from numpy import matlib
import statsmodels.api as sm
import GridSens.tools.ARMAmodel as ARMA
mat = loadmat('MatpowerPQ.mat')
loadsP15to25 = mat['loadsP15to25'] # Active power
loadsQ15to25 = mat['loadsQ15to25'] # Reactive power
genP19 = mat['genP19']
genQ19 = mat['genQ19']
mat1 = loadmat('NLO_data.mat')
Y = mat1['Y']
fname = "Net1_UKGDS_60_subgrid.m"
fnameWhole = "Net1_UKGDS_60.m"
convert_mcase(fname)
from Net1_UKGDS_60_subgrid import Net1_UKGDS_60_ as net
from Net1_UKGDS_60 import Net1_UKGDS_60_ as netWhole
t_f = 96
# time variation
t_ges = 1440 # all time in min
delta_t = 15 # time intervals in min
time = np.arange(delta_t, t_ges + delta_t, delta_t)
casedataWhole = netWhole()
convert_to_python_indices(casedataWhole)
ppc = casedataWhole
ppopt = ppoption(PF_ALG=2)
ppc = ext2int(ppc)
baseMVA, bus, gen, branch = ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc[
from GridSens.tools.load import convert_mcase, convert_to_python_indices
import GridSens.tools.profilesPV as ProfilePV
################# Load grid ########################
mat = loadmat('MatpowerPQ.mat')
loadsP15to25 = mat['loadsP15to25'] # Active power
loadsQ15to25 = mat['loadsQ15to25'] # Reactive power
genP19 = mat['genP19']
genQ19 = mat['genQ19']
mat1 = loadmat('NLO_data.mat')
Y=mat1['Y']
fname = "Net1_UKGDS_60_subgrid.m"
fnameWhole = "Net1_UKGDS_60.m"
convert_mcase(fname)
convert_mcase(fnameWhole)
from Net1_UKGDS_60_subgrid import Net1_UKGDS_60_ as net
from Net1_UKGDS_60 import Net1_UKGDS_60_ as netWhole
t_f = 96 # time variation
t_ges = 1440 # all time in min
delta_t = 15 # time intervals in min
time= np.arange(delta_t, t_ges+delta_t,delta_t)
casedataWhole = netWhole()
convert_to_python_indices(casedataWhole)
ppc = casedataWhole
ppopt = ppoption(PF_ALG=2)
ppc = ext2int(ppc)
baseMVA, bus, gen, branch = ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]