def test_aep_wind_atlas_method():
site = Hornsrev1Site()
wt = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site, wt)
x, y = [0], [0]
wd = np.arange(360)
aep_lps = wfm(x, y, wd=wd,
ws=np.arange(3, 27)).aep(linear_power_segments=True)
aep = wfm(x, y, wd=wd, ws=np.r_[3, np.arange(3.5, 27)]).aep()
if 0:
plt.plot(aep_lps.ws,
np.cumsum(aep_lps.sum(['wt', 'wd'])),
'.-',
label='Linear power segments')
plt.plot(aep.ws,
np.cumsum(aep.sum(['wt', 'wd'])),
'.-',
label='Constant power segments')
plt.ylabel('Cumulated AEP [GWh]')
plt.xlabel('Wind speed [m/s]')
plt.legend()
plt.show()
npt.assert_almost_equal(aep_lps.sum(), 16.73490444)
npt.assert_almost_equal(aep.sum(), 16.69320343)
def get_iea37_cost(n_wt=9):
"""Cost component that wraps the IEA 37 AEP calculator"""
wd = npa(range(16)) * 22.5 # only 16 bins
site = IEA37Site(n_wt)
wind_turbines = IEA37_WindTurbines()
wake_model = IEA37SimpleBastankhahGaussian(site, wind_turbines)
return PyWakeAEPCostModelComponent(wake_model, n_wt, wd=wd)
def test_yaw_tilt(yaw, tilt, cx, cz):
site = IEA37Site(16)
x, y = [0], [0]
windTurbines = V80()
D = windTurbines.diameter()
wfm = IEA37SimpleBastankhahGaussian(
site, windTurbines, deflectionModel=JimenezWakeDeflection())
x_lst = np.linspace(-200, 1000, 100)
y_lst = np.linspace(-100, 100, 201)
z_lst = np.arange(10, 200, 1)
fm_yz = wfm(x, y, wd=270, ws=10, yaw=yaw,
tilt=tilt).flow_map(YZGrid(x=5 * D, y=y_lst, z=z_lst))
fm_xz = wfm(x, y, wd=180, ws=10, yaw=yaw,
tilt=tilt).flow_map(YZGrid(x=0, y=x_lst, z=z_lst))
fm_xy = wfm(x, y, wd=270, ws=10, yaw=yaw,
tilt=tilt).flow_map(XYGrid(x=x_lst))
if 0:
axes = plt.subplots(3, 1)[1].flatten()
fm_xy.plot_wake_map(ax=axes[0])
axes[0].axvline(5 * D)
fm_xz.plot_wake_map(ax=axes[1])
axes[1].axvline(5 * D)
fm_yz.plot_wake_map(ax=axes[2])
plt.show()
wake_center = fm_yz.WS_eff.where(fm_yz.WS_eff == fm_yz.WS_eff.min(),
drop=True).squeeze()
npt.assert_allclose(wake_center.y, cx, atol=.1)
npt.assert_allclose(wake_center.h, cz, atol=.24)
def test_get_model_input():
site, windTurbines = IEA37Site(16), IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site, windTurbines)
args = get_model_input(wfm, [1000], [0], ws=10, wd=270)
npt.assert_array_almost_equal(args['dw_ijl'], [[[1000]]])
npt.assert_array_almost_equal(args['hcw_ijl'], [[[0]]])
npt.assert_array_almost_equal(args['dh_ijl'], [[[0]]])
npt.assert_array_almost_equal(args['yaw_ilk'], [[[0]]])
npt.assert_array_almost_equal(args['WS_ilk'], [[[10]]])
npt.assert_array_almost_equal(args['ct_ilk'], [[[8 / 9]]])
def test_RotorGridAvg_deficit():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines)
flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100)))
plt.plot(flow_map.Y[:, 0], flow_map.WS_eff_xylk[:, 0, 0, 0])
R = windTurbines.diameter() / 2
for name, rotorAvgModel, ref1 in [
('RotorCenter', RotorCenter(), 7.172723970425709),
('RotorGrid2', EqGridRotorAvg(2), 7.495889360682771),
('RotorGrid3', EqGridRotorAvg(3), 7.633415167369133),
('RotorGrid4', EqGridRotorAvg(4), 7.710215921858325),
('RotorGrid100', EqGridRotorAvg(100), 7.820762402628349),
('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 7.826105012683896),
('RotorCGI4', CGIRotorAvg(4), 7.848406907726826),
('RotorCGI4', CGIRotorAvg(7), 7.819900693605533),
('RotorCGI4', CGIRotorAvg(9), 7.82149363932618),
('RotorCGI4', CGIRotorAvg(21), 7.821558905416136)]:
# test with PropagateDownwind
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
rotorAvgModel=rotorAvgModel)
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.WS_eff_ilk[1, 0, 0], ref1)
# test with All2AllIterative
wfm = All2AllIterative(site, windTurbines,
IEA37SimpleBastankhahGaussianDeficit(),
rotorAvgModel=rotorAvgModel,
superpositionModel=SquaredSum())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.WS_eff_ilk[1, 0, 0], ref1)
plt.plot([-R, R], [sim_res.WS_eff_ilk[1, 0, 0]] * 2, label=name)
if 0:
plt.legend()
plt.show()
plt.close('all')
def main():
if __name__ == '__main__':
try:
import matplotlib.pyplot as plt
plt.gcf()
plot_comp = XYPlotComp()
plot = True
except RuntimeError:
plot_comp = NoPlot()
plot = False
n_wt = 16
site = IEA37Site(n_wt)
windTurbines = IEA37_WindTurbines()
windFarmModel = IEA37SimpleBastankhahGaussian(site, windTurbines)
Drotor_vector = [windTurbines.diameter()] * n_wt
power_rated_vector = [float(windTurbines.power(20) / 1000)] * n_wt
hub_height_vector = [windTurbines.hub_height()] * n_wt
def irr_func(aep, **kwargs):
my_irr = economic_evaluation(Drotor_vector, power_rated_vector,
hub_height_vector,
aep).calculate_irr()
print(my_irr)
return my_irr
aep_comp = CostModelComponent(
input_keys=['x', 'y'],
n_wt=n_wt,
cost_function=lambda x, y, **_: windFarmModel(x=x, y=y).aep().sum(
['wd', 'ws']) * 10**6,
output_key="aep",
output_unit="GWh",
objective=False,
output_val=np.zeros(n_wt))
irr_comp = CostModelComponent(input_keys=['aep'],
n_wt=n_wt,
cost_function=irr_func,
output_key="irr",
output_unit="%",
objective=True,
income_model=True)
group = TopFarmGroup([aep_comp, irr_comp])
problem = TopFarmProblem(
design_vars=dict(zip('xy', site.initial_position.T)),
cost_comp=group,
driver=EasyRandomSearchDriver(
randomize_func=RandomizeTurbinePosition_Circle(), max_iter=5),
constraints=[
SpacingConstraint(200),
CircleBoundaryConstraint([0, 0], 1300.1)
],
plot_comp=plot_comp)
cost, state, recorder = problem.optimize()
def test_RotorGridAvg_ti():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel())
flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100)))
plt.plot(flow_map.Y[:, 0], flow_map.TI_eff_xylk[:, 0, 0, 0])
R = windTurbines.diameter() / 2
for name, rotorAvgModel, ref1 in [
('RotorCenter', RotorCenter(), 0.22292190804089568),
('RotorGrid2', EqGridRotorAvg(2), 0.2111162769995657),
('RotorGrid3', EqGridRotorAvg(3), 0.2058616982653193),
('RotorGrid4', EqGridRotorAvg(4), 0.2028701990648858),
('RotorGrid100', EqGridRotorAvg(100), 0.1985255601976247),
('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 0.1982984399750206)]:
# test with PropagateDownwind
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
rotorAvgModel=rotorAvgModel,
turbulenceModel=STF2017TurbulenceModel())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1)
# test with All2AllIterative
wfm = All2AllIterative(site, windTurbines,
IEA37SimpleBastankhahGaussianDeficit(),
rotorAvgModel=rotorAvgModel,
superpositionModel=SquaredSum(),
turbulenceModel=STF2017TurbulenceModel())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1)
plt.plot([-R, R], [sim_res.TI_eff_ilk[1, 0, 0]] * 2, label=name)
if 0:
plt.legend()
plt.show()
plt.close('all')
def test_dAEP_2wt():
site = Hornsrev1Site()
iea37_site = IEA37Site(16)
wt = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site, wt)
x, y = iea37_site.initial_position[np.array([0, 2, 5, 8, 14])].T
# plot 2 wt case
x, y = np.array([[0, 130 * 4], [0, 0]], dtype=np.float)
x_lst = np.array([0., 1.]) * np.arange(1, 600, 10)[:, na]
kwargs = {'ws': [10], 'wd': [270]}
_, (ax1, ax2) = plt.subplots(1, 2, sharey=False)
ax1.plot(x_lst[:, 1], [wfm.aep(x_, y, **kwargs) for x_ in x_lst])
ax1.set_xlabel('Downwind distance [m]')
ax1.set_ylabel('AEP [GWh]')
x_ = x_lst[20]
ax1.set_title("Center line")
for grad in [fd, cs, autograd]:
dAEPdx = wfm.dAEPdn(0, grad)(x_, y, **kwargs)[1]
npt.assert_almost_equal(dAEPdx / 360, 3.976975605364392e-06,
(10, 5)[grad == fd])
plot_gradients(wfm.aep(x_, y, **kwargs),
dAEPdx,
x_[1],
grad.__name__,
step=100,
ax=ax1)
y_lst = np.array([0, 1.]) * np.arange(-100, 100, 5)[:, na]
ax2.plot(y_lst[:, 1], [wfm.aep(x, y_, **kwargs) for y_ in y_lst])
ax2.set_xlabel('Crosswind distance [m]')
ax2.set_ylabel('AEP [GWh]')
y_ = y_lst[25]
ax2.set_title("%d m downstream" % x[1])
for grad in [fd, cs, autograd]:
dAEPdy = wfm.dAEPdn(1, grad)(x, y_, **kwargs)[1]
plot_gradients(wfm.aep(x, y_, **kwargs),
dAEPdy,
y_[1],
grad.__name__,
step=50,
ax=ax2)
npt.assert_almost_equal(dAEPdy / 360, 3.794435973860448e-05,
(10, 5)[grad == fd])
if 0:
plt.legend()
plt.show()
plt.close()
def test_RotorAvg_deficit():
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel())
flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100)))
plt.plot(flow_map.Y[:, 0], flow_map.TI_eff_xylk[:, 0, 0, 0])
R = windTurbines.diameter() / 2
for name, rotorAvgModel, ref1 in [
('None', None, 0.22292190804089568),
('RotorCenter', RotorCenter(), 0.22292190804089568),
('RotorGrid100', EqGridRotorAvg(100), 0.1989725533174574),
('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 0.19874837617113356),
('RotorCGI4', CGIRotorAvg(4), 0.19822024411411204),
('RotorCGI4', CGIRotorAvg(21), 0.1989414764606653)]:
# test with PropagateDownwind
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel(rotorAvgModel=rotorAvgModel))
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1, err_msg=name)
# test with All2AllIterative
wfm = All2AllIterative(site, windTurbines,
IEA37SimpleBastankhahGaussianDeficit(),
turbulenceModel=STF2017TurbulenceModel(rotorAvgModel=rotorAvgModel),
superpositionModel=SquaredSum())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1)
plt.plot([-R, R], [sim_res.WS_eff_ilk[1, 0, 0]] * 2, label=name)
if 0:
plt.legend()
plt.show()
plt.close()
def test_dAEPdx():
site = Hornsrev1Site()
iea37_site = IEA37Site(16)
wt = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site, wt)
x, y = iea37_site.initial_position[np.array([0, 2, 5, 8, 14])].T
dAEPdxy_autograd = wfm.dAEPdxy(gradient_method=autograd)(x, y)
dAEPdxy_cs = wfm.dAEPdxy(gradient_method=cs)(x, y)
dAEPdxy_fd = wfm.dAEPdxy(gradient_method=fd)(x, y)
npt.assert_array_almost_equal(dAEPdxy_autograd, dAEPdxy_cs, 15)
npt.assert_array_almost_equal(dAEPdxy_autograd, dAEPdxy_fd, 6)
def main():
if __name__ == '__main__':
n_wt = 16
site = IEA37Site(n_wt)
windTurbines = IEA37_WindTurbines()
windFarmModel = IEA37SimpleBastankhahGaussian(site, windTurbines)
tf = TopFarmProblem(
design_vars=dict(zip('xy', site.initial_position.T)),
cost_comp=PyWakeAEPCostModelComponent(windFarmModel, n_wt),
driver=EasyRandomSearchDriver(
randomize_func=RandomizeTurbinePosition_Circle(), max_iter=5),
constraints=[CircleBoundaryConstraint([0, 0], 1300.1)],
plot_comp=XYPlotComp())
tf.optimize()
tf.plot_comp.show()
def main():
if __name__ == '__main__':
plot_comp = XYPlotComp()
site = get_site()
n_wt = len(site.initial_position)
windTurbines = DTU10MW()
min_spacing = 2 * windTurbines.diameter(0)
windFarmModel = IEA37SimpleBastankhahGaussian(site, windTurbines)
Drotor_vector = [windTurbines.diameter()] * n_wt
power_rated_vector = [float(windTurbines.power(20) / 1000)] * n_wt
hub_height_vector = [windTurbines.hub_height()] * n_wt
def aep_func(x, y, **_):
sim_res = windFarmModel(x, y)
aep = sim_res.aep()
return aep.sum(['wd', 'ws']).values * 10**6
def irr_func(aep, **_):
return economic_evaluation(Drotor_vector, power_rated_vector,
hub_height_vector, aep).calculate_irr()
aep_comp = CostModelComponent(input_keys=['x', 'y'],
n_wt=n_wt,
cost_function=aep_func,
output_key="aep",
output_unit="GWh",
objective=False,
output_val=np.zeros(n_wt))
irr_comp = CostModelComponent(input_keys=['aep'],
n_wt=n_wt,
cost_function=irr_func,
output_key="irr",
output_unit="%",
objective=True,
income_model=True)
group = TopFarmGroup([aep_comp, irr_comp])
problem = TopFarmProblem(
design_vars=dict(zip('xy', site.initial_position.T)),
cost_comp=group,
driver=EasyRandomSearchDriver(
randomize_func=RandomizeTurbinePosition_Circle(), max_iter=10),
constraints=[
SpacingConstraint(min_spacing),
XYBoundaryConstraint(site.boundary),
],
plot_comp=plot_comp)
cost, state, recorder = problem.optimize()
problem.plot_comp.show()
def main():
if __name__ == '__main__':
for n_wt in [9, 16, 36, 64]:
x, y, aep_ref = read_iea37_windfarm(iea37_path +
'iea37-ex%d.yaml' % n_wt)
if 0:
import matplotlib.pyplot as plt
plt.plot(x, y, '2k')
for i, (x_, y_) in enumerate(zip(x, y)):
plt.annotate(i, (x_, y_))
plt.axis('equal')
plt.show()
site = IEA37Site(n_wt)
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
aep = wake_model(x, y, wd=np.arange(0, 360, 22.5), ws=[9.8]).aep()
# Compare to reference results provided for IEA task 37
print(n_wt, aep_ref[0] * 1e-3, aep)
def test_dAEP_2wt():
site = Hornsrev1Site()
iea37_site = IEA37Site(16)
wsp_cut_in = 4
wsp_cut_out = 25
wsp_rated = 9.8
power_rated = 3350000
constant_ct = 8 / 9
def ct(wsp):
wsp = np.asarray(wsp)
ct = np.zeros_like(wsp, dtype=float)
ct[(wsp >= wsp_cut_in) & (wsp <= wsp_cut_out)] = constant_ct
return ct
def power(wsp):
wsp = np.asarray(wsp)
power = np.where((wsp > wsp_cut_in) & (wsp <= wsp_cut_out),
np.minimum(
power_rated * ((wsp - wsp_cut_in) /
(wsp_rated - wsp_cut_in))**3,
power_rated), 0)
return power
def dpower(wsp):
return np.where((wsp > wsp_cut_in) & (wsp <= wsp_rated),
3 * power_rated * (wsp - wsp_cut_in)**2 /
(wsp_rated - wsp_cut_in)**3, 0)
def dct(wsp):
return wsp * 0 # constant ct
wt = DeprecatedOneTypeWindTurbines(name='test',
diameter=130,
hub_height=110,
ct_func=ct,
power_func=power,
power_unit='w')
wt.set_gradient_funcs(dpower, dct)
wfm = IEA37SimpleBastankhahGaussian(site, wt)
x, y = iea37_site.initial_position[np.array([0, 2, 5, 8, 14])].T
# plot 2 wt case
x, y = np.array([[0, 130 * 4], [0, 0]], dtype=float)
x_lst = np.array([0., 1.]) * np.arange(1, 600, 10)[:, na]
kwargs = {'ws': [10], 'wd': [270]}
_, (ax1, ax2) = plt.subplots(1, 2, sharey=False)
ax1.plot(x_lst[:, 1], [wfm.aep(x_, y, **kwargs) for x_ in x_lst])
ax1.set_xlabel('Downwind distance [m]')
ax1.set_ylabel('AEP [GWh]')
x_ = x_lst[20]
ax1.set_title("Center line")
for grad in [fd, cs, autograd]:
dAEPdx = wfm.dAEPdn(0, grad)(x_, y, **kwargs)[1]
npt.assert_almost_equal(dAEPdx / 360, 3.976975605364392e-06,
(10, 5)[grad == fd])
plot_gradients(wfm.aep(x_, y, **kwargs),
dAEPdx,
x_[1],
grad.__name__,
step=100,
ax=ax1)
y_lst = np.array([0, 1.]) * np.arange(-100, 100, 5)[:, na]
ax2.plot(y_lst[:, 1], [wfm.aep(x, y_, **kwargs) for y_ in y_lst])
ax2.set_xlabel('Crosswind distance [m]')
ax2.set_ylabel('AEP [GWh]')
y_ = y_lst[25]
ax2.set_title("%d m downstream" % x[1])
for grad in [fd, cs, autograd]:
dAEPdy = wfm.dAEPdn(1, grad)(x, y_, **kwargs)[1]
plot_gradients(wfm.aep(x, y_, **kwargs),
dAEPdy,
y_[1],
grad.__name__,
step=50,
ax=ax2)
npt.assert_almost_equal(dAEPdy / 360, 3.794435973860448e-05,
(10, 5)[grad == fd])
if 0:
plt.legend()
plt.show()
plt.close('all')
from topfarm.drivers.random_search_driver import RandomizeTurbinePosition
from topfarm import TopFarmProblem
from topfarm.plotting import NoPlot, XYPlotComp
from topfarm.constraint_components.boundary import XYBoundaryConstraint
from topfarm.constraint_components.spacing import SpacingConstraint
from topfarm.examples.data.parque_ficticio_offshore import ParqueFicticioOffshore
from py_wake.deficit_models.gaussian import IEA37SimpleBastankhahGaussian
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines
site = ParqueFicticioOffshore()
x_init, y_init = site.initial_position[:, 0], site.initial_position[:, 1]
boundary = site.boundary
# # # Wind turbines and wind farm model definition
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site, windTurbines)
wsp = np.asarray([10, 15])
wdir = np.arange(0, 360, 45)
maximum_water_depth = -52
n_wt = x_init.size
maxiter = 10
def aep_func(x, y, **kwargs):
simres = wfm(x, y, wd=wdir, ws=wsp)
aep = simres.aep().values.sum()
water_depth = np.diag(wfm.site.ds.interp(x=x, y=y)['water_depth'])
return [aep, water_depth]
def main():
if __name__ == '__main__':
try:
import matplotlib.pyplot as plt
plt.gcf()
plot_comp = XYPlotComp()
plot = True
except RuntimeError:
plot_comp = NoPlot()
plot = False
n_wt = 16
site = IEA37Site(n_wt)
windTurbines = IEA37_WindTurbines()
windFarmModel = IEA37SimpleBastankhahGaussian(site, windTurbines)
Drotor_vector = [windTurbines.diameter()] * n_wt
power_rated_vector = [float(windTurbines.power(20)) * 1e-6] * n_wt
hub_height_vector = [windTurbines.hub_height()] * n_wt
distance_from_shore = 10 # [km]
energy_price = 0.1 # [Euro/kWh] What we get per kWh
project_duration = 20 # [years]
rated_rpm_array = [12] * n_wt # [rpm]
water_depth_array = [15] * n_wt # [m]
eco_eval = economic_evaluation(distance_from_shore, energy_price, project_duration)
def irr_func(aep, **kwargs):
eco_eval.calculate_irr(
rated_rpm_array,
Drotor_vector,
power_rated_vector,
hub_height_vector,
water_depth_array,
aep)
print(eco_eval.IRR)
return eco_eval.IRR
aep_comp = CostModelComponent(
input_keys=['x', 'y'],
n_wt=n_wt,
cost_function=lambda x, y, **_: windFarmModel(x=x, y=y).aep().sum(['wd', 'ws']) * 10**6,
output_key="aep",
output_unit="kWh",
objective=False,
output_val=np.zeros(n_wt))
irr_comp = CostModelComponent(
input_keys=['aep'],
n_wt=n_wt,
cost_function=irr_func,
output_key="irr",
output_unit="%",
objective=True,
income_model=True)
group = TopFarmGroup([aep_comp, irr_comp])
problem = TopFarmProblem(
design_vars=dict(zip('xy', site.initial_position.T)),
cost_comp=group,
driver=EasyRandomSearchDriver(randomize_func=RandomizeTurbinePosition_Circle(), max_iter=5),
constraints=[SpacingConstraint(200),
CircleBoundaryConstraint([0, 0], 1300.1)],
plot_comp=plot_comp)
cost, state, recorder = problem.optimize()
from py_wake.examples.data.iea34_130rwt import IEA34_130_1WT_Surrogate
from py_wake.deflection_models.jimenez import JimenezWakeDeflection
from py_wake.superposition_models import MaxSum
from py_wake.wind_turbines.power_ct_functions import SimpleYawModel
site = LillgrundSite()
x, y = site.initial_position.T
#keeping only every second turbine as lillegrund turbines are approx. half the size of the iea 3.4MW
x = x[::2]
y = y[::2]
# # Wind turbines and wind farm model definition
windTurbines = IEA34_130_1WT_Surrogate() #additional_models=[SimpleYawModel()]
wfm = IEA37SimpleBastankhahGaussian(
site,
windTurbines,
deflectionModel=JimenezWakeDeflection(),
turbulenceModel=STF2017TurbulenceModel(
addedTurbulenceSuperpositionModel=MaxSum()))
load_signals = [
'del_blade_flap', 'del_blade_edge', 'del_tower_bottom_fa',
'del_tower_bottom_ss', 'del_tower_top_torsion'
]
wsp = np.asarray([10, 15])
wdir = np.asarray([90])
n_wt = x.size
i = n_wt
k = wsp.size
l = wdir.size
from py_wake.turbulence_models.stf import STF2017TurbulenceModel
from py_wake.examples.data.iea34_130rwt import IEA34_130_1WT_Surrogate
from py_wake.superposition_models import MaxSum
site = LillgrundSite()
x, y = site.initial_position.T
#keeping only every second turbine as lillegrund turbines are approx. half the size of the iea 3.4MW
x = x[::2]
y = y[::2]
x_init = x
y_init = y
# # Wind turbines and wind farm model definition
windTurbines = IEA34_130_1WT_Surrogate()
wfm = IEA37SimpleBastankhahGaussian(
site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel(
addedTurbulenceSuperpositionModel=MaxSum()))
load_signals = [
'del_blade_flap', 'del_blade_edge', 'del_tower_bottom_fa',
'del_tower_bottom_ss', 'del_tower_top_torsion'
]
wsp = np.asarray([10, 15])
wdir = np.arange(0, 360, 45)
n_wt = x.size
i = n_wt
k = wsp.size
l = wdir.size