def wt_init(self, wt, wt_type):
self.name = wt['name']
self.turbine_type = wt['turbine_type']
self.position = wt['position']
self.type = wt_type['name']
self.H = wt_type['hub_height']
self.R = wt_type['rotor_diameter'] / 2.0
if 'c_t_idle' in wt:
self.CT_idle = wt_type['c_t_idle']
else:
self.CT_idle = 0.056
self.power_factor = 1000.0 # <- Juan Pablo is using W as a basis to define power
self.pc = np.array(wt_type['power_curve'])
self.ctc = np.array(wt_type['c_t_curve'])
self.u_cutin = wt_type['cut_in_wind_speed']
self.u_cutout = wt_type['cut_out_wind_speed']
self.P_rated = wt_type['rated_power'] * self.power_factor
self.PCI = interpolator(self.pc[:,0], self.pc[:,1]*self.power_factor)
self.CTCI = interpolator(self.ctc[:,0], self.ctc[:,1])
index = np.nonzero(self.pc[:,1]*self.power_factor==self.P_rated)[0][0]
self.PCI_u = interpolator(self.pc[:index+1,1] * self.power_factor, self.pc[:index+1,0])
self.u_rated = wt_type['rated_wind_speed']
self.refCurvesArray = np.vstack([self.pc[:,0].T,
self.pc[:,1].T*self.power_factor,
self.CTCI(self.pc[:,0].T)]).T
def wt_init(self, wt, wt_type):
self.name = wt['name']
self.turbine_type = wt['turbine_type']
self.position = wt['position']
self.type = wt_type['name']
self.H = wt_type['hub_height']
self.R = wt_type['rotor_diameter'] / 2.0
if 'c_t_idle' in wt:
self.CT_idle = wt_type['c_t_idle']
else:
self.CT_idle = 0.056
self.power_factor = 1000.0 # <- Juan Pablo is using W as a basis to define power
self.pc = np.array(wt_type['power_curve'])
self.ctc = np.array(wt_type['c_t_curve'])
self.u_cutin = wt_type['cut_in_wind_speed']
self.u_cutout = wt_type['cut_out_wind_speed']
self.P_rated = wt_type['rated_power'] * self.power_factor
self.PCI = interpolator(self.pc[:, 0],
self.pc[:, 1] * self.power_factor)
self.CTCI = interpolator(self.ctc[:, 0], self.ctc[:, 1])
index = np.nonzero(self.pc[:, 1] *
self.power_factor == self.P_rated)[0][0]
self.PCI_u = interpolator(self.pc[:index + 1, 1] * self.power_factor,
self.pc[:index + 1, 0])
self.u_rated = wt_type['rated_wind_speed']
self.refCurvesArray = np.vstack([
self.pc[:, 0].T, self.pc[:, 1].T * self.power_factor,
self.CTCI(self.pc[:, 0].T)
]).T
def wt_init(self):
self.ref_u = self.refCurvesArray[:, 0]
self.ref_P = self.refCurvesArray[:, 1]
self.ref_CT = self.refCurvesArray[:, 2]
self.u_cutin = self.ref_u[0]
self.u_cutout = self.ref_u[-1]
self.P_rated = np.max(self.ref_P)
self.PCI = interpolator(self.ref_u, self.ref_P)
self.CTCI = interpolator(self.ref_u, self.ref_CT)
index = np.nonzero(self.ref_P == self.P_rated)[0][0]
self.PCI_u = interpolator(self.ref_P[:index + 1],
self.ref_u[:index + 1])
self.u_rated = np.float(self.PCI_u(self.P_rated))
def wt_init(self):
self.ref_u = self.refCurvesArray[:,0]
self.ref_P = self.refCurvesArray[:,1]
self.ref_CT = self.refCurvesArray[:,2]
self.u_cutin = self.ref_u[0]
self.u_cutout = self.ref_u[-1]
self.P_rated = np.max(self.ref_P)
self.PCI = interpolator(self.ref_u, self.ref_P)
self.CTCI = interpolator(self.ref_u, self.ref_CT)
index = np.nonzero(self.ref_P==self.P_rated)[0][0]
self.PCI_u = interpolator(
self.ref_P[:index+1],self.ref_u[:index+1])
self.u_rated = np.float(self.PCI_u(self.P_rated))
def interp(φ, Γ, ib, il):
φn = np.linspace(0, 2 * π, 200)
vals = Γ[:, ib, il, :]
Γip = np.zeros([φn.size, 3])
for i in range(3):
spl = interpolator(φ, vals[:, i])
spl.set_smoothing_factor(0.5)
Γip[:, i] = spl(φn)
return φn, Γip
def interpolate(self, table):
sigmas = np.loadtxt(table, dtype=np.double, usecols=(0,))
inds = np.loadtxt(table, dtype=np.double, usecols=np.arange(1,51, 2)).T
corrs = np.loadtxt(table, dtype=np.double,usecols=np.arange(2,51, 2)).T
self.fs = []
self.indlims = []
for i, (idx, corr) in enumerate(zip(inds, corrs)):
f = interpolator(np.column_stack((sigmas, idx)), corr)
self.fs.append(f)
self.indlims.append([idx.min(), idx.max()])
self.siglims = [sigmas.min(), sigmas.max()]
return
def interpolate(self, table):
sigmas = np.loadtxt(table, dtype=np.double, usecols=(0, ))
inds = np.loadtxt(table, dtype=np.double, usecols=np.arange(1, 51,
2)).T
corrs = np.loadtxt(table, dtype=np.double, usecols=np.arange(2, 51,
2)).T
self.fs = []
self.indlims = []
for i, (idx, corr) in enumerate(zip(inds, corrs)):
f = interpolator(np.column_stack((sigmas, idx)), corr)
self.fs.append(f)
self.indlims.append([idx.min(), idx.max()])
self.siglims = [sigmas.min(), sigmas.max()]
return
def interpolate(self, model_table):
modeldata = np.loadtxt(model_table, dtype=np.double)
self.model = interpolator(modeldata[:, :3], modeldata[:, 3:])
def interpolate(self, model_table):
modeldata = np.loadtxt(model_table, dtype=np.double)
self.model = interpolator(modeldata[:, :3], modeldata[:, 3:])
