def test_neighbour_farm_speed():
# import and setup site and windTurbines
site = IEA37Site(16)
# setup current, neighbour and all positions
wt_x, wt_y = site.initial_position.T
neighbour_x, neighbour_y = wt_x - 4000, wt_y
all_x, all_y = np.r_[wt_x, neighbour_x], np.r_[wt_y, neighbour_y]
windTurbines = WindTurbines.from_WindTurbines([IEA37_WindTurbines(), IEA37_WindTurbines()])
windTurbines._names = ["Current wind farm", "Neighbour wind farm"]
types = [0] * len(wt_x) + [1] * len(neighbour_x)
wf_model = PropagateDownwind(site, windTurbines,
wake_deficitModel=BastankhahGaussianDeficit(use_effective_ws=True),
superpositionModel=LinearSum())
# Consider wd=270 +/- 30 deg only
wd_lst = np.arange(240, 301)
sim_res, t = timeit(wf_model, verbose=False)(all_x, all_y, type=types, ws=9.8, wd=wd_lst)
if 1:
ext = 100
flow_box = wf_model(neighbour_x, neighbour_y, wd=wd_lst).flow_box(
x=np.linspace(min(wt_x) - ext, max(wt_x) + ext, 53),
y=np.linspace(min(wt_y) - ext, max(wt_y) + ext, 51),
h=[100, 110, 120])
wake_site = XRSite.from_flow_box(flow_box)
wake_site.save('tmp.nc')
else:
wake_site = XRSite.load('tmp.nc')
wf_model_wake_site = PropagateDownwind(wake_site, windTurbines,
wake_deficitModel=BastankhahGaussianDeficit(use_effective_ws=True),
superpositionModel=LinearSum())
sim_res_wake_site, _ = timeit(wf_model_wake_site, verbose=False)(wt_x, wt_y, ws=9.8, wd=wd_lst)
npt.assert_allclose(sim_res.aep().sel(wt=np.arange(len(wt_x))).sum(), sim_res_wake_site.aep().sum(), rtol=0.0005)
npt.assert_array_almost_equal(sim_res.aep().sel(wt=np.arange(len(wt_x))), sim_res_wake_site.aep(), 2)
def run_wms(swc,
test_cases=[
'Wieringermeer-West', 'Wieringermeer-East', 'Nibe',
'Nordtank-500', 'NREL-5MW_TIlow', 'NREL-5MW_TIhigh'
],
deficit_models=[NOJDeficit(),
BastankhahGaussianDeficit()],
wds=np.linspace(-30, 30, 61)):
'''
Run the different wake models for the specified sites and output simulation results
'''
swc_out = {}
for case in test_cases:
swc_out[case] = swc[case]
x_j = swc[case]['sDown'] * (np.dot(
swc[case]['xDown'][:, na],
np.cos(wds / 180.0 * np.pi)[na, :])).flatten()
y_j = swc[case]['sDown'] * (np.dot(
swc[case]['xDown'][:, na],
np.sin(wds / 180.0 * np.pi)[na, :])).flatten()
ii, jj = len(swc[case]['xDown']), len(wds)
swc_out[case]['x'] = x_j.reshape(ii, jj)
swc_out[case]['y'] = y_j.reshape(ii, jj)
swc_out[case]['wds'] = wds
swc_out[case]['deficit_models'] = []
for model in deficit_models:
# set up model
tmp = {}
wfm = PropagateDownwind(swc[case]['site'],
swc[case]['wt'],
model,
superpositionModel=SquaredSum(),
rotorAvgModel=RotorCenter(),
turbulenceModel=STF2017TurbulenceModel())
# simulation
sim_res = wfm([0], [0], h=[100], wd=[270])
lw_j, WS_eff_jlk, TI_eff_jlk = wfm._flow_map(
x_j, y_j,
np.ones_like(x_j) * 100, sim_res)
deficit_name = modify_deficit_name_sw(wfm.wake_deficitModel,
TI_eff_jlk)
tmp['bar_label'] = modify_deficit_name(wfm.wake_deficitModel)
tmp['WS_eff'] = WS_eff_jlk[:, 0, 0].reshape(ii, jj)
tmp['TI_eff'] = TI_eff_jlk[:, 0, 0].reshape(ii, jj)
swc_out[case][deficit_name] = tmp
swc_out[case]['deficit_models'].append(deficit_name)
return swc_out
'deficitModel,aep_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[
(NOJDeficit(), (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(), (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
])),
(BastankhahGaussianDeficit(), (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/'),
(404441.6306021485, [
9912.33731, 9762.05717, 12510.14066, 15396.76584, 23017.66483,
27799.7161, 43138.41606, 49623.79059, 24979.09001, 15460.45923,
16723.02619, 35694.35526, 77969.14805, 19782.41376, 13721.45739,
8950.79218
])),
return all_deficit_modules
@pytest.mark.parametrize(
'deficitModel,aep_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[(NOJDeficit(), (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(), (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])),
(BastankhahGaussianDeficit(), (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=0.00014617Zi=00399Zeta0=0.00E+0/'),
(398938.8941139709, [9632.92248, 9733.98766, 12462.98413, 15332.30502, 22199.91899,
27683.32851, 42975.80734, 49481.10395, 24274.96466, 15416.63681,
16681.20957, 35508.27583, 75263.59612, 19679.2854, 13687.14632,
8925.42131])),
(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])),
'deficitModel,aep_ref',
# test that the result is equal to last run (no evidens that these number are correct)
[
(NOJDeficit(), (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(), (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
])),
(BastankhahGaussianDeficit(), (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=0.00014617Zi=00399Zeta0=0.00E+0/'),
(398938.8941139709, [
9632.92248, 9733.98766, 12462.98413, 15332.30502, 22199.91899,
27683.32851, 42975.80734, 49481.10395, 24274.96466, 15416.63681,
16681.20957, 35508.27583, 75263.59612, 19679.2854, 13687.14632,
8925.42131
])),