from abc import abstractmethod, ABC
from py_wake.site._site import Site, UniformSite, LocalWind
from py_wake.wind_turbines import WindTurbines
from py_wake import np
from py_wake.flow_map import FlowMap, HorizontalGrid, FlowBox, Grid
import xarray as xr
from py_wake.utils import xarray_utils, weibull # register ilk function @UnusedImport
from numpy import newaxis as na
from py_wake.utils.model_utils import check_model, fix_shape
from py_wake.utils.xarray_utils import ilk2da, ijlk2da
import multiprocessing
from py_wake.utils.parallelization import get_pool_map, get_pool_starmap
from py_wake.utils.functions import arg2ilk, coords2ILK
from py_wake.utils.gradients import autograd
from py_wake.noise_models.iso import ISONoiseModel
class WindFarmModel(ABC):
"""Base class for RANS and engineering flow models"""
verbose = True
def __init__(self, site, windTurbines):
check_model(site, Site, 'site')
check_model(windTurbines, WindTurbines, 'windTurbines')
self.site = site
self.windTurbines = windTurbines
def __call__(self, x, y, h=None, type=0, wd=None, ws=None, yaw=None,
tilt=None, time=False, verbose=False,
n_cpu=1, wd_chunks=None, ws_chunks=1, **kwargs):
"""Run the wind farm simulation
Parameters
----------
x : array_like
Wind turbine x positions
y : array_like
Wind turbine y positions
h : array_like, optional
Wind turbine hub heights
type : int or array_like, optional
Wind turbine type, default is 0
wd : int or array_like
Wind direction(s)
ws : int, float or array_like
Wind speed(s)
yaw : int, float, array_like or None, optional
Yaw misalignement, Positive is counter-clockwise when seen from above.
May be
- constant for all wt and flow cases or dependent on
- wind turbine(i),
- wind turbine and wind direction(il) or
- wind turbine, wind direction and wind speed (ilk)
tilt : array_like or None, optional
Tilt angle of rotor shaft. Normal tilt (rotor center above tower top) is positivie
May be
- constant for all wt and flow cases or dependent on
- wind turbine(i),
- wind turbine and wind direction(il) or
- wind turbine, wind direction and wind speed (ilk)
time : boolean or array_like
If False (default), the simulation will be computed for the full wd x ws matrix
If True, the wd and ws will be considered as a time series of flow conditions with time stamp 0,1,..,n
If array_like: same as True, but the time array is used as flow case time stamp
n_cpu : int or None, optional
Number of CPUs to be used for execution.
If 1 (default), the execution is not parallized
If None, the available number of CPUs are used
wd_chunks : int or None, optional
The wind directions are divided into <wd_chunks> chunks. More chunks reduces the memory usage
and allows parallel execution if n_cpu>1.
If wd_chunks is None, wd_chunks is set to the number of CPUs used, i.e. 1 if n_cpu is not specified
ws_chunks : int, optional
The wind speeds are divided into <ws_chunks> chunks. More chunks reduces the memory usage
and allows parallel execution if n_cpu>1.
Returns
-------
SimulationResult
"""
if time is False and np.ndim(wd):
wd = np.sort(wd)
assert len(x) == len(y)
self.verbose = verbose
h, _ = self.windTurbines.get_defaults(len(x), type, h)
wd, ws = self.site.get_defaults(wd, ws)
I, L, K, = len(x), len(np.atleast_1d(wd)), (1, len(np.atleast_1d(ws)))[time is False]
if len([k for k in kwargs if 'yaw' in k.lower() and k != 'yaw' and not k.startswith('yawc_')]):
raise ValueError(
'Custom *yaw*-keyword arguments not allowed to avoid confusion with the default "yaw" keyword')
yaw_ilk = arg2ilk('yaw', [yaw, 0][yaw is None], I, L, K)
tilt_ilk = arg2ilk('tilt', [tilt, 0][tilt is None], I, L, K)
if len(x) == 0:
# No WT
lw = UniformSite([1], 0.1).local_wind(x_i=[], y_i=[], h_i=[], wd=wd, ws=ws)
z_ilk = np.zeros((0, len(lw.wd), len(lw.ws))) # WS_eff_ilk, etc.
return SimulationResult(self, lw, [], yaw_ilk, tilt_ilk, z_ilk, z_ilk, z_ilk, z_ilk, kwargs)
res = self.calc_wt_interaction(x_i=np.asarray(x), y_i=np.asarray(y), h_i=h, type_i=type,
yaw_ilk=yaw_ilk, tilt_ilk=tilt_ilk,
wd=wd, ws=ws, time=time,
n_cpu=n_cpu, wd_chunks=wd_chunks, ws_chunks=ws_chunks,
**kwargs)
WS_eff_ilk, TI_eff_ilk, power_ilk, ct_ilk, localWind, wt_inputs = res
return SimulationResult(self, localWind=localWind,
type_i=np.zeros(len(x), dtype=int) + type,
yaw_ilk=yaw_ilk, tilt_ilk=tilt_ilk,
WS_eff_ilk=WS_eff_ilk, TI_eff_ilk=TI_eff_ilk,
power_ilk=power_ilk, ct_ilk=ct_ilk, wt_inputs=wt_inputs)
def aep(self, x, y, h=None, type=0, wd=None, ws=None, yaw=None, tilt=None, # @ReservedAssignment
normalize_probabilities=False, with_wake_loss=True,
n_cpu=1, wd_chunks=None, ws_chunks=None, **kwargs):
"""Anual Energy Production (sum of all wind turbines, directions and speeds) in GWh.
the typical use is:
>> sim_res = windFarmModel(x,y,...)
>> sim_res.aep()
This function bypasses the simulation result and returns only the total AEP,
which makes it slightly faster for small problems.
>> windFarmModel.aep(x,y,...)
Parameters
----------
x : array_like
Wind turbine x positions
y : array_like
Wind turbine y positions
h : array_like, optional
Wind turbine hub heights
type : int or array_like, optional
Wind turbine type, default is 0
wd : int or array_like
Wind direction(s)
ws : int, float or array_like
Wind speed(s)
yaw : int, float, array_like or None, optional
Yaw misalignement, Positive is counter-clockwise when seen from above.
May be
- constant for all wt and flow cases or dependent on
- wind turbine(i),
- wind turbine and wind direction(il) or
- wind turbine, wind direction and wind speed (ilk)
tilt : array_like or None, optional
Tilt angle of rotor shaft. Normal tilt (rotor center above tower top) is positivie
May be
- constant for all wt and flow cases or dependent on
- wind turbine(i),
- wind turbine and wind direction(il) or
- wind turbine, wind direction and wind speed (ilk)
n_cpu : int or None, optional
Number of CPUs to be used for execution.
If 1 (default), the execution is not parallized
If None, the available number of CPUs are used
wd_chunks : int or None, optional
If n_cpu>1, the wind directions are divided into <wd_chunks> chunks and executed in parallel.
If wd_chunks is None, wd_chunks is set to the available number of CPUs
ws_chunks : int or None, optional
If n_cpu>1, the wind speeds are divided into <ws_chunks> chunks and executed in parallel.
If ws_chunks is None, ws_chunks is set to 1
Returns
-------
AEP in GWh
"""
if n_cpu != 1 or wd_chunks or ws_chunks:
return self._aep_chunk_wrapper(
self._aep_kwargs,
x, y, h, type, wd, ws, yaw, tilt,
normalize_probabilities=False, with_wake_loss=True,
n_cpu=n_cpu, wd_chunks=wd_chunks, ws_chunks=ws_chunks, **kwargs)
wd, ws = self.site.get_defaults(wd, ws)
I, L, K, = len(x), len(np.atleast_1d(wd)), len(np.atleast_1d(ws))
yaw_ilk = fix_shape(yaw, (I, L, K), allow_None=True, allow_number=True)
tilt_ilk = fix_shape(tilt, (I, L, K), allow_None=True, allow_number=True)
_, _, power_ilk, _, localWind, power_ct_inputs = self.calc_wt_interaction(
x_i=x, y_i=y, h_i=h, type_i=type, yaw_ilk=yaw_ilk, tilt_ilk=tilt_ilk, wd=wd, ws=ws, **kwargs)
P_ilk = localWind.P_ilk
if normalize_probabilities:
norm = P_ilk.sum((1, 2))[:, na, na]
else:
norm = 1
if with_wake_loss is False:
power_ilk = self.windTurbines.power(localWind.WS_ilk, **power_ct_inputs)
return (power_ilk * P_ilk / norm * 24 * 365 * 1e-9).sum()
@abstractmethod
def calc_wt_interaction(self, x_i, y_i, h_i=None, type_i=0, yaw_ilk=None,
wd=None, ws=None, time=False,
n_cpu=1, wd_chunks=None, ws_chunks=None, **kwargs):
"""Calculate effective wind speed, turbulence intensity,
power and thrust coefficient, and local site parameters
Typical users should not call this function directly, but by calling the
windFarmModel object (invokes the __call__() function above)
which returns a nice SimulationResult object
Parameters
----------
x_i : array_like
X position of wind turbines
y_i : array_like
Y position of wind turbines
h_i : array_like or None, optional
Hub height of wind turbines\n
If None, default, the standard hub height is used
type_i : array_like or None, optional
Wind turbine types\n
If None, default, the first type is used (type=0)
yaw_ilk : array_like or None, optional
Yaw misalignement [deg] of turbine(i) for wind direction(l) and wind speed (k)\n
Positive is counter-clockwise when seen from above
wd : int, float, array_like or None
Wind directions(s)\n
If None, default, the wake is calculated for site.default_wd
ws : int, float, array_like or None
Wind speed(s)\n
If None, default, the wake is calculated for site.default_ws
n_cpu : int or None, optional
Number of CPUs to be used for execution.
If 1 (default), the execution is not parallized
If None, the available number of CPUs are used
wd_chunks : int or None, optional
If n_cpu>1, the wind directions are divided into <wd_chunks> chunks and executed in parallel.
If wd_chunks is None, wd_chunks is set to the available number of CPUs
ws_chunks : int or None, optional
If n_cpu>1, the wind speeds are divided into <ws_chunks> chunks and executed in parallel.
If ws_chunks is None, ws_chunks is set to 1
Returns
-------
WS_eff_ilk : array_like
Effective wind speeds [m/s]
TI_eff_ilk : array_like
Effective turbulence intensities [-]
power_ilk : array_like
Power productions [w]
ct_ilk : array_like
Thrust coefficients
localWind : LocalWind
Local free-flow wind
"""
def _multiprocessing_chunks(self, wd, ws, time,
n_cpu, wd_chunks, ws_chunks, **kwargs):
n_cpu = n_cpu or multiprocessing.cpu_count()
wd_chunks = np.minimum(wd_chunks or n_cpu, len(wd))
ws_chunks = np.minimum(ws_chunks or 1, len(ws))
if time is not False:
wd_chunks = ws_chunks = np.maximum(ws_chunks, wd_chunks)
wd_i = np.linspace(0, len(wd) + 1, wd_chunks + 1).astype(int)
ws_i = np.linspace(0, len(ws) + 1, ws_chunks + 1).astype(int)
if n_cpu > 1:
map_func = get_pool_map(n_cpu)
else:
map_func = map
if time is False:
# (wd x ws) matrix
slice_lst = [(slice(wd_i0, wd_i1), slice(ws_i0, ws_i1))
for wd_i0, wd_i1 in zip(wd_i[:-1], wd_i[1:])
for ws_i0, ws_i1 in zip(ws_i[:-1], ws_i[1:])]
else:
# (wd, ws) vector
if time is True:
time = np.arange(len(wd))
slice_lst = [(slice(wd_i0, wd_i1), slice(wd_i0, wd_i1))
for wd_i0, wd_i1 in zip(wd_i[:-1], wd_i[1:])
]
I, L, K = len(kwargs.get('x_i', kwargs.get('x'))), len(wd), len(ws)
def get_subtask_arg(k, arg, wd_slice, ws_slice):
if (isinstance(arg, (None.__class__, bool, int, float)) or
k in {'gradient_method', 'wrt_arg'}):
return arg
s = np.shape(arg)
if s in [(), (I,)]:
return arg
elif s == (I, L):
return arg[:, wd_slice]
elif s == (I, L, K):
return arg[:, wd_slice][:, :, ws_slice]
elif s == (L,):
return arg[wd_slice]
elif s == (L, K):
return arg[wd_slice][:, ws_slice]
arg_lst = [{'wd': wd[wd_slice], 'ws': ws[ws_slice], 'time':get_subtask_arg('time', time, wd_slice, ws_slice),
** {k: get_subtask_arg(k, v, wd_slice, ws_slice) for k, v in kwargs.items()}} for wd_slice, ws_slice in slice_lst]
return map_func, arg_lst, wd_chunks, ws_chunks
def _aep_chunk_wrapper(self, aep_function,
x, y, h=None, type=0, wd=None, ws=None, yaw=None, tilt=None, # @ReservedAssignment
normalize_probabilities=False, with_wake_loss=True,
n_cpu=1, wd_chunks=None, ws_chunks=None, **kwargs):
wd, ws = self.site.get_defaults(wd, ws)
wd_bin_size = self.site.wd_bin_size(wd)
map_func, kwargs_lst, wd_chunks, ws_chunks = self._multiprocessing_chunks(
wd=wd, ws=ws, time=False, n_cpu=n_cpu, wd_chunks=wd_chunks, ws_chunks=ws_chunks,
x=x, y=y, h=h, type=type, yaw=yaw, tilt=tilt, **kwargs)
return np.sum([np.array(aep) / self.site.wd_bin_size(args['wd']) * wd_bin_size
for args, aep in zip(kwargs_lst, map_func(aep_function, kwargs_lst))], 0)
def aep_gradients(self, gradient_method=autograd, wrt_arg=['x', 'y'], gradient_method_kwargs={},
n_cpu=1, wd_chunks=None, ws_chunks=None, **kwargs):
"""Method to compute the gradients of the AEP with respect to wrt_arg using the gradient_method
Note, this method has two behaviours:
1) Without specifying additional key-word arguments, kwargs, the method returns the function to
compute the gradients of the aep:
gradient_function = wfm.aep_gradietns(autograd, ['x','y'])
gradients = gradient_function(x,y)
2) With additional key-word arguments, kwargs, the method returns the gradients of the aep:
gradients = wfm.aep_gradients(autograd,['x','y'],x=x,y=y)
Parameters
----------
gradient_method : gradient function, {fd, cs, autograd}
gradient function
wrt_arg : {'x', 'y', 'h', 'wd', 'ws', 'yaw','tilt'} or list of these arguments, e.g. ['x','y']
argument to compute gradients of AEP with respect to
gradient_method_kwargs : dict, optional
additional arguments for the gradient method, e.g. step size
n_cpu : int or None, optional
Number of CPUs to be used for execution.
If 1 (default), the execution is not parallized
If None, the available number of CPUs are used
wd_chunks : int or None, optional
If n_cpu>1, the wind directions are divided into <wd_chunks> chunks and executed in parallel.
If wd_chunks is None, wd_chunks is set to the available number of CPUs
ws_chunks : int or None, optional
If n_cpu>1, the wind speeds are divided into <ws_chunks> chunks and executed in parallel.
If ws_chunks is None, ws_chunks is set to 1
"""
if n_cpu != 1 or wd_chunks or ws_chunks:
return self._aep_chunk_wrapper(
self._aep_gradients_kwargs, gradient_method=gradient_method, wrt_arg=wrt_arg,
gradient_method_kwargs=gradient_method_kwargs,
n_cpu=n_cpu, wd_chunks=wd_chunks, ws_chunks=ws_chunks, **kwargs)
argnum = [['x', 'y', 'h', 'type', 'wd', 'ws', 'yaw', 'tilt'].index(a) for a in np.atleast_1d(wrt_arg)]
f = gradient_method(self.aep, True, argnum, **gradient_method_kwargs)
if kwargs:
return f(**kwargs)
else:
return f
def _aep_gradients_kwargs(self, kwargs):
return self.aep_gradients(**kwargs)
def _aep_kwargs(self, kwargs):
return self.aep(**kwargs)
class SimulationResult(xr.Dataset):
"""Simulation result returned when calling a WindFarmModel object"""
__slots__ = ('windFarmModel', 'localWind', 'wt_inputs')
def __init__(self, windFarmModel, localWind, type_i, yaw_ilk, tilt_ilk,
WS_eff_ilk, TI_eff_ilk, power_ilk, ct_ilk, wt_inputs):
self.windFarmModel = windFarmModel
lw = localWind
self.localWind = localWind
self.wt_inputs = wt_inputs
n_wt = len(lw.i)
coords = {k: (dep, v, {'Description': d}) for k, dep, v, d in [
('wt', 'wt', np.arange(n_wt), 'Wind turbine number'),
('wd', ('wd', 'time')['time' in lw], lw.wd, 'Ambient reference wind direction [deg]'),
('ws', ('ws', 'time')['time' in lw], lw.ws, 'Ambient reference wind speed [m/s]'),
('x', 'wt', lw.x, 'Wind turbine x coordinate [m]'),
('y', 'wt', lw.y, 'Wind turbine y coordinate [m]'),
('h', 'wt', lw.h, 'Wind turbine hub height [m]'),
('type', 'wt', type_i, 'Wind turbine type')]}
if 'time' in lw:
coords['time'] = lw.time
ilk_dims = (['wt', 'wd', 'ws'], ['wt', 'time'])['time' in lw]
xr.Dataset.__init__(self,
data_vars={k: (ilk_dims, (v, v[:, :, 0])['time' in lw], {'Description': d})
for k, v, d in [('WS_eff', WS_eff_ilk, 'Effective local wind speed [m/s]'),
('TI_eff', np.zeros_like(WS_eff_ilk) + TI_eff_ilk,
'Effective local turbulence intensity'),
('Power', power_ilk, 'Power [W]'),
('CT', ct_ilk, 'Thrust coefficient'),
]},
coords=coords)
for n in localWind:
if n[-4:] == '_ilk':
self[n[:-4]] = getattr(localWind, n[:-4])
elif n in ['ws_lower', 'ws_upper']:
v = localWind[n]
dims = [n for n, d in zip(('wt', 'wd', 'ws'), v.shape) if d > 1]
self[n[:-4]] = (dims, v.squeeze())
else:
self[n] = localWind[n]
# self.attrs.update(localWind.attrs)
for n in set(wt_inputs) - {'type', 'TI_eff', 'yaw'}:
if wt_inputs[n] is not None:
if '_ijl' in n:
self[n] = ijlk2da(wt_inputs[n], self.coords)
else:
self[n] = ilk2da(arg2ilk(n, wt_inputs[n], *coords2ILK(self.coords)), self.coords)
self['yaw'] = ilk2da(yaw_ilk, self.coords, 'Yaw misalignment [deg]')
self['tilt'] = ilk2da(tilt_ilk, self.coords, 'Rotor tilt [deg]')
# for backward compatibility
for k in ['WD', 'WS', 'TI', 'P', 'WS_eff', 'TI_eff']:
setattr(self.__class__, "%s_ilk" % k, property(lambda self, k=k: self[k].ilk()))
setattr(self.__class__, "ct_ilk", property(lambda self: self.CT.ilk()))
setattr(self.__class__, "power_ilk", property(lambda self: self.Power.ilk()))
def aep_ilk(self, normalize_probabilities=False, with_wake_loss=True):
"""Anual Energy Production of all turbines (i), wind directions (l) and wind speeds (k) in in GWh
Parameters
----------
normalize_propabilities : Optional bool, defaults to False
In case only a subset of all wind speeds and/or wind directions is simulated,
this parameter determines whether the returned AEP represents the energy produced in the fraction
of a year where these flow cases occur or a whole year of only these cases.
If for example, wd=[0], then
- False means that the AEP only includes energy from the faction of year\n
with northern wind (359.5-0.5deg), i.e. no power is produced the rest of the year.
- True means that the AEP represents a whole year of northen wind.
with_wake_loss : Optional bool, defaults to True
If True, wake loss is included, i.e. power is calculated using local effective wind speed\n
If False, wake loss is neglected, i.e. power is calculated using local free flow wind speed
"""
return self.aep(normalize_probabilities=normalize_probabilities, with_wake_loss=with_wake_loss).ilk()
def aep(self, normalize_probabilities=False, with_wake_loss=True,
hours_pr_year=24 * 365, linear_power_segments=False):
"""Anual Energy Production (sum of all wind turbines, directions and speeds) in GWh.
See aep_ilk
"""
if normalize_probabilities:
norm = self.P.ilk().sum((1, 2))[:, na, na]
else:
norm = 1
if with_wake_loss:
power_ilk = self.Power.ilk()
else:
wt_kwargs_keys = set(self.windFarmModel.windTurbines.powerCtFunction.required_inputs +
self.windFarmModel.windTurbines.powerCtFunction.optional_inputs)
power_ilk = self.windFarmModel.windTurbines.power(self.WS.ilk(
self.Power.ilk().shape), **{k: v for k, v in self.wt_inputs.items() if k in wt_kwargs_keys})
if linear_power_segments:
s = "The linear_power_segments method "
assert all([n in self for n in ['Weibull_A', 'Weibull_k', 'Sector_frequency']]),\
s + "requires a site with weibull information"
assert normalize_probabilities is False, \
s + "cannot be combined with normalize_probabilities"
assert np.all(self.Power.isel(ws=0) == 0) and np.all(self.Power.isel(ws=-1) == 0),\
s + "requires first wind speed to have no power (just below cut-in)"
assert np.all(self.Power.isel(ws=-1) == 0),\
s + "requires last wind speed to have no power (just above cut-out)"
weighted_power = weibull.WeightedPower(
self.ws.values,
self.Power.ilk(),
self.Weibull_A.ilk(),
self.Weibull_k.ilk())
aep = weighted_power * self.Sector_frequency.ilk() * hours_pr_year * 1e-9
ws = (self.ws.values[1:] + self.ws.values[:-1]) / 2
return xr.DataArray(aep, [('wt', self.wt.values), ('wd', self.wd.values), ('ws', ws)])
else:
weighted_power = power_ilk * self.P.ilk() / norm
if 'time' in self.dims and weighted_power.shape[2] == 1:
weighted_power = weighted_power[:, :, 0]
return xr.DataArray(weighted_power * hours_pr_year * 1e-9,
self.Power.coords,
name='AEP [GWh]',
attrs={'Description': 'Annual energy production [GWh]'})
def loads(self, method, lifetime_years=20, n_eq_lifetime=1e7, normalize_probabilities=False, softmax_base=None):
assert method in ['TwoWT', 'OneWT_WDAvg', 'OneWT']
wt = self.windFarmModel.windTurbines
P_ilk = self.P_ilk
if normalize_probabilities:
P_ilk /= P_ilk.sum((1, 2))[:, na, na]
WS_eff_ilk = self.WS_eff_ilk
TI_eff_ilk = self.TI_eff_ilk
kwargs = self.wt_inputs
if method == 'OneWT_WDAvg': # average over wd
p_wd_ilk = P_ilk.sum((0, 2))[na, :, na]
ws_ik = (WS_eff_ilk * p_wd_ilk).sum(1)
kwargs_ik = {k: (fix_shape(v, WS_eff_ilk) * p_wd_ilk).sum(1) for k, v in kwargs.items()
if k != 'TI_eff' and v is not None}
kwargs_ik.update({k: v for k, v in kwargs.items() if v is None})
loads, i_lst = [], []
m_lst = np.asarray(wt.loadFunction.wohler_exponents)
for m in np.unique(m_lst):
i = np.where(m_lst == m)[0]
if 'TI_eff' in kwargs:
kwargs_ik['TI_eff'] = ((p_wd_ilk * TI_eff_ilk ** m).sum(1)) ** (1 / m)
loads.extend(wt.loads(ws_ik, run_only=i, **kwargs_ik))
i_lst.extend(i)
loads = [loads[i] for i in np.argsort(i_lst)] # reorder
ds = xr.DataArray(
loads,
dims=['sensor', 'wt', 'ws'],
coords={'sensor': wt.loadFunction.output_keys,
'm': ('sensor', wt.loadFunction.wohler_exponents),
'wt': self.wt, 'ws': self.ws},
attrs={'description': '1Hz Damage Equivalent Load'}).to_dataset(name='DEL')
if 'wd' in self.P.dims:
ds['P'] = self.P.sum('wd')
else:
ds['P'] = self.P
t_flowcase = ds.P * lifetime_years * 365 * 24 * 3600
f = ds.DEL.mean() # factor used to reduce numerical errors in power
ds['LDEL'] = ((t_flowcase * (ds.DEL / f)**ds.m).sum('ws') / n_eq_lifetime)**(1 / ds.m) * f
ds.LDEL.attrs['description'] = "Lifetime (%d years) equivalent loads, n_eq_L=%d" % (
lifetime_years, n_eq_lifetime)
elif method == 'OneWT' or method == 'TwoWT':
if method == 'OneWT':
loads_silk = wt.loads(WS_eff_ilk, **kwargs)
else: # method == 'TwoWT':
I, L, K = WS_eff_ilk.shape
ws_iilk = np.broadcast_to(WS_eff_ilk[na], (I, I, L, K))
def _fix_shape(k, v):
if k[-3:] == 'ijl':
return fix_shape(v, ws_iilk)
else:
return np.broadcast_to(fix_shape(v, WS_eff_ilk)[na], (I, I, L, K))
kwargs_iilk = {k: _fix_shape(k, v)
for k, v in kwargs.items()
if k in wt.loadFunction.required_inputs + wt.loadFunction.optional_inputs}
loads_siilk = np.array(wt.loads(ws_iilk, **kwargs_iilk))
if softmax_base is None:
loads_silk = loads_siilk.max(1)
else:
# factor used to reduce numerical errors in power
f = loads_siilk.mean((1, 2, 3, 4)) / 10
loads_silk = (np.log((softmax_base**(loads_siilk / f[:, na, na, na, na])).sum(1)) /
np.log(softmax_base) * f[:, na, na, na])
if 'time' in self.dims:
ds = xr.DataArray(
np.array(loads_silk)[..., 0],
dims=['sensor', 'wt', 'time'],
coords={'sensor': wt.loadFunction.output_keys,
'm': ('sensor', wt.loadFunction.wohler_exponents, {'description': 'Wohler exponents'}),
'wt': self.wt, 'time': self.time, 'wd': self.wd, 'ws': self.ws},
attrs={'description': '1Hz Damage Equivalent Load'}).to_dataset(name='DEL')
else:
ds = xr.DataArray(
loads_silk,
dims=['sensor', 'wt', 'wd', 'ws'],
coords={'sensor': wt.loadFunction.output_keys,
'm': ('sensor', wt.loadFunction.wohler_exponents, {'description': 'Wohler exponents'}),
'wt': self.wt, 'wd': self.wd, 'ws': self.ws},
attrs={'description': '1Hz Damage Equivalent Load'}).to_dataset(name='DEL')
f = ds.DEL.mean() # factor used to reduce numerical errors in power
if 'time' in self.dims:
assert 'duration' in self, "Simulation must contain a dataarray 'duration' with length of time steps in seconds"
t_flowcase = self.duration
ds['LDEL'] = ((t_flowcase * (ds.DEL / f)**ds.m).sum(('time')) / n_eq_lifetime)**(1 / ds.m) * f
else:
ds['P'] = self.P
t_flowcase = ds.P * 3600 * 24 * 365 * lifetime_years
ds['LDEL'] = ((t_flowcase * (ds.DEL / f)**ds.m).sum(('wd', 'ws')) / n_eq_lifetime)**(1 / ds.m) * f
ds.LDEL.attrs['description'] = "Lifetime (%d years) equivalent loads, n_eq_L=%d" % (
lifetime_years, n_eq_lifetime)
return ds
def noise_model(self, noiseModel=ISONoiseModel):
WS_eff_ilk = self.WS_eff_ilk
freqs, sound_power_level = self.windFarmModel.windTurbines.sound_power_level(WS_eff_ilk, **self.wt_inputs)
return noiseModel(src_x=self.x.values, src_y=self.y.values, src_h=self.h.values,
freqs=freqs, sound_power_level=sound_power_level,
elevation_function=self.windFarmModel.site.elevation)
def noise_map(self, noiseModel=ISONoiseModel, grid=None, ground_type=0, temperature=20, relative_humidity=80):
if grid is None:
grid = HorizontalGrid(h=2)
nm = self.noise_model(noiseModel)
X, Y, x_j, y_j, h_j, plane = self._get_grid(grid)
spl_jlk, spl_jlkf = nm(x_j, y_j, h_j, temperature, relative_humidity, ground_type=ground_type)
return xr.Dataset({'Total sound pressure level': (('y', 'x', 'wd', 'ws'),
spl_jlk.reshape(X.shape + (spl_jlk.shape[1:]))),
'Sound pressure level': (('y', 'x', 'wd', 'ws', 'freq'),
spl_jlkf.reshape(X.shape + (spl_jlkf.shape[1:])))},
coords={'x': X[0], 'y': Y[:, 0], 'wd': self.wd, 'ws': self.ws, 'freq': nm.freqs})
def flow_box(self, x, y, h, wd=None, ws=None):
X, Y, H = np.meshgrid(x, y, h)
x_j, y_j, h_j = X.flatten(), Y.flatten(), H.flatten()
wd, ws = self._wd_ws(wd, ws)
lw_j, WS_eff_jlk, TI_eff_jlk = self.windFarmModel._flow_map(
x_j, y_j, h_j,
self.sel(wd=wd, ws=ws)
)
return FlowBox(self, X, Y, H, lw_j, WS_eff_jlk, TI_eff_jlk)
def _get_grid(self, grid):
if grid is None:
grid = HorizontalGrid()
if isinstance(grid, Grid):
plane = grid.plane
h = self.h.values
if len(h) == 0:
h = self.windFarmModel.windTurbines.hub_height()
grid = grid(x_i=self.x, y_i=self.y, h_i=h,
d_i=self.windFarmModel.windTurbines.diameter(self.type))
else:
plane = (None,)
return grid + (plane, )
def aep_map(self, grid=None, wd=None, ws=None, type=0, normalize_probabilities=False, n_cpu=1, wd_chunks=None):
X, Y, x_j, y_j, h_j, plane = self._get_grid(grid)
wd, ws = self._wd_ws(wd, ws)
sim_res = self.sel(wd=wd, ws=ws)
n_cpu = n_cpu or multiprocessing.cpu_count()
wd_chunks = np.minimum(wd_chunks or n_cpu, len(wd))
if n_cpu != 1:
n_cpu = n_cpu or multiprocessing.cpu_count()
map = get_pool_starmap(n_cpu) # @ReservedAssignment
if len(wd) >= n_cpu:
# chunkification more efficient on wd than j
wd_i = np.linspace(0, len(wd), n_cpu + 1).astype(int)
args_lst = [[x_j, y_j, h_j, type, sim_res.sel(wd=wd[i0:i1])] for i0, i1 in zip(wd_i[:-1], wd_i[1:])]
aep_lst = map(self.windFarmModel._aep_map, args_lst)
aep_j = np.sum(aep_lst, 0)
else:
j_i = np.linspace(0, len(x_j), n_cpu + 1).astype(int)
args_lst = [[xyh_j[i0:i1] for xyh_j in [x_j, y_j, h_j]] + [type, sim_res]
for i0, i1 in zip(j_i[:-1], j_i[1:])]
aep_lst = map(self.windFarmModel._aep_map, args_lst)
aep_j = np.concatenate(aep_lst)
else:
aep_j = self.windFarmModel._aep_map(x_j, y_j, h_j, type, sim_res)
if normalize_probabilities:
lw_j = self.windFarmModel.site.local_wind(x_i=x_j, y_i=y_j, h_i=h_j, wd=wd, ws=ws)
aep_j /= lw_j.P_ilk.sum((1, 2))
if plane[0] == 'XY':
coords = {'x': X[0], 'y': Y[:, 0]}
return xr.DataArray(aep_j.reshape(X.shape), name='AEP', attrs={
'units': 'GWh'}, coords=coords, dims=['y', 'x'])
elif plane[0] == 'xyz':
return xr.DataArray(aep_j, name='AEP', attrs={'units': 'GWh'}, coords={
'x': ('i', grid.x), 'y': ('i', grid.y)}, dims=['i'])
else: # pragma: no cover
raise NotImplementedError()
def flow_map(self, grid=None, wd=None, ws=None):
"""Return a FlowMap object with WS_eff and TI_eff of all grid points
Parameters
----------
grid : Grid or tuple(X, Y, x, y, h)
Grid, e.g. HorizontalGrid or\n
tuple(X, Y, x, y, h) where X, Y is the meshgrid for visualizing data\n
and x, y, h are the flattened grid points
See Also
--------
pywake.wind_farm_models.flow_map.FlowMap
"""
X, Y, x_j, y_j, h_j, plane = self._get_grid(grid)
wd, ws = self._wd_ws(wd, ws)
lw_j, WS_eff_jlk, TI_eff_jlk = self.windFarmModel._flow_map(x_j, y_j, h_j, self.sel(wd=wd, ws=ws))
return FlowMap(self, X, Y, lw_j, WS_eff_jlk, TI_eff_jlk, plane=plane)
def _wd_ws(self, wd, ws):
if wd is None:
wd = self.wd
else:
assert np.all(np.isin(wd, self.wd)), "All wd=%s not in simulation result" % wd
if ws is None:
ws = self.ws
else:
assert np.all(np.isin(ws, self.ws)), "All ws=%s not in simulation result (ws=%s)" % (ws, self.ws)
return np.atleast_1d(wd), np.atleast_1d(ws)
def save(self, filename):
self.to_netcdf(filename)
@staticmethod
def load(filename, wfm):
ds = xr.load_dataset(filename)
if 'time' in ds:
time = ds.time.data
else:
time = False
lw = LocalWind(ds.x.data, ds.y.data, ds.h.data, ds.wd.data, ds.ws.data, time,
wd_bin_size=ds['wd_bin_size'],
WD=ds.WD, WS=ds.WS, TI=ds.TI, P=ds.P)
sim_res = SimulationResult(wfm, lw, type_i=ds.type.values, yaw_ilk=ds.yaw.ilk(), tilt_ilk=ds.tilt.ilk(),
WS_eff_ilk=ds.WS_eff.ilk(), TI_eff_ilk=ds.TI_eff.ilk(), power_ilk=ds.Power.ilk(),
ct_ilk=ds.CT.ilk(),
wt_inputs={})
return sim_res
def main():
if __name__ == '__main__':
from py_wake.examples.data.iea37 import IEA37Site, IEA37_WindTurbines
from py_wake import IEA37SimpleBastankhahGaussian
import matplotlib.pyplot as plt
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wind_farm_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
simulation_result = wind_farm_model(x, y)
fm = simulation_result.flow_map(wd=30)
fm.plot_wake_map()
plt.figure()
fm.plot(fm.power_xylk().sum(['wd', 'ws']) * 1e-3, "Power [kW]")
fm = simulation_result.flow_map(grid=HorizontalGrid(resolution=50))
plt.figure()
fm.plot(fm.aep_xy(), "AEP [GWh]")
plt.show()
main()