def test_local_wind(site):
x_i = y_i = np.arange(5)
wdir_lst = np.arange(0, 360, 90)
wsp_lst = np.arange(3, 6)
lw = site.local_wind(x_i=x_i, y_i=y_i, wd=wdir_lst, ws=wsp_lst)
npt.assert_array_equal(lw.WS_ilk.shape, (5, 4, 3))
lw = site.local_wind(x_i=x_i, y_i=y_i)
npt.assert_array_equal(lw.WS_ilk.shape, (5, 360, 23))
# check probability local_wind()[-1]
npt.assert_equal(site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], wd_bin_size=1).P_ilk,
site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], wd_bin_size=2).P_ilk / 2)
npt.assert_almost_equal(site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[9, 10, 11]).P_ilk.sum((1, 2)),
site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], ws_bins=3).P_ilk[:, 0, 0], 5)
z = np.arange(1, 100)
zero = [0] * len(z)
ws = site.local_wind(x_i=zero, y_i=zero, h_i=z, wd=[0], ws=[10]).WS_ilk[:, 0, 0]
site2 = UniformWeibullSite(f, A, k, ti, shear=PowerShear(70, alpha=np.zeros_like(f) + .3))
ws70 = site2.local_wind(x_i=zero, y_i=zero, h_i=z, wd=[0], ws=[10]).WS_ilk[:, 0, 0]
if 0:
import matplotlib.pyplot as plt
plt.plot(ws, z)
plt.plot(ws70, z)
plt.show()
npt.assert_array_equal(10 * (z / 50)**.3, ws)
npt.assert_array_equal(10 * (z / 70)**.3, ws70)
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_with_all_deficit_models(WFM, deficitModel):
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wfm = WFM(site,
windTurbines,
wake_deficitModel=deficitModel,
rotorAvgModel=RotorCenter(),
superpositionModel=LinearSum(),
deflectionModel=None,
turbulenceModel=STF2017TurbulenceModel())
wfm2 = WFM(site,
windTurbines,
wake_deficitModel=deficitModel,
rotorAvgModel=EqGridRotorAvg(1),
superpositionModel=LinearSum(),
deflectionModel=None,
turbulenceModel=STF2017TurbulenceModel())
kwargs = {
'x': [0, 0, 500, 500],
'y': [0, 500, 0, 500],
'wd': [0],
'ws': [8]
}
npt.assert_equal(wfm.aep(**kwargs), wfm2.aep(**kwargs))
def test_wd_independent_site():
ti = 0.1
ds = xr.Dataset(
data_vars={
'WS': 10, 'Sector_frequency': 1,
'Weibull_A': 4, 'Weibull_k': 2, 'TI': ti},
coords={})
site = XRSite(ds, shear=None)
npt.assert_equal(site.ds.sector_width, 360)
def test_scalar2scalar():
def f(x):
return x**2 + 1
x = np.array([3])
npt.assert_equal(cs(f)(x), 6)
npt.assert_almost_equal(fd(f)(x), 6, 5)
npt.assert_equal(autograd(f)(x), 6)
pf = primitive(f)
defvjp(pf, lambda ans, x: lambda g: g * 2 * x)
npt.assert_array_equal(autograd(pf, False)(x), 6)
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_read_iea_windturbine():
wt_id, hubheight, diameter, ct, power, dct, dpower = read_iea37_windturbine(
iea37_path + 'iea37-335mw.yaml')
assert wt_id == "3.35MW"
assert hubheight == 110
assert diameter == 130
u = np.arange(30)
p_r = 3350000
npt.assert_array_almost_equal([0, 1 / 5.8**3 * p_r, p_r, p_r, 0],
power([4, 5, 9.8, 25, 25.1]))
ct_ = 4 * 1 / 3 * (1 - 1 / 3)
npt.assert_array_almost_equal([0, ct_, ct_, 0], ct([3.9, 4, 25, 25.1]))
npt.assert_almost_equal(dpower(7), cs(power)(7))
npt.assert_equal(dct(7), 0)
if 0:
import matplotlib.pyplot as plt
plt.plot(u, power(u) / 1e6)
plt.plot(u, ct(u))
plt.show()
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_scalar2multi_scalar():
def fxy(x):
return x**2 + 1, 2 * x + 1
def f(x):
fx, fy = fxy(x)
return fx + fy
x = 3.
ref = 8
npt.assert_equal(cs(f)(x), ref)
npt.assert_almost_equal(fd(f)(x), ref, 5)
npt.assert_equal(autograd(f)(x), ref)
pf = primitive(f)
defvjp(pf, lambda ans, x: lambda g: g * (2 * x + 2))
npt.assert_array_equal(autograd(pf, False)(x), ref)
pf = primitive(fxy)
defvjp(pf, lambda ans, x: lambda g: (g[0] * 2 * x, g[1] * 2))
npt.assert_array_equal(autograd(f, False)(x), ref)
def test_local_wind(site):
x_i = y_i = np.arange(5)
wdir_lst = np.arange(0, 360, 90)
wsp_lst = np.arange(3, 6)
WD_ilk, WS_ilk, TI_ilk, P_lk = site.local_wind(x_i=x_i,
y_i=y_i,
wd=wdir_lst,
ws=wsp_lst)
npt.assert_array_equal(WS_ilk.shape, (5, 4, 3))
WD_ilk, WS_ilk, TI_ilk, P_lk = site.local_wind(x_i=x_i, y_i=y_i)
npt.assert_array_equal(WS_ilk.shape, (5, 360, 23))
# check probability local_wind()[-1]
npt.assert_equal(
site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10])[-1],
site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], wd_bin_size=2)[-1] *
180)
npt.assert_almost_equal(
site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[9, 10, 11])[-1].sum(),
site.local_wind(x_i=x_i, y_i=y_i, wd=[0], ws=[10], ws_bin_size=3)[-1],
5)
z = np.arange(1, 100)
zero = [0] * len(z)
ws = site.local_wind(x_i=zero, y_i=zero, h_i=z, wd=[0], ws=[10])[1][:, 0,
0]
site.h_ref = 70
ws70 = site.local_wind(x_i=zero, y_i=zero, h_i=z, wd=[0], ws=[10])[1][:, 0,
0]
if 0:
import matplotlib.pyplot as plt
plt.plot(ws, z)
plt.plot(ws70, z)
plt.show()
npt.assert_array_equal(10 * (z / 50)**.3, ws)
npt.assert_array_equal(10 * (z / 70)**.3, ws70)
def test_vector2multi_vector():
def fxy(x):
return x**2 + 1, 2 * x + 1
def f0(x):
return fxy(x)[0]
def fsum(x):
fx, fy = fxy(x)
return fx + fy
x = np.array([1., 2, 3])
ref0 = [2, 4, 6]
refsum = [4, 6, 8]
npt.assert_equal(cs(f0)(x), ref0)
npt.assert_almost_equal(fd(f0)(x), ref0, 5)
npt.assert_equal(autograd(f0)(x), ref0)
pf0 = primitive(f0)
defvjp(pf0, lambda ans, x: lambda g: g * (2 * x))
npt.assert_array_equal(autograd(pf0, False)(x), ref0)
npt.assert_equal(cs(fsum)(x), refsum)
npt.assert_almost_equal(fd(fsum)(x), refsum, 5)
npt.assert_equal(autograd(fsum)(x), refsum)
pfsum = primitive(fsum)
defvjp(pfsum, lambda ans, x: lambda g: g * (2 * x + 2))
npt.assert_array_equal(autograd(pfsum, False)(x), refsum)
pfxy = primitive(fxy)
def dfxy(x):
return 2 * x, np.full(x.shape, 2)
def gsum(x):
fx, fy = pfxy(x)
return fx + fy
def g0(x):
return pfxy(x)[0]
pgsum = primitive(gsum)
pg0 = primitive(g0)
defvjp(pgsum, lambda ans, x: lambda g: g * np.sum(dfxy(x), 0))
defvjp(pg0, lambda ans, x: lambda g: g * dfxy(x)[0])
npt.assert_array_equal(autograd(pgsum, False)(x), refsum)
npt.assert_array_equal(autograd(pg0, False)(x), ref0)
defvjp(pfxy, lambda ans, x: lambda g: dfxy(x)[0])
def h0(x):
return pfxy(x)[0]
npt.assert_array_equal(autograd(h0, False)(x), ref0)
defvjp(pfxy, lambda ans, x: lambda g: np.sum(g * np.asarray(dfxy(x)), 0))
def hsum(x):
fx, fy = pfxy(x)
return fx + fy
npt.assert_array_equal(autograd(hsum, False)(x), refsum)