import pytest
import matplotlib.pyplot as plt
from py_wake import np
from py_wake.deficit_models.deficit_model import WakeDeficitModel, BlockageDeficitModel
from py_wake.deficit_models.fuga import FugaDeficit, Fuga
from py_wake.deficit_models.gaussian import BastankhahGaussianDeficit, IEA37SimpleBastankhahGaussianDeficit,\
ZongGaussianDeficit, NiayifarGaussianDeficit, BastankhahGaussian, ZongGaussian,\
NiayifarGaussian, CarbajofuertesGaussianDeficit, TurboGaussianDeficit, IEA37SimpleBastankhahGaussian
from py_wake.deficit_models.gcl import GCLDeficit, GCL, GCLLocal
from py_wake.deficit_models.noj import NOJDeficit, NOJ, NOJLocalDeficit, NOJLocal, TurboNOJDeficit
from py_wake.deficit_models import VortexDipole
from py_wake.examples.data.hornsrev1 import Hornsrev1Site
from py_wake.examples.data.iea37 import iea37_path
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines, IEA37Site
from py_wake.examples.data.iea37.iea37_reader import read_iea37_windfarm
from py_wake.flow_map import HorizontalGrid, XYGrid, YZGrid
from py_wake.superposition_models import SquaredSum, WeightedSum
from py_wake.tests import npt
from py_wake.tests.test_files import tfp
from py_wake.turbulence_models.gcl_turb import GCLTurbulence
from py_wake.turbulence_models.stf import STF2017TurbulenceModel
from py_wake.wind_farm_models.engineering_models import PropagateDownwind, All2AllIterative
from py_wake.utils.model_utils import get_models
from numpy import newaxis as na
from py_wake.deficit_models.no_wake import NoWakeDeficit
from py_wake.rotor_avg_models.rotor_avg_model import CGIRotorAvg
import warnings
from py_wake.deficit_models.utils import ct2a_mom1d
from py_wake.wind_turbines._wind_turbines import WindTurbine
from py_wake.site._site import UniformSite
class GCLLocalDeficit(GCLDeficit):
def __init__(self):
GCLDeficit.__init__(self, use_effective_ws=True, use_effective_ti=True)
@pytest.mark.parametrize(
'deficitModel,aep_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[
(NOJDeficit(ct2a=ct2a_mom1d), (367205.0846866496, [9833.86287, 8416.99088, 10820.37673, 13976.26422, 22169.66036,
25234.9215, 37311.64388, 42786.37028, 24781.33444, 13539.82115,
14285.22744, 31751.29488, 75140.15677, 17597.10319, 11721.21226,
7838.84383])),
(NOJLocalDeficit(ct2a=ct2a_mom1d), (335151.6404628441, [8355.71335, 7605.92379, 10654.172, 13047.6971, 19181.46408,
23558.34198, 36738.52415, 38663.44595, 21056.39764, 12042.79324,
13813.46269, 30999.42279, 63947.61202, 17180.40299, 11334.12323,
6972.14345])),
(TurboNOJDeficit(ct2a=ct2a_mom1d), (354154.2962989713, [9320.85263, 8138.29496, 10753.75662, 13398.00865, 21189.29438,
24190.84895, 37081.91938, 41369.66605, 23488.54863, 12938.48451,
14065.00719, 30469.75602, 71831.78532, 16886.85274, 11540.51872,
7490.70156])),
(BastankhahGaussianDeficit(ct2a=ct2a_mom1d), (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])),
(IEA37SimpleBastankhahGaussianDeficit(), read_iea37_windfarm(iea37_path + 'iea37-ex16.yaml')[2]),
(FugaDeficit(LUT_path=tfp + 'fuga/2MW/Z0=0.00408599Zi=00400Zeta0=0.00E+00.nc'),
(404422.9302289747, [9911.82745, 9761.75297, 12509.30601, 15396.26107, 23016.74091,
27798.80471, 43135.53798, 49622.24427, 24977.80518, 15459.8399,
16721.53971, 35692.36221, 77966.92736, 19781.30917, 13720.23771,
8950.43363])),
(GCLDeficit(), (370863.6246093183,
[9385.75387, 8768.52105, 11450.13309, 14262.42186, 21178.74926,
25751.59502, 39483.21753, 44573.31533, 23652.09976, 13924.58752,
15106.11692, 32840.02909, 71830.22035, 18200.49805, 12394.7626,
8061.6033])),
(GCLLocalDeficit(), (381187.36105425097,
[9678.85358, 9003.65526, 11775.06899, 14632.42259, 21915.85495,
26419.65189, 40603.68618, 45768.58091, 24390.71103, 14567.43106,
15197.82861, 32985.67922, 75062.92788, 18281.21981, 12470.01322,
8433.77587])),
(ZongGaussianDeficit(eps_coeff=0.35, ct2a=ct2a_mom1d),
(354263.1062694012, [8960.58672, 8095.48341, 11328.51572, 13924.50408, 19938.78428,
25141.4657, 39063.84731, 41152.04065, 22580.67854, 12944.55424,
14778.95385, 32294.41749, 66540.62665, 17898.1109, 12126.32111,
7494.21561])),
(NiayifarGaussianDeficit(ct2a=ct2a_mom1d),
(362228.4003016239, [9210.01349, 8330.06642, 11392.04851, 14082.13366, 20643.45247,
25426.07466, 39282.9259, 42344.50431, 23209.23399, 13390.08421,
14807.66824, 32535.89096, 69640.3283, 18031.93957, 12149.88163,
7752.15401])),
(CarbajofuertesGaussianDeficit(ct2a=ct2a_mom1d),
(362232.9643785641, [9140.59, 8399.80837, 11382.50882, 14053.32754, 20625.5118,
25374.06361, 39250.03042, 42699.02591, 23034.28679, 13479.45387,
14791.05027, 32446.01148, 69635.02796, 17982.12684, 12136.24637,
7803.89434])),
(TurboGaussianDeficit(ct2a=ct2a_mom1d, rotorAvgModel=None),
(333612.8032572539, [7696.10577, 7913.67274, 11102.28219, 13503.62483, 17184.49031,
24381.54484, 38283.7317, 40227.83645, 19394.18655, 12536.27008,
14613.38763, 31681.28939, 58287.76333, 17558.30497, 11990.4719,
7257.84057])),
])
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=.12)
npt.assert_allclose(aep_ref[1], [9500, 8700, 11500, 14300, 21300, 25900, 39600, 44300, 23900,
13900, 15200, 33000, 72100, 18300, 12500, 8000], rtol=.2)
npt.assert_almost_equal(aep_MW_l.sum(), aep_ref[0], 5)
npt.assert_array_almost_equal(aep_MW_l, aep_ref[1], 5)
@pytest.mark.parametrize('deficitModel', get_models(WakeDeficitModel))
def test_huge_distance(deficitModel):
ref = {"NOJDeficit": 9.799733,
"FugaDeficit": 9.8,
"FugaYawDeficit": 9.8,
"FugaMultiLUTDeficit": 9.8,
"BastankhahGaussianDeficit": 9.79916,
"CarbajofuertesGaussianDeficit": 9.798729,
"IEA37SimpleBastankhahGaussianDeficit": 9.799151,
"NiayifarGaussianDeficit": 9.799162,
"TurboGaussianDeficit": 9.67046,
"ZongGaussianDeficit": 9.799159,
"GCLDeficit": 9.728704,
"NoWakeDeficit": 9.8,
"NOJLocalDeficit": 9.797536,
"TurboNOJDeficit": 9.795322, }
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wfm = All2AllIterative(site, windTurbines, wake_deficitModel=deficitModel(), turbulenceModel=GCLTurbulence())
sim_res = wfm([0, 100000], [0, 0], wd=[0, 90, 180, 270], yaw=0)
# print(f'"{deficitModel.__name__}": {np.round(sim_res.WS_eff.sel(wt=1, ws=9.8, wd=270).item(),6)},')
npt.assert_array_almost_equal([9.8, 9.8, 9.8, ref[deficitModel.__name__]], sim_res.WS_eff.sel(wt=1).squeeze())
@pytest.mark.parametrize('deficitModel', get_models(BlockageDeficitModel))
def test_huge_distance_blockage(deficitModel):
if deficitModel is None:
return
ref = {"FugaDeficit": 9.8,
"FugaYawDeficit": 9.8,
"FugaMultiLUTDeficit": 9.8,
"HybridInduction": 9.799999,
"SelfSimilarityDeficit2020": 9.799999,
"VortexDipole": 9.799999,
"RankineHalfBody": 9.799999,
"Rathmann": 9.799999,
"RathmannScaled": 9.799999,
"SelfSimilarityDeficit": 9.799999,
"VortexCylinder": 9.799999, }
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wfm = All2AllIterative(site, windTurbines, wake_deficitModel=NoWakeDeficit(),
blockage_deficitModel=deficitModel(),
turbulenceModel=GCLTurbulence())
sim_res = wfm([0, 100000], [0, 0], wd=[0, 90, 180, 270], yaw=0)
# print(f'"{deficitModel.__name__}": {np.round(sim_res.WS_eff.sel(wt=0, ws=9.8, wd=270).item(),6)},')
npt.assert_array_almost_equal([9.8, 9.8, 9.8, ref[deficitModel.__name__]], sim_res.WS_eff.sel(wt=1).squeeze())
@pytest.mark.parametrize(
'deficitModel,ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(NOJDeficit(ct2a=ct2a_mom1d),
[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]),
(NOJLocalDeficit(ct2a=ct2a_mom1d),
[3.09, 3.09, 9., 9., 5.54, 5.54, 5.54, 5.54, 5.54, 5.54, 6.73, 6.73, 6.73, 6.73, 6.73, 6.73, 6.73]),
(TurboNOJDeficit(ct2a=ct2a_mom1d),
[3.51, 3.51, 3.51, 7.45, 7.45, 7.45, 7.45, 7.45, 7.13, 7.13, 7.13, 7.96, 7.96, 7.96, 7.96, 7.96, 7.96]),
(BastankhahGaussianDeficit(ct2a=ct2a_mom1d),
[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]),
(IEA37SimpleBastankhahGaussianDeficit(),
[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]),
(FugaDeficit(LUT_path=tfp + 'fuga/2MW/Z0=0.00408599Zi=00400Zeta0=0.00E+00.nc'),
[7.06, 7.87, 8.77, 8.85, 8.52, 7.96, 7.49, 7.55, 8.06, 8.58, 8.69, 8.45, 8.18, 8.05, 8.15, 8.41, 8.68]),
(GCLDeficit(),
[2.39, 5.01, 7.74, 8.34, 7.95, 7.58, 7.29, 7.32, 7.61, 7.92, 8.11, 8.09, 7.95, 7.83, 7.92, 8.1, 8.3]),
(GCLLocalDeficit(),
[3.05, 5.24, 7.61, 8.36, 8.08, 7.81, 7.61, 7.63, 7.82, 8.01, 8.11, 8.07, 7.94, 7.83, 7.92, 8.1, 8.3]),
(ZongGaussianDeficit(ct2a=ct2a_mom1d, eps_coeff=0.35),
[6.34, 7.08, 8.09, 8.36, 7.5, 6.2, 5.23, 5.34, 6.43, 7.64, 8.08, 7.75, 7.36, 7.19, 7.32, 7.69, 8.14]),
(NiayifarGaussianDeficit(ct2a=ct2a_mom1d),
[0.18, 3.6, 7.27, 8.32, 7.61, 6.64, 5.97, 6.04, 6.8, 7.69, 8.08, 7.87, 7.59, 7.46, 7.56, 7.84, 8.19]),
(CarbajofuertesGaussianDeficit(ct2a=ct2a_mom1d),
[6.49, 7.16, 8.08, 8.3, 7.68, 6.9, 6.37, 6.42, 7.02, 7.74, 8.06, 7.86, 7.57, 7.44, 7.54, 7.83, 8.19]),
(TurboGaussianDeficit(ct2a=ct2a_mom1d),
[3.27, 4.83, 7., 8.14, 7.54, 6.37, 5.52, 5.61, 6.57, 7.69, 8.02, 7.31, 6.43, 6., 6.32, 7.18, 8.05]),
])
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[3:], mean_ref[3:], atol=2.6)
@pytest.mark.parametrize(
'deficitModel,wake_radius_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[
(NOJDeficit(), [100., 75., 150., 100., 100.]),
(NOJLocalDeficit(), [71., 46., 92., 71., 61.5]),
(TurboNOJDeficit(), [99.024477, 61.553917, 123.107833, 92.439673, 97.034049]),
(BastankhahGaussianDeficit(), [83.336286, 57.895893, 115.791786, 75.266662, 83.336286]),
(IEA37SimpleBastankhahGaussianDeficit(), [103.166178, 67.810839, 135.621678, 103.166178, 103.166178]),
(FugaDeficit(LUT_path=tfp + 'fuga/2MW/Z0=0.00408599Zi=00400Zeta0=0.00E+00.nc'), [100, 50, 100, 100, 100]),
(GCLDeficit(), [156.949964, 97.763333, 195.526667, 113.225695, 111.340236]),
(GCLLocalDeficit(), [156.949964, 97.763333, 195.526667, 113.225695, 111.340236]),
(ZongGaussianDeficit(eps_coeff=0.35), [91.15734, 66.228381, 132.456762, 94.90156, 79.198215]),
(NiayifarGaussianDeficit(), [92.880786, 67.440393, 134.880786, 84.811162, 73.880786]),
(CarbajofuertesGaussianDeficit(), [102.914211, 68.866465, 137.866624, 102.914211, 85.457105]),
(TurboGaussianDeficit(), [76.674176, 41.548202, 83.096405, 64.831198, 76.143396]),
])
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)),
WS_ilk=np.reshape([10, 10, 10, 10, 10], (5, 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:
ax1, ax2 = plt.subplots(2, 1)[1]
sim_res = wfm([0], [0], wd=[270], ws=10)
fm = sim_res.flow_map(HorizontalGrid(x=np.arange(-100, 1500, 10)))
fm.WS_eff.plot(ax=ax1)
x = np.arange(0, 1500, 10)
wr = deficitModel.wake_radius(
WS_ilk=np.reshape([10], (1, 1, 1)),
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]
ax1.set_title(deficitModel.__class__.__name__)
ax1.plot(x, wr)
ax1.plot(x, -wr)
ax1.axvline(500, linestyle='--', color='k')
ax1.axis('equal')
fm.WS_eff.sel(x=500).plot(ax=ax2)
ax2.axvline(-wr[x == 500], color='k')
ax2.axvline(wr[x == 500], color='k')
ax2.grid()
plt.show()
def test_wake_radius_not_implemented():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
class MyDeficitModel(WakeDeficitModel):
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)
@pytest.mark.parametrize(
'deficitModel,aep_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(BastankhahGaussianDeficit(ct2a=ct2a_mom1d), (345846.3355259293,
[8835.30563, 7877.90062, 11079.66832, 13565.65235, 18902.99769,
24493.53897, 38205.75284, 40045.9948, 22264.97018, 12662.90784,
14650.96535, 31289.90349, 65276.92307, 17341.39229, 12021.3049,
7331.15717])),
(ZongGaussianDeficit(ct2a=ct2a_mom1d, eps_coeff=0.35),
(342944.4057168523, [8674.44232, 7806.22033, 11114.78804, 13549.48197, 18895.50866,
24464.34244, 38326.85532, 39681.61999, 21859.59465, 12590.25899,
14530.24656, 31158.81189, 63812.14454, 17268.73912, 11922.25359,
7289.09731])),
(NiayifarGaussianDeficit(ct2a=ct2a_mom1d),
(349044.6891842835, [8888.36909, 8025.38191, 11134.3202, 13643.08009, 19503.25093,
24633.33906, 38394.20758, 40795.69138, 22398.69011, 12972.04355,
14504.45911, 31328.69308, 66049.04782, 17362.89014, 11901.09465,
7510.13048])),
])
def test_IEA37_ex16_convection(deficitModel, aep_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())
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)
@pytest.mark.parametrize(
'deficitModel,ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(BastankhahGaussianDeficit(ct2a=ct2a_mom1d),
[0.17, 3.55, 7.61, 8.12, 7.58, 6.63, 5.93, 6.04, 6.65, 7.35, 7.69, 7.65, 7.54, 7.45, 7.55, 7.84, 8.19]),
(ZongGaussianDeficit(ct2a=ct2a_mom1d, eps_coeff=0.35),
[6.34, 7.05, 7.9, 8.15, 7.45, 6.19, 5.21, 5.26, 6.38, 7.32, 7.7, 7.54, 7.34, 7.18, 7.32, 7.69, 8.14]),
(NiayifarGaussianDeficit(ct2a=ct2a_mom1d),
[0.17, 3.55, 7.61, 8.12, 7.58, 6.63, 5.93, 6.04, 6.65, 7.35, 7.69, 7.65, 7.55, 7.45, 7.56, 7.84, 8.19]),
])
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)
@pytest.mark.parametrize(
'deficitModel,ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(ZongGaussianDeficit(ct2a=ct2a_mom1d, eps_coeff=0.35),
[6.34, 7.05, 8.18, 8.25, 7.49, 6.2, 5.2, 5.26, 6.37, 7.33, 7.7, 7.54, 7.34, 7.18, 7.32, 7.7, 8.16])
])
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')
print(np.round(Z[49, 100:133:2], 2).values.tolist())
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)
@pytest.mark.parametrize(
'windFarmModel,aep_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(NOJ,
(368764.7199209298, [9863.98445, 8458.99574, 10863.93795, 14022.65446, 22279.85026,
25318.68166, 37461.855, 42999.89503, 24857.24081, 13602.39867,
14348.77375, 31867.18218, 75509.51435, 17661.32988, 11773.35282,
7875.07291])),
(NOJLocal,
(336587.0633777636, [8393.09366, 7644.39035, 10690.37193, 13095.25925, 19271.97188,
23644.21809, 36863.35147, 38858.98426, 21150.59603, 12106.08548,
13868.64937, 31092.31296, 64287.70342, 17231.88429, 11379.40461,
7008.78633])),
(BastankhahGaussian, (355597.1277349052,
[9147.1739, 8136.64045, 11296.20887, 13952.43453, 19784.35422,
25191.89568, 38952.44439, 41361.25562, 23050.87822, 12946.16957,
14879.10238, 32312.09672, 66974.91909, 17907.90902, 12208.49426,
7495.1508])),
(IEA37SimpleBastankhahGaussian, read_iea37_windfarm(iea37_path + 'iea37-ex16.yaml')[2]),
(lambda *args, **kwargs: Fuga(tfp + 'fuga/2MW/Z0=0.00408599Zi=00400Zeta0=0.00E+00.nc', *args, **kwargs),
(404422.9302289747, [9911.82745, 9761.75297, 12509.30601, 15396.26107, 23016.74091,
27798.80471, 43135.53798, 49622.24427, 24977.80518, 15459.8399,
16721.53971, 35692.36221, 77966.92736, 19781.30917, 13720.23771,
8950.43363])),
(GCL, (370863.6246093183,
[9385.75387, 8768.52105, 11450.13309, 14262.42186, 21178.74926,
25751.59502, 39483.21753, 44573.31533, 23652.09976, 13924.58752,
15106.11692, 32840.02909, 71830.22035, 18200.49805, 12394.7626,
8061.6033])),
(GCLLocal, (381187.36105425097,
[9678.85358, 9003.65526, 11775.06899, 14632.42259, 21915.85495,
26419.65189, 40603.68618, 45768.58091, 24390.71103, 14567.43106,
15197.82861, 32985.67922, 75062.92788, 18281.21981, 12470.01322,
8433.77587])),
(ZongGaussian, (354394.892004361,
[8980.72989, 8091.71749, 11310.53734, 13932.45776, 19973.7833,
25155.82651, 39001.85288, 41132.89722, 22631.43932, 12939.46983,
14755.07905, 32302.53737, 66685.94946, 17902.61107, 12106.73153,
7491.272])),
(NiayifarGaussian, (362094.6051760222,
[9216.49947, 8317.79564, 11380.71714, 14085.18651, 20633.99691,
25431.58675, 39243.85219, 42282.12782, 23225.57866, 13375.79217,
14794.64817, 32543.64467, 69643.8641, 18036.2368, 12139.1985,
7743.87967])),
])
def test_IEA37_ex16_windFarmModel(windFarmModel, aep_ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
wf_model = windFarmModel(site, windTurbines, turbulenceModel=GCLTurbulence())
wf_model.superpositionModel = SquaredSum()
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_own_deficit_is_zero():
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
for deficitModel in get_models(WakeDeficitModel):
wf_model = All2AllIterative(site, windTurbines, wake_deficitModel=deficitModel(),
turbulenceModel=STF2017TurbulenceModel())
sim_res = wf_model([0], [0], yaw=0)
npt.assert_array_equal(sim_res.WS_eff, sim_res.WS.broadcast_like(sim_res.WS_eff))
@pytest.mark.parametrize('upstream_only,ref', [(False, [[9, 9, 9], # -1 upstream
[7, 7, 7]]), # - (1+2) downstream
(True, [[9, 9, 9], # -1 upstream
[8, 8, 8]])]) # -2 downstream
def test_wake_blockage_split(upstream_only, ref):
class MyWakeModel(WakeDeficitModel):
def calc_deficit(self, dw_ijlk, **kwargs):
return np.ones_like(dw_ijlk) * 2
class MyBlockageModel(BlockageDeficitModel):
def __init__(self, upstream_only):
BlockageDeficitModel.__init__(self, upstream_only=upstream_only)
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)
@pytest.mark.parametrize('deficitModel', get_models(WakeDeficitModel))
def test_All2AllIterative_WakeDeficit_RotorAvg(deficitModel):
if deficitModel == NOJLocalDeficit:
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines,
wake_deficitModel=deficitModel(rotorAvgModel=CGIRotorAvg(4)),
turbulenceModel=STF2017TurbulenceModel())
sim_res = wf_model([0, 500, 1000, 1500], [0, 0, 0, 0], wd=270, ws=10)
if 0:
sim_res.flow_map(XYGrid(x=np.linspace(-200, 2000, 100))).plot_wake_map()
plt.show()
@pytest.mark.parametrize('deficitModel', get_models(WakeDeficitModel))
def test_flow_map_symmetry(deficitModel):
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(UniformSite(), windTurbines,
wake_deficitModel=deficitModel(),
turbulenceModel=STF2017TurbulenceModel())
sim_res = wf_model([0], [0], wd=270, ws=10, yaw=0)
ws = sim_res.flow_map(YZGrid(x=0, y=np.linspace(-150, 150, 20), z=windTurbines.hub_height())).WS_eff.squeeze()
ws_center = sim_res.flow_map(XYGrid(x=np.linspace(-150, 150, 20), y=0)).WS_eff.squeeze()
if 0:
ax1, ax2 = plt.subplots(2)[1]
ws.plot(ax=ax1, label=deficitModel.__name__)
ax1.legend()
ws_center.plot(ax=ax2)
plt.show()
npt.assert_array_almost_equal(ws[:10], ws[10:][::-1])
if deficitModel is not NoWakeDeficit:
assert min(ws) < 9
npt.assert_array_less(ws_center[10:], 9)
npt.assert_array_almost_equal(ws_center[:10], 10)