def test_elevation(site):
x_i, y_i = site.initial_position.T
npt.assert_array_less(
np.abs(
site.elevation(x_i=x_i, y_i=y_i) -
[519.4, 567.7, 583.6, 600, 574.8, 559.9, 517.7, 474.5
] # ref from wasp
),
5)
def test_speed_up_using_pickle():
pkl_fn = ParqueFicticio_path + "ParqueFicticio.pkl"
if os.path.exists(pkl_fn):
os.remove(pkl_fn)
start = time.time()
site = WaspGridSiteBase.from_wasp_grd(ParqueFicticio_path, speedup_using_pickle=False)
time_wo_pkl = time.time() - start
site = WaspGridSiteBase.from_wasp_grd(ParqueFicticio_path, speedup_using_pickle=True)
assert os.path.exists(pkl_fn)
start = time.time()
site = WaspGridSiteBase.from_wasp_grd(ParqueFicticio_path, speedup_using_pickle=True)
time_w_pkl = time.time() - start
npt.assert_array_less(time_w_pkl * 10, time_wo_pkl)
def test_wasp_resources_grid_point(site):
# x = np.array([l.split() for l in """0.6010665 -10.02692 32.71442 -6.746912
# 0.5007213 -4.591617 37.10247 -11.0699
# 0.3104101 -1.821247 59.18301 -12.56743
# 0.4674515 16.14293 44.84665 -9.693183
# 0.8710347 5.291974 26.01634 -6.154611
# 0.9998786 -2.777032 15.72486 1.029988
# 0.9079611 -7.882853 16.33216 6.42329
# 0.759553 -5.082487 17.23354 10.18187
# 0.7221162 4.606324 17.96417 11.45838
# 0.8088576 8.196074 16.16308 9.277925
# 0.8800673 3.932325 14.82337 5.380589
# 0.8726974 -3.199536 19.99724 -1.433086""".split("\n")], dtype=np.float)
# for x_ in x.T:
# print(list(x_))
x = [262978]
y = [6504814]
npt.assert_almost_equal(site.elevation(x, y), 227.8, 1)
# Data from WAsP:
# - add turbine (262878,6504814,30)
# - Turbine (right click) - reports - Turbine Site Report full precision
wasp_A = [
2.197305, 1.664085, 1.353185, 2.651781, 5.28438, 5.038289, 4.174325,
4.604496, 5.043066, 6.108261, 6.082033, 3.659798
]
wasp_k = [
1.771484, 2.103516, 2.642578, 2.400391, 2.357422, 2.306641, 2.232422,
2.357422, 2.400391, 2.177734, 1.845703, 1.513672
]
wasp_f = [
5.188083, 2.509297, 2.869334, 4.966141, 13.16969, 9.514355, 4.80275,
6.038354, 9.828702, 14.44174, 16.60567, 10.0659
]
wasp_spd = [
0.6010665, 0.5007213, 0.3104101, 0.4674515, 0.8710347, 0.9998786,
0.9079611, 0.759553, 0.7221162, 0.8088576, 0.8800673, 0.8726974
]
wasp_trn = [
-10.02692, -4.591617, -1.821247, 16.14293, 5.291974, -2.777032,
-7.882853, -5.082487, 4.606324, 8.196074, 3.932325, -3.199536
]
wasp_inc = [
-6.746912, -11.0699, -12.56743, -9.693183, -6.154611, 1.029988,
6.42329, 10.18187, 11.45838, 9.277925, 5.380589, -1.433086
]
wasp_ti = [
32.71442, 37.10247, 59.18301, 44.84665, 26.01634, 15.72486, 16.33216,
17.23354, 17.96417, 16.16308, 14.82337, 19.99724
]
rho = 1.179558
wasp_u_mean = [
1.955629, 1.473854, 1.202513, 2.350761, 4.683075, 4.463644, 3.697135,
4.080554, 4.470596, 5.409509, 5.402648, 3.300305
]
wasp_p_air = [
9.615095, 3.434769, 1.556282, 12.45899, 99.90289, 88.03519, 51.41135,
66.09097, 85.69466, 164.5592, 193.3779, 56.86945
]
# wasp_aep = np.array([3725293.0, 33722.71, 0.3093564, 3577990.0, 302099600.0, 188784100.0,
# 48915640.0, 84636210.0, 189009800.0, 549195100.0, 691258600.0, 120013000.0]) / 1000
wasp_aep_no_density_correction = np.array([
3937022.0, 36046.93, 0.33592, 3796496.0, 314595600.0, 196765700.0,
51195440.0, 88451200.0, 197132700.0, 568584400.0, 712938400.0,
124804600.0
]) / 1000
# wasp_aep_total = 2.181249024
wasp_aep_no_density_correction_total = 2.26224
wt_u = np.array([
3.99, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25
])
wt_p = np.array([
0, 55., 185., 369., 619., 941., 1326., 1741., 2133., 2436., 2617.,
2702., 2734., 2744., 2747., 2748., 2748., 2750., 2750., 2750., 2750.,
2750., 2750.
])
wt_ct = np.array([
0, 0.871, 0.853, 0.841, 0.841, 0.833, 0.797, 0.743, 0.635, 0.543,
0.424, 0.324, 0.258, 0.21, 0.175, 0.147, 0.126, 0.109, 0.095, 0.083,
0.074, 0.065, 0.059
])
wt = OneTypeWindTurbines.from_tabular(name="NEG-Micon 2750/92 (2750 kW)",
diameter=92,
hub_height=70,
ws=wt_u,
ct=wt_ct,
power=wt_p,
power_unit='kw')
A_lst, k_lst, f_lst, spd_lst, orog_trn_lst, flow_inc_lst, tke_lst = [
site.interp_funcs[n]((x, y, 30, range(0, 360, 30)))
for n in ['A', 'k', 'f', 'spd', 'orog_trn', 'flow_inc', 'tke']
]
f_lst = f_lst * 360 / 12
pdf_lst = [
lambda x, A=A, k=k: k / A * (x / A)**(k - 1) * np.exp(-(x / A)**k) *
(x[1] - x[0]) for A, k in zip(A_lst, k_lst)
]
# cdf_lst = [lambda x, A=A, k=k: 1 - np.exp(-(x / A) ** k) for A, k in zip(A_lst, k_lst)]
dx = .1
ws = np.arange(dx / 2, 35, dx)
# compare to wasp data
npt.assert_array_equal(A_lst, wasp_A)
npt.assert_array_equal(k_lst, wasp_k)
npt.assert_array_almost_equal(f_lst, np.array(wasp_f) / 100)
npt.assert_array_almost_equal(spd_lst, wasp_spd)
npt.assert_array_almost_equal(orog_trn_lst, wasp_trn)
npt.assert_array_almost_equal(flow_inc_lst, wasp_inc)
npt.assert_array_almost_equal(tke_lst, np.array(wasp_ti) / 100)
# compare pdf, u_mean and aep to wasp
lw = site.local_wind(x,
np.array(y) + 1e-6,
30,
wd=np.arange(0, 360, 30),
ws=ws)
P = lw.P_ilk / lw.P_ilk.sum(2)[:, :,
na] # only wind speed probablity (not wdir)
# pdf
for l in range(12):
npt.assert_array_almost_equal(
np.interp(ws, lw.WS_ilk[0, l], np.cumsum(P[0, l])),
np.cumsum(pdf_lst[l](ws)), 1)
# u_mean
npt.assert_almost_equal(
[A * math.gamma(1 + 1 / k) for A, k in zip(A_lst, k_lst)], wasp_u_mean,
5)
npt.assert_almost_equal([(pdf(ws) * ws).sum() for pdf in pdf_lst],
wasp_u_mean, 5)
npt.assert_almost_equal((P * lw.WS_ilk).sum((0, 2)), wasp_u_mean, 5)
# air power
p_air = [(pdf(ws) * 1 / 2 * rho * ws**3).sum() for pdf in pdf_lst]
npt.assert_array_almost_equal(p_air, wasp_p_air, 3)
npt.assert_array_almost_equal((P * 1 / 2 * rho * lw.WS_ilk**3).sum((0, 2)),
wasp_p_air, 2)
# AEP
AEP_ilk = NOJ(site, wt)(x, y, h=30, wd=np.arange(0, 360, 30),
ws=ws).aep_ilk(with_wake_loss=False,
normalize_probabilities=True)
if 0:
plt.plot(wasp_aep_no_density_correction / 1000, '.-', label='WAsP')
plt.plot(AEP_ilk.sum((0, 2)) * 1e3, label='PyWake')
plt.xlabel('Sector')
plt.ylabel('AEP [MWh]')
plt.legend()
plt.show()
npt.assert_array_less(
np.abs(wasp_aep_no_density_correction - AEP_ilk.sum((0, 2)) * 1e6),
300)
npt.assert_almost_equal(AEP_ilk.sum(),
wasp_aep_no_density_correction_total, 3)
