def get_index_loading_cdf(self, max_val=1.0):
"""
Find the elements where the CDF is greater or equal to a value
:param max_val: value to compare
:return: indices, associated probability
"""
# turn the loading real values into CDF
cdf = CDF(np.abs(self.Sbr_points.real))
n = cdf.arr.shape[1]
idx = list()
val = list()
prob = list()
for i in range(n):
# Find the indices that surpass max_val
many_idx = np.where(cdf.arr[:, i] > max_val)[0]
# if there are indices, pick the first; store it and its associated probability
if len(many_idx) > 0:
idx.append(i)
val.append(cdf.arr[many_idx[0], i])
prob.append(
1 - cdf.prob[many_idx[0]]
) # the CDF stores the chance of beign leq than the value, hence the overload is the complementary
return idx, val, prob, cdf.arr[-1, :]
def make_monte_carlo_input(numerical_input_island: CalculationInputs):
"""
Generate a monte carlo input instance
:param numerical_input_island:
:return:
"""
n = numerical_input_island.nbus
Scdf = [None] * n
Icdf = [None] * n
Ycdf = [None] * n
for i in range(n):
Scdf[i] = CDF(numerical_input_island.Sbus_prof[i, :])
Icdf[i] = CDF(numerical_input_island.Ibus_prof[i, :])
Ycdf[i] = CDF(numerical_input_island.Ysh_prof[i, :])
return MonteCarloInput(n, Scdf, Icdf, Ycdf)
def make_monte_carlo_input(numerical_input_island: TimeCircuit):
"""
Generate a monte carlo input instance
:param numerical_input_island:
:return:
"""
n = numerical_input_island.nbus
Scdf = [None] * n
Icdf = [None] * n
Ycdf = [None] * n
for i in range(n):
Scdf[i] = CDF(numerical_input_island.Sbus[i, :])
Icdf[i] = CDF(numerical_input_island.Ibus[i, :])
Ycdf[i] = CDF(numerical_input_island.Yshunt_from_devices[i, :])
return StochasticPowerFlowInput(n, Scdf, Icdf, Ycdf)
def mdl(self, result_type: ResultTypes) -> "ResultsModel":
"""
Plot the results
:param result_type:
:param ax:
:param indices:
:param names:
:return:
"""
cdf_result_types = [
ResultTypes.BusVoltageCDF, ResultTypes.BusPowerCDF,
ResultTypes.BranchPowerCDF, ResultTypes.BranchLoadingCDF,
ResultTypes.BranchLossesCDF
]
if result_type == ResultTypes.BusVoltageAverage:
labels = self.bus_names
y = self.v_avg_conv[1:-1, :]
y_label = '(p.u.)'
x_label = 'Sampling points'
title = 'Bus voltage \naverage convergence'
elif result_type == ResultTypes.BranchPowerAverage:
labels = self.branch_names
y = self.s_avg_conv[1:-1, :]
y_label = '(MW)'
x_label = 'Sampling points'
title = 'Branch power \naverage convergence'
elif result_type == ResultTypes.BranchLoadingAverage:
labels = self.branch_names
y = self.l_avg_conv[1:-1, :]
y_label = '(%)'
x_label = 'Sampling points'
title = 'Branch loading \naverage convergence'
elif result_type == ResultTypes.BranchLossesAverage:
labels = self.branch_names
y = self.loss_avg_conv[1:-1, :]
y_label = '(MVA)'
x_label = 'Sampling points'
title = 'Branch losses \naverage convergence'
elif result_type == ResultTypes.BusVoltageStd:
labels = self.bus_names
y = self.v_std_conv[1:-1, :]
y_label = '(p.u.)'
x_label = 'Sampling points'
title = 'Bus voltage standard \ndeviation convergence'
elif result_type == ResultTypes.BranchPowerStd:
labels = self.branch_names
y = self.s_std_conv[1:-1, :]
y_label = '(MW)'
x_label = 'Sampling points'
title = 'Branch power standard \ndeviation convergence'
elif result_type == ResultTypes.BranchLoadingStd:
labels = self.branch_names
y = self.l_std_conv[1:-1, :]
y_label = '(%)'
x_label = 'Sampling points'
title = 'Branch loading standard \ndeviation convergence'
elif result_type == ResultTypes.BranchLossesStd:
labels = self.branch_names
y = self.loss_std_conv[1:-1, :]
y_label = '(MVA)'
x_label = 'Sampling points'
title = 'Branch losses standard \ndeviation convergence'
elif result_type == ResultTypes.BusVoltageCDF:
labels = self.bus_names
cdf = CDF(np.abs(self.V_points))
y_label = '(p.u.)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BranchLoadingCDF:
labels = self.branch_names
cdf = CDF(np.abs(self.loading_points.real))
y_label = '(p.u.)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BranchLossesCDF:
labels = self.branch_names
cdf = CDF(np.abs(self.losses_points))
y_label = '(MVA)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BranchPowerCDF:
labels = self.branch_names
cdf = CDF(self.Sbr_points.real)
y_label = '(MW)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BusPowerCDF:
labels = self.bus_names
cdf = CDF(self.S_points.real)
y_label = '(p.u.)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
else:
x_label = ''
y_label = ''
title = ''
if result_type not in cdf_result_types:
# assemble model
index = np.arange(0, y.shape[0], 1)
mdl = ResultsModel(data=np.abs(y),
index=index,
columns=labels,
title=title,
ylabel=y_label,
xlabel=x_label,
units=y_label)
else:
mdl = ResultsModel(data=cdf.arr,
index=cdf.prob,
columns=labels,
title=title,
ylabel=y_label,
xlabel=x_label,
units=y_label)
return mdl
def plot(self,
result_type: ResultTypes,
ax=None,
indices=None,
names=None):
"""
Plot the results
:param result_type:
:param ax:
:param indices:
:param names:
:return:
"""
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
p, n = self.V_points.shape
cdf_result_types = [
ResultTypes.BusVoltageCDF, ResultTypes.BusPowerCDF,
ResultTypes.BranchCurrentCDF, ResultTypes.BranchLoadingCDF,
ResultTypes.BranchLossesCDF
]
if indices is None:
if names is None:
indices = np.arange(0, n, 1)
labels = None
else:
indices = np.array(range(len(names)))
labels = names[indices]
else:
labels = names[indices]
if len(indices) > 0:
y_label = ''
title = ''
if result_type == ResultTypes.BusVoltageAverage:
y = self.v_avg_conv[1:-1, indices]
y_label = '(p.u.)'
x_label = 'Sampling points'
title = 'Bus voltage \naverage convergence'
elif result_type == ResultTypes.BranchCurrentAverage:
y = self.c_avg_conv[1:-1, indices]
y_label = '(p.u.)'
x_label = 'Sampling points'
title = 'Bus current \naverage convergence'
elif result_type == ResultTypes.BranchLoadingAverage:
y = self.l_avg_conv[1:-1, indices]
y_label = '(%)'
x_label = 'Sampling points'
title = 'Branch loading \naverage convergence'
elif result_type == ResultTypes.BranchLossesAverage:
y = self.loss_avg_conv[1:-1, indices]
y_label = '(MVA)'
x_label = 'Sampling points'
title = 'Branch losses \naverage convergence'
elif result_type == ResultTypes.BusVoltageStd:
y = self.v_std_conv[1:-1, indices]
y_label = '(p.u.)'
x_label = 'Sampling points'
title = 'Bus voltage standard \ndeviation convergence'
elif result_type == ResultTypes.BranchCurrentStd:
y = self.c_std_conv[1:-1, indices]
y_label = '(p.u.)'
x_label = 'Sampling points'
title = 'Bus current standard \ndeviation convergence'
elif result_type == ResultTypes.BranchLoadingStd:
y = self.l_std_conv[1:-1, indices]
y_label = '(%)'
x_label = 'Sampling points'
title = 'Branch loading standard \ndeviation convergence'
elif result_type == ResultTypes.BranchLossesStd:
y = self.loss_std_conv[1:-1, indices]
y_label = '(MVA)'
x_label = 'Sampling points'
title = 'Branch losses standard \ndeviation convergence'
elif result_type == ResultTypes.BusVoltageCDF:
cdf = CDF(np.abs(self.V_points[:, indices]))
cdf.plot(ax=ax)
y_label = '(p.u.)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BranchLoadingCDF:
cdf = CDF(np.abs(self.loading_points.real[:, indices]))
cdf.plot(ax=ax)
y_label = '(p.u.)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BranchLossesCDF:
cdf = CDF(np.abs(self.losses_points[:, indices]))
cdf.plot(ax=ax)
y_label = '(MVA)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BranchCurrentCDF:
cdf = CDF(np.abs(self.I_points[:, indices]))
cdf.plot(ax=ax)
y_label = '(kA)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BusPowerCDF:
cdf = CDF(np.abs(self.S_points[:, indices]))
cdf.plot(ax=ax)
y_label = '(p.u.)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
else:
x_label = ''
y_label = ''
title = ''
if result_type not in cdf_result_types:
df = pd.DataFrame(data=np.abs(y), columns=labels)
lines = ax.plot(np.abs(y), linewidth=LINEWIDTH)
if len(df.columns) < 10:
ax.legend(lines, labels, loc='best')
else:
df = pd.DataFrame(index=cdf.prob, data=cdf.arr, columns=labels)
ax.set_title(title)
ax.set_ylabel(y_label)
ax.set_xlabel(x_label)
return df
else:
return None
def mdl(self,
result_type: ResultTypes,
indices=None,
names=None) -> "ResultsModel":
"""
Plot the results
:param result_type:
:param ax:
:param indices:
:param names:
:return:
"""
p, n = self.V_points.shape
cdf_result_types = [
ResultTypes.BusVoltageCDF, ResultTypes.BusPowerCDF,
ResultTypes.BranchCurrentCDF, ResultTypes.BranchLoadingCDF,
ResultTypes.BranchLossesCDF
]
if indices is None:
if names is None:
indices = np.arange(0, n, 1)
labels = None
else:
indices = np.array(range(len(names)))
labels = names[indices]
else:
labels = names[indices]
if len(indices) > 0:
y_label = ''
title = ''
if result_type == ResultTypes.BusVoltageAverage:
y = self.v_avg_conv[1:-1, indices]
y_label = '(p.u.)'
x_label = 'Sampling points'
title = 'Bus voltage \naverage convergence'
elif result_type == ResultTypes.BranchCurrentAverage:
y = self.c_avg_conv[1:-1, indices]
y_label = '(p.u.)'
x_label = 'Sampling points'
title = 'Bus current \naverage convergence'
elif result_type == ResultTypes.BranchLoadingAverage:
y = self.l_avg_conv[1:-1, indices]
y_label = '(%)'
x_label = 'Sampling points'
title = 'Branch loading \naverage convergence'
elif result_type == ResultTypes.BranchLossesAverage:
y = self.loss_avg_conv[1:-1, indices]
y_label = '(MVA)'
x_label = 'Sampling points'
title = 'Branch losses \naverage convergence'
elif result_type == ResultTypes.BusVoltageStd:
y = self.v_std_conv[1:-1, indices]
y_label = '(p.u.)'
x_label = 'Sampling points'
title = 'Bus voltage standard \ndeviation convergence'
elif result_type == ResultTypes.BranchCurrentStd:
y = self.c_std_conv[1:-1, indices]
y_label = '(p.u.)'
x_label = 'Sampling points'
title = 'Bus current standard \ndeviation convergence'
elif result_type == ResultTypes.BranchLoadingStd:
y = self.l_std_conv[1:-1, indices]
y_label = '(%)'
x_label = 'Sampling points'
title = 'Branch loading standard \ndeviation convergence'
elif result_type == ResultTypes.BranchLossesStd:
y = self.loss_std_conv[1:-1, indices]
y_label = '(MVA)'
x_label = 'Sampling points'
title = 'Branch losses standard \ndeviation convergence'
elif result_type == ResultTypes.BusVoltageCDF:
cdf = CDF(np.abs(self.V_points[:, indices]))
# cdf.plot(ax=ax)
y_label = '(p.u.)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BranchLoadingCDF:
cdf = CDF(np.abs(self.loading_points.real[:, indices]))
# cdf.plot(ax=ax)
y_label = '(p.u.)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BranchLossesCDF:
cdf = CDF(np.abs(self.losses_points[:, indices]))
# cdf.plot(ax=ax)
y_label = '(MVA)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BranchCurrentCDF:
cdf = CDF(np.abs(self.I_points[:, indices]))
# cdf.plot(ax=ax)
y_label = '(kA)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
elif result_type == ResultTypes.BusPowerCDF:
cdf = CDF(np.abs(self.S_points[:, indices]))
# cdf.plot(ax=ax)
y_label = '(p.u.)'
x_label = 'Probability $P(X \leq x)$'
title = result_type.value[0]
else:
x_label = ''
y_label = ''
title = ''
if result_type not in cdf_result_types:
# assemble model
index = np.arange(0, y.shape[0], 1)
mdl = ResultsModel(data=np.abs(y),
index=index,
columns=labels,
title=title,
ylabel=y_label,
xlabel=x_label)
else:
mdl = ResultsModel(data=cdf.arr,
index=cdf.prob,
columns=labels,
title=title,
ylabel=y_label,
xlabel=x_label)
return mdl
else:
return None