0], wd=[270])
plt.show()
def test_wake_blockage_split(upstream_only, ref):
class MyWakeModel(WakeDeficitModel):
args4deficit = []
def calc_deficit(self, dw_ijlk, **kwargs):
return np.ones_like(dw_ijlk) * 2
class MyBlockageModel(BlockageDeficitModel):
args4deficit = []
def calc_deficit(self, dw_ijlk, **kwargs):
return np.ones_like(dw_ijlk)
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(
site,
windTurbines,
wake_deficitModel=MyWakeModel(),
blockage_deficitModel=MyBlockageModel(upstream_only=upstream_only))
sim_res = wf_model([0], [0], ws=10, wd=270)
fm = sim_res.flow_map(XYGrid(x=[-100, 100], y=[-100, 0, 100]))
if 0:
sim_res.flow_map().plot_wake_map()
print(fm.WS_eff.values.squeeze().T)
plt.show()
npt.assert_array_equal(fm.WS_eff.values.squeeze().T, ref)
def test_blockage_map(setup, blockage_model, center_ref, side_ref):
site, windTurbines = setup
wm = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=blockage_model())
xy = np.linspace(-200, 200, 500)
flow_map = wm(x=[0], y=[0], wd=[270],
ws=[10]).flow_map(XYGrid(x=xy[::50], y=xy[[190, 250]]))
X_j, Y_j = flow_map.XY
WS_eff = flow_map.WS_eff_xylk[:, :, 0, 0]
if debug:
flow_map_full = wm(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
X_j_full, Y_j_full = flow_map_full.XY
WS_eff_full = flow_map_full.WS_eff_xylk[:, :, 0, 0]
plt.contourf(X_j_full, Y_j_full, WS_eff_full)
plt.plot(X_j.T, Y_j.T, '.-')
print(list(np.round(np.array(WS_eff[0]), 6)))
print(list(np.round(np.array(WS_eff[1]), 6)))
plt.title(blockage_model.__name__)
plt.show()
npt.assert_array_almost_equal(WS_eff[0], center_ref)
npt.assert_array_almost_equal(WS_eff[1], side_ref)
def test_All2AllIterativeDeflection():
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
deflectionModel=JimenezWakeDeflection())
wf_model([0], [0])
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)"
def test_own_deficit_is_zero():
for deficitModel in get_models(WakeDeficitModel):
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site,
windTurbines,
wake_deficitModel=deficitModel(),
turbulenceModel=STF2017TurbulenceModel())
sim_res = wf_model([0], [0])
npt.assert_array_equal(sim_res.WS_eff,
sim_res.WS.broadcast_like(sim_res.WS_eff))
def test_deficit_symmetry(wake_deficitModel, blockage_deficitModel):
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
wfm = All2AllIterative(site, windTurbines, wake_deficitModel=wake_deficitModel,
superpositionModel=LinearSum(),
blockage_deficitModel=blockage_deficitModel,
deflectionModel=None, turbulenceModel=None)
power = wfm([0, 0, 500, 500], [0, 500, 0, 500], wd=[0], ws=[8]).power_ilk[:, 0, 0]
npt.assert_array_almost_equal(power[:2], power[2:])
def test_NOJ_Nibe_result():
# Replicate result from: Jensen, Niels Otto. "A note on wind generator interaction." (1983).
site = UniformSite([1], 0.1)
x_i = [0, 0, 0]
y_i = [0, -40, -100]
h_i = [50, 50, 50]
wfm = All2AllIterative(site,
NibeA0,
wake_deficitModel=NOJDeficit(),
superpositionModel=LinearSum())
WS_eff_ilk = wfm.calc_wt_interaction(x_i, y_i, h_i, [0, 1, 1], 0.0, 8.1)[0]
npt.assert_array_almost_equal(WS_eff_ilk[:, 0, 0], [8.1, 4.35, 5.7])
def test_All2AllIterativeDeflection():
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site,
windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
deflectionModel=JimenezWakeDeflection())
sim_res = wf_model([0, 500], [0, 0], wd=270, ws=10, yaw=30)
if 0:
sim_res.flow_map(
XYGrid(x=np.linspace(-200, 1000, 100))).plot_wake_map()
plt.show()
def test_aep_two_turbines(setup, blockage_model, blockage_loss):
site, windTurbines = setup
nwm_ss = All2AllIterative(site, windTurbines, wake_deficitModel=NoWakeDeficit(),
blockage_deficitModel=blockage_model(), superpositionModel=LinearSum())
sim_res = nwm_ss(x=[0, 80 * 3], y=[0, 0])
aep_no_blockage = sim_res.aep_ilk(with_wake_loss=False).sum(2)
aep = sim_res.aep_ilk().sum(2)
# blockage reduce aep(wd=270) by .5%
npt.assert_almost_equal((aep_no_blockage[0, 270] - aep[0, 270]) / aep_no_blockage[0, 270] * 100, blockage_loss)
if debug:
plt.plot(sim_res.WS_eff_ilk[:, :, 7].T)
plt.title(blockage_model.__name__)
plt.show()
def test_stf(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(np.round(res.TI_eff_ilk[:, 0, 0], 3).tolist())
npt.assert_array_almost_equal(res.TI_eff_ilk[:, 0, 0], ref_ti, 3)
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_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 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_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_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_blockage_map(setup, blockage_model, center_ref, side_ref):
site, windTurbines = setup
wm = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=blockage_model())
flow_map = wm(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
X_j, Y_j = flow_map.XY
WS_eff = flow_map.WS_eff_xylk[:, :, 0, 0]
if debug:
plt.contourf(X_j, Y_j, WS_eff)
plt.plot(X_j[190, ::50], Y_j[190, ::50], '.-')
plt.plot(X_j[250, ::50], Y_j[250, ::50], '.-')
print(list(np.round(np.array(WS_eff[190, ::50]), 6)))
print(list(np.round(np.array(WS_eff[250, ::50]), 6)))
plt.title(blockage_model.__name__)
plt.show()
npt.assert_array_almost_equal(WS_eff[190, ::50], center_ref)
npt.assert_array_almost_equal(WS_eff[250, ::50], side_ref)
def test_superposition_model_indices(superpositionModel, sum_func):
class WTSite(UniformSite):
def local_wind(self,
x_i=None,
y_i=None,
h_i=None,
wd=None,
ws=None,
wd_bin_size=None,
ws_bins=None):
lw = UniformSite.local_wind(self,
x_i=x_i,
y_i=y_i,
h_i=h_i,
wd=wd,
ws=ws,
wd_bin_size=wd_bin_size,
ws_bins=ws_bins)
lw['WS'] = xr.DataArray(
lw.WS_ilk + np.arange(len(x_i))[:, np.newaxis, np.newaxis],
[('wt', [0, 1, 2]), ('wd', np.atleast_1d(wd)),
('ws', np.atleast_1d(ws))])
return lw
site = WTSite([1], 0.1)
x_i = [0, 0, 0]
y_i = [0, -40, -100]
h_i = [50, 50, 50]
# WS_ilk different at each wt position
WS_ilk = site.local_wind(x_i, y_i, h_i, wd=0, ws=8.1).WS_ilk
npt.assert_array_equal(WS_ilk, np.reshape([8.1, 9.1, 10.1], (3, 1, 1)))
for wake_model in [
PropagateDownwind(site,
NibeA0,
wake_deficitModel=NOJDeficit(),
superpositionModel=superpositionModel),
All2AllIterative(site,
NibeA0,
wake_deficitModel=NOJDeficit(),
superpositionModel=superpositionModel)
]:
# No wake (ct = 0), i.e. WS_eff == WS
WS_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [1, 1, 1],
0.0, 8.1)[0]
npt.assert_array_equal(WS_eff_ilk, WS_ilk)
ref = WS_ilk - np.reshape(
[0, 3.75, sum_func([2.4, 3.58974359])], (3, 1, 1))
# full wake (CT=8/9)
WS_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [0, 0, 0],
0.0, 8.1)[0]
npt.assert_array_almost_equal(WS_eff_ilk, ref)
sim_res = wake_model(x_i, y_i, h_i, [0, 0, 0], 0.0, 8.1)
WS_eff_ilk = sim_res.flow_map(HorizontalGrid(x=[0], y=y_i,
h=50)).WS_eff_xylk[:, 0]
npt.assert_array_almost_equal(WS_eff_ilk, ref)
def get_wfms(wt, site=Hornsrev1Site(), wake_model=NOJDeficit(), superpositionModel=SquaredSum()):
wfm1 = PropagateDownwind(site, wt, wake_model, superpositionModel=superpositionModel)
wfm2 = All2AllIterative(site, wt, wake_model, superpositionModel=superpositionModel)
wfm2.verbose = False
wfm1.verbose = False
return wfm1, wfm2