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91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
from py_wake.wind_turbines import WindTurbine
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from py_wake.wind_turbines.power_ct_functions import PowerCtTabular
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from py_wake.site._site import UniformSite
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from py_wake.site.xrsite import XRSite
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from py_wake.wind_farm_models import PropagateDownwind
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from py_wake.deficit_models.gaussian import TurboGaussianDeficit
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from py_wake.flow_map import XYGrid
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import numpy as np
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import xarray as xr
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D = 126 #Diametro del wT
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h = 90 #Altura del WT
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U_ref = 8
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initial_position = np.array([[ 1 , 1],
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[ 2. , 2],
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[ 4 , 4],
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[ 1 , 4],
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[ 2 , 4],
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[ 3 , 4],
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[ 1 , 5],
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[ 2 , 5],
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[ 5 , 5]])
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def py_wake_Initial_Cong(D,name,h,U_ref,initial_position=initial_position):
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#Curvas del WT
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power_curve = np.array([
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[ 0.1, 1.000],
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[ 4.5, 267.7],
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[ 5.0, 387.6],
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[ 5.5, 534.0],
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[ 6.0, 707.4],
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[ 6.5, 910.0],
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[ 7.0, 1142.7],
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[ 7.5, 1407.5],
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[ 8.0, 1707.1],
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[ 8.5, 2047.3],
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[ 9.0, 2430.6]]) * [1, 1000]
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ct_curve = np.array([
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[ 0.1, 0.100],
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[ 4.5, 0.928],
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[ 5.0, 0.892],
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[ 5.5, 0.861],
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[ 6.0, 0.835],
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[ 6.5, 0.812],
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[ 7.0, 0.792],
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[ 7.5, 0.776],
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[ 8.0, 0.7702530978349217],
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[ 8.5, 0.762],
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[ 9.0, 0.763]])
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return WindTurbine(name=name,
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diameter=D,
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hub_height=h,
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powerCtFunction=PowerCtTabular(power_curve[:, 0],
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power_curve[:, 1],
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'w',ct_curve[:, 1],
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method='linear'))
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windTurbines = py_wake_Initial_Cong(D, 'NREL_5MW', h, U_ref)
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def run(direction=0, initial_position=initial_position, U_ref=U_ref):
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initial_position = np.array(initial_position);
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site = UniformSite(p_wd=[1] * 8,
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ws=U_ref,
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initial_position=initial_position*D)
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#ds = xr.Dataset(
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# data_vars={'P': ('wd', [1, 1, 1, 1])},
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# coords={'wd': [0, 90, 180, 270]})
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#ds['TI'] = 0.1
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#site = XRSite(ds, interp_method='nearest', initial_position=initial_position*D, default_ws=np.atleast_1d(U_ref))
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wt_x, wt_y = site.initial_position.T/D
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wfm = PropagateDownwind(site, windTurbines, wake_deficitModel=TurboGaussianDeficit())
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grid = XYGrid(x=np.arange(0, 10.01, 0.1)*126, y=np.arange(0, 10.01, 0.1)*126)
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xa = wfm(x=wt_x*126, y=wt_y*126, wd=direction, yaw=0).flow_map(grid)
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ws = xa.WS_eff
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return ws[:,:,0,0].values[:,:,0].tolist()
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