def __init__(self, site, windTurbines, k=.1, superpositionModel=SquaredSum(),
deflectionModel=None, turbulenceModel=None):
"""
Parameters
----------
site : Site
Site object
windTurbines : WindTurbines
WindTurbines object representing the wake generating wind turbines
k : float, default 0.1
wake expansion factor
superpositionModel : SuperpositionModel, default SquaredSum
Model defining how deficits sum up
blockage_deficitModel : DeficitModel, default None
Model describing the blockage(upstream) deficit
deflectionModel : DeflectionModel, default None
Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.
turbulenceModel : TurbulenceModel, default None
Model describing the amount of added turbulence in the wake
"""
PropagateDownwind.__init__(self, site, windTurbines,
wake_deficitModel=NOJDeficit(k),
superpositionModel=superpositionModel,
deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel)
def __init__(self,
site,
windTurbines,
a=[0.38, 4e-3],
superpositionModel=SquaredSum(),
deflectionModel=None,
turbulenceModel=None,
groundModel=None):
"""
Parameters
----------
site : Site
Site object
windTurbines : WindTurbines
WindTurbines object representing the wake generating wind turbines
superpositionModel : SuperpositionModel, default SquaredSum
Model defining how deficits sum up
deflectionModel : DeflectionModel, default None
Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.
turbulenceModel : TurbulenceModel, default None
Model describing the amount of added turbulence in the wake
"""
PropagateDownwind.__init__(self,
site,
windTurbines,
wake_deficitModel=ZongGaussianDeficit(a=a),
superpositionModel=superpositionModel,
deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel,
groundModel=groundModel)
def __init__(self,
LUT_path,
site,
windTurbines,
rotorAvgModel=RotorCenter(),
deflectionModel=None,
turbulenceModel=None):
"""
Parameters
----------
LUT_path : str
path to look up tables
site : Site
Site object
windTurbines : WindTurbines
WindTurbines object representing the wake generating wind turbines
rotorAvgModel : RotorAvgModel
Model defining one or more points at the down stream rotors to
calculate the rotor average wind speeds from.\n
Defaults to RotorCenter that uses the rotor center wind speed (i.e. one point) only
deflectionModel : DeflectionModel
Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.
turbulenceModel : TurbulenceModel
Model describing the amount of added turbulence in the wake
"""
PropagateDownwind.__init__(self,
site,
windTurbines,
wake_deficitModel=FugaDeficit(LUT_path),
rotorAvgModel=rotorAvgModel,
superpositionModel=LinearSum(),
deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel)
def __init__(self,
site,
windTurbines,
rotorAvgModel=RotorCenter(),
superpositionModel=SquaredSum(),
deflectionModel=None,
turbulenceModel=None):
"""
Parameters
----------
site : Site
Site object
windTurbines : WindTurbines
WindTurbines object representing the wake generating wind turbines
rotorAvgModel : RotorAvgModel
Model defining one or more points at the down stream rotors to
calculate the rotor average wind speeds from.\n
Defaults to RotorCenter that uses the rotor center wind speed (i.e. one point) only
superpositionModel : SuperpositionModel, default SquaredSum
Model defining how deficits sum up
deflectionModel : DeflectionModel, default None
Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.
turbulenceModel : TurbulenceModel, default None
Model describing the amount of added turbulence in the wake
"""
PropagateDownwind.__init__(
self,
site,
windTurbines,
wake_deficitModel=IEA37SimpleBastankhahGaussianDeficit(),
rotorAvgModel=rotorAvgModel,
superpositionModel=superpositionModel,
deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel)
def __init__(self, site, windTurbines, rotorAvgModel=RotorCenter(), superpositionModel=LinearSum(),
deflectionModel=None, turbulenceModel=None):
PropagateDownwind.__init__(self, site, windTurbines,
wake_deficitModel=GCLDeficit(use_effective_ws=True, use_effective_ti=True),
rotorAvgModel=rotorAvgModel, superpositionModel=superpositionModel,
deflectionModel=deflectionModel, turbulenceModel=turbulenceModel)
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_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_fuga_deflection_time_series_gradient_evaluation():
p = Path(tfp) / "fuga/v80_wake_center_x.csv"
x, notebook_wake_center = np.array([v.split(",") for v in p.read_text().strip().split("\n")], dtype=float).T
powerCtFunction = PowerCtTabular([0, 100], [0, 0], 'w', [0.850877, 0.850877])
wt = WindTurbine(name='', diameter=80, hub_height=70, powerCtFunction=powerCtFunction)
path = tfp + 'fuga/2MW/Z0=0.00001000Zi=00400Zeta0=0.00E+00'
site = UniformSite([1, 0, 0, 0], ti=0.075)
wfm = PropagateDownwind(
site,
wt,
wake_deficitModel=FugaYawDeficit(path),
deflectionModel=FugaDeflection(path, 'input_par')
)
WS = 10
yaw_ref = np.full((10, 1), 17)
yaw_step = np.eye(10, 10) * 1e-6 + yaw_ref
yaw = np.concatenate([yaw_step, yaw_ref], axis=1)
sim_res = wfm(np.arange(10) * wt.diameter() * 4, [0] * 10, yaw=yaw, wd=[270] * 11, ws=[WS] * 11, time=True)
print(sim_res)
def test_fuga_wake_center_vs_notebook():
p = Path(tfp) / "fuga/v80_wake_center_x.csv"
x, notebook_wake_center = np.array([v.split(",") for v in p.read_text().strip().split("\n")], dtype=float).T
powerCtFunction = PowerCtTabular([0, 100], [0, 0], 'w', [0.850877, 0.850877])
wt = WindTurbine(name='', diameter=80, hub_height=70, powerCtFunction=powerCtFunction)
path = tfp + 'fuga/2MW/Z0=0.00001000Zi=00400Zeta0=0.00E+00'
site = UniformSite([1, 0, 0, 0], ti=0.075)
wfm = PropagateDownwind(
site,
wt,
wake_deficitModel=FugaYawDeficit(path),
deflectionModel=FugaDeflection(path, 'input_par')
)
WS = 10
sim_res = wfm([0], [0], yaw=[17.4493], wd=270, ws=[WS])
y = wfm.wake_deficitModel.mirror(wfm.wake_deficitModel.y, anti_symmetric=True)
fm = sim_res.flow_map(XYGrid(x=x[1:], y=y[240:271]))
fuga_wake_center = [np.interp(0, InterpolatedUnivariateSpline(ws.y, ws.values).derivative()(ws.y), ws.y)
for ws in fm.WS_eff.squeeze().T]
if 0:
plt.plot(x, notebook_wake_center, label='Notebook deflection')
plt.plot(x[1:], fuga_wake_center)
plt.show()
plt.close('all')
npt.assert_allclose(fuga_wake_center, notebook_wake_center[1:], atol=.14)
def test_interpolation():
wts = HornsrevV80()
path = tfp + 'fuga/2MW/Z0=0.00408599Zi=00400Zeta0=0.00E+00/'
site = UniformSite([1, 0, 0, 0], ti=0.075)
plot = 0
if plot:
ax1 = plt.gca()
ax2 = plt.twinx()
for wdm, n_d_values in (
(FugaDeficit(path, method='linear'), 4),
(FugaDeficit(path, method='spline'), 20),
(FugaYawDeficit(path, method='linear'), 4),
(FugaYawDeficit(path, method='spline'), 20),
):
wfm = PropagateDownwind(site, wts, wdm)
sim_res = wfm(x=[0], y=[0], wd=[270], ws=[10], yaw=[[[10]]])
fm = sim_res.flow_map(XYGrid(x=[200], y=np.arange(-10, 11)))
fm = sim_res.flow_map(
XYGrid(x=np.arange(-100, 800, 10), y=np.arange(-10, 11)))
# linear has 4 line segments with same gradient, while spline has 20 different gradient values
npt.assert_equal(
len(np.unique(np.round(np.diff(fm.WS_eff.sel(x=500).squeeze()),
6))), n_d_values)
if plot:
ax1.plot(fm.y, fm.WS_eff.sel(x=500).squeeze())
ax2.plot(fm.y[:-1], np.diff(fm.WS_eff.sel(x=500).squeeze()), '--')
if plot:
plt.show()
plt.close('all')
def test_fuga_wriggles():
wts = HornsrevV80()
path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+00/'
site = hornsrev1.Hornsrev1Site()
fuga = PropagateDownwind(site, wts, FugaDeficit(path,
remove_wriggles=True))
D = 80
flow_map_cw = fuga([0], [0], wd=270, ws=10).flow_map(
HorizontalGrid([0], np.arange(-20 * D, 20 * D)))
y = np.linspace(-5 * D, 5 * D, 100)
dw_lst = range(10)
flow_map_cw_lst = np.array([
fuga([0], [0], wd=270,
ws=10).flow_map(HorizontalGrid([dw * D], y)).WS_eff.squeeze()
for dw in dw_lst
])
if 0:
for flow_map_cw, dw in zip(flow_map_cw_lst, dw_lst):
plt.plot(y, flow_map_cw, label="%dD" % dw)
plt.xlabel('y [m]')
plt.ylabel('ws [m/s')
plt.ylim([9.9, 10.1])
plt.grid()
plt.legend(loc=1)
plt.show()
assert np.all(flow_map_cw_lst > 0)
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_fuga_downwind():
wts = HornsrevV80()
path = tfp + 'fuga/2MW/Z0=0.00408599Zi=00400Zeta0=0.00E+00'
site = UniformSite([1, 0, 0, 0], ti=0.075)
wfm_UL = Fuga(path, site, wts)
wfm_ULT = PropagateDownwind(site, wts, FugaYawDeficit(path))
(ax1, ax2), (ax3, ax4) = plt.subplots(2, 2)[1]
def plot(wfm, yaw, ax, min_ws):
levels = np.arange(6.5, 10.5, .5)
sim_res = wfm([0], [0], wd=270, ws=10, yaw=[[[yaw]]])
fm = sim_res.flow_map(XYGrid(x=np.arange(-100, 500, 5)))
npt.assert_almost_equal(fm.WS_eff.min(), min_ws)
fm.plot_wake_map(ax=ax, levels=levels)
fm.min_WS_eff(fm.x, 70).plot(ax=ax, color='r')
plt.axhline(0, color='k')
plot(wfm_UL, 0, ax1, 7.15853738)
plot(wfm_UL, 30, ax2, 7.83219266)
plot(wfm_ULT, 0, ax3, 7.15853738)
plot(wfm_ULT, 30, ax4, 8.12261872)
if 0:
plt.show()
plt.close('all')
def test_fuga_downwind_vs_notebook():
powerCtFunction = PowerCtTabular([0, 100], [0, 0], 'w',
[0.850877, 0.850877])
wt = WindTurbine(name='',
diameter=80,
hub_height=70,
powerCtFunction=powerCtFunction)
path = tfp + 'fuga/2MW/Z0=0.00001000Zi=00400Zeta0=0.00E+00'
site = UniformSite([1, 0, 0, 0], ti=0.075)
wfm_ULT = PropagateDownwind(site, wt, FugaYawDeficit(path))
WS = 10
p = Path(tfp) / "fuga/v80_wake_4d_y_no_deflection.csv"
y, notebook_deficit_4d = np.array(
[v.split(",") for v in p.read_text().strip().split("\n")],
dtype=float).T
sim_res = wfm_ULT([0], [0], wd=270, ws=WS, yaw=[[[17.4493]]])
fm = sim_res.flow_map(XYGrid(4 * wt.diameter(), y=y))
npt.assert_allclose(fm.WS_eff.squeeze() - WS,
notebook_deficit_4d,
atol=1e-6)
if 0:
plt.plot(y, notebook_deficit_4d, label='Notebook deficit 4d')
plt.plot(y, fm.WS_eff.squeeze() - WS)
plt.show()
plt.close('all')
def test_GCL_ex80():
site = Hornsrev1Site()
x, y = site.initial_position.T
windTurbines = V80()
wfm = PropagateDownwind(site,
windTurbines,
wake_deficitModel=GCLDeficit(),
superpositionModel=LinearSum())
if 0:
windTurbines.plot(x, y)
plt.show()
sim_res = timeit(wfm.__call__,
line_profile=0,
profile_funcs=[get_dU],
verbose=0)(x, y, ws=np.arange(10, 15))[0]
# test that the result is equal to previuos runs (no evidens that these number are correct)
aep_ref = 1055.956615887197
npt.assert_almost_equal(
sim_res.aep_ilk(normalize_probabilities=True).sum(), aep_ref, 5)
sim_res = wfm(x, y, ws=np.arange(3, 10))
npt.assert_array_almost_equal(
sim_res.aep_ilk(normalize_probabilities=True).sum(), 261.6143039016946,
5)
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=NoWakeDeficit(),
turbulenceModel=GCLTurbulence())
with pytest.raises(NotImplementedError, match="wake_radius not implemented for NoWakeDeficit"):
wfm(x, y)
def __init__(self, LUT_path, site, windTurbines, deflectionModel=None, turbulenceModel=None):
"""
Parameters
----------
LUT_path : str
path to look up tables
site : Site
Site object
windTurbines : WindTurbines
WindTurbines object representing the wake generating wind turbines
deflectionModel : DeflectionModel
Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.
turbulenceModel : TurbulenceModel
Model describing the amount of added turbulence in the wake
"""
PropagateDownwind.__init__(self, site, windTurbines,
wake_deficitModel=FugaDeficit(LUT_path), superpositionModel=LinearSum(),
deflectionModel=deflectionModel, turbulenceModel=turbulenceModel)
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)