I wanna optimize the following class using the Scipy and got this error: RecursionError: maximum recursion depth exceeded
the fact is that if I reduce wd and ws I can get the results while for higher numbers it is not possible. Indeed I need to work with larger numbers so this problem must be solved. regarding the code, I want to optimize the class for different wd and ws by using for loop; in a better way for each value of [l][k], the class output will be maximized (which is optimization function in class). As I found the problem is related to recursion but I cannot understand where it happened?
import numpy as np
import pandas as pd
from py_wake.examples.data.hornsrev1 import V80
from py_wake.examples.data.hornsrev1 import Hornsrev1Site # We work with the Horns Rev 1 site, which comes already set up with PyWake.
from py_wake import BastankhahGaussian
from py_wake.turbulence_models import GCLTurbulence
from py_wake.deflection_models.jimenez import JimenezWakeDeflection
from scipy.optimize import minimize
from py_wake.wind_turbines.power_ct_functions import PowerCtFunctionList, PowerCtTabular
#-----------------------------------------
class optimization():
def __init__(self,wt,l,k,site,windturbine,wfModel,deflectionModel,turbulencemodel):
self.wt=wt
self.l=l
self.k=k
self.site=site
self.windTurbines=windturbine
self.wfModel=wfModel
self.deflectionModel=deflectionModel
self.turbulenceModel=turbulencemodel
#--------------------------------------------------
def newSite(self):
x, y = self.site.initial_position.T
xNew=np.array([x[0]+560*i for i in range(3)])
yNew=np.array([y[0]+560*i for i in range(3)])
x_newsite=np.array([xNew[0],xNew[0],xNew[0],xNew[1],xNew[1],xNew[1],xNew[2],xNew[2],xNew[2]])
y_newsite=np.array([yNew[0],yNew[1],yNew[2],yNew[0],yNew[1],yNew[2],yNew[0],yNew[1],yNew[2]])
return(x_newsite[:(self.wt)],y_newsite[:(self.wt)])
#-----------------------------------------------------
def objective(self,x0) :
x_newsite, y_newsite=self.newSite()
self.windTurbines.powerCtFunction = PowerCtFunctionList(
key='operating',
powerCtFunction_lst=[PowerCtTabular(ws=[0, 100], power=[0, 0], power_unit='w', ct=[0, 0]), # 0=No power and ct
self.windTurbines.powerCtFunction],default_value=1) # 1=Normal operation
power=0
if (all(x0 <= 0.5)==True):
power=0
else:
operating = np.ones((self.wt))
operating[x0 <= 0.5]=0
wf_model = self.wfModel(self.site, self.windTurbines, deflectionModel= self.deflectionModel,turbulenceModel= self.turbulenceModel)
# run wind farm simulation
sim_res = wf_model(
x_newsite, y_newsite, # wind turbine positions
h=None, # wind turbine heights (defaults to the heights defined in windTurbines)
wd=self.l, # Wind direction (defaults to site.default_wd (0,1,...,360 if not overriden))
ws=self.k, # Wind speed (defaults to site.default_ws (3,4,...,25m/s if not overriden))
operating=operating)
power=np.sum(sim_res.Power)
print(power)
return (float(-power))
#----------------------------------------------------------------
def optimization(self):
x0 = np.full((wt),0.5)
bounds=np.full((wt,2),(0,1)).reshape(-1, 2)
res= minimize(self.objective, x0=x0, bounds=bounds)
return(res)
# =============================================================================
# Run the Optimization
# =============================================================================
wt =9
wd=np.arange(0,10,5)
ws=np.arange(4,10)
site=Hornsrev1Site()
windTurbines=V80()
wf_model=BastankhahGaussian
deflection_model=JimenezWakeDeflection()
turbulence_model=GCLTurbulence()
C_result=np.zeros((len(wd),len(ws),wt))
power=np.zeros((len(wd),len(ws),1))
status=np.zeros((len(wd),len(ws),1))
for l in range(len(wd)):
for k in range(len(ws)):
opt=optimization(wt,wd[l],ws[k],site,windTurbines,wf_model,deflection_model,turbulence_model)
res=opt.optimization()
C_result[l][k]=res.x
power[l][k]=-res.fun
status[l][k]=res.success
