def estimate(self,
v_start=None,
delta_start=None,
calculate_voltage_angles=True):
"""
The function estimate is the main function of the module. It takes up to three input
arguments: v_start, delta_start and calculate_voltage_angles. The first two are the initial
state variables for the estimation process. Usually they can be initialized in a
"flat-start" condition: All voltages being 1.0 pu and all voltage angles being 0 degrees.
In this case, the parameters can be left at their default values (None). If the estimation
is applied continuously, using the results from the last estimation as the starting
condition for the current estimation can decrease the amount of iterations needed to
estimate the current state. The third parameter defines whether all voltage angles are
calculated absolutely, including phase shifts from transformers. If only the relative
differences between buses are required, this parameter can be set to False. Returned is a
boolean value, which is true after a successful estimation and false otherwise.
The resulting complex voltage will be written into the pandapower network. The result
fields are found res_bus_est of the pandapower network.
INPUT:
**net** - The net within this line should be created
**v_start** (np.array, shape=(1,), optional) - Vector with initial values for all
voltage magnitudes in p.u. (sorted by bus index)
**delta_start** (np.array, shape=(1,), optional) - Vector with initial values for all
voltage angles in degrees (sorted by bus index)
OPTIONAL:
**calculate_voltage_angles** - (bool) - Take into account absolute voltage angles and
phase shifts in transformers Default is True.
OUTPUT:
**successful** (boolean) - True if the estimation process was successful
Optional estimation variables:
The bus power injections can be accessed with *se.s_node_powers* and the estimated
values corresponding to the (noisy) measurement values with *se.hx*. (*hx* denotes h(x))
EXAMPLE:
success = estimate(np.array([1.0, 1.0, 1.0]), np.array([0.0, 0.0, 0.0]))
"""
if self.net is None:
raise UserWarning("Component was not initialized with a network.")
# add initial values for V and delta
# node voltages
# V<delta
if v_start is None:
v_start = np.ones(self.net.bus.shape[0])
if delta_start is None:
delta_start = np.zeros(self.net.bus.shape[0])
# initialize the ppc bus with the initial values given
vm_backup, va_backup = self.net.res_bus.vm_pu.copy(
), self.net.res_bus.va_degree.copy()
self.net.res_bus.vm_pu = v_start
self.net.res_bus.vm_pu[self.net.bus.index[self.net.bus.in_service ==
False]] = np.nan
self.net.res_bus.va_degree = delta_start
# select elements in service and convert pandapower ppc to ppc
self.net._options = {}
_add_ppc_options(self.net,
check_connectivity=False,
init="results",
trafo_model="t",
copy_constraints_to_ppc=False,
mode="pf",
enforce_q_lims=False,
calculate_voltage_angles=calculate_voltage_angles,
r_switch=0.0,
recycle=dict(_is_elements=False,
ppc=False,
Ybus=False))
self.net["_is_elements"] = _select_is_elements(self.net)
ppc, _ = _pd2ppc(self.net)
mapping_table = self.net["_pd2ppc_lookups"]["bus"]
br_cols = ppc["branch"].shape[1]
bs_cols = ppc["bus"].shape[1]
self.net.res_bus.vm_pu = vm_backup
self.net.res_bus.va_degree = va_backup
# add 6 columns to ppc[bus] for Vm, Vm std dev, P, P std dev, Q, Q std dev
bus_append = np.full((ppc["bus"].shape[0], 6),
np.nan,
dtype=ppc["bus"].dtype)
v_measurements = self.net.measurement[
(self.net.measurement.type == "v")
& (self.net.measurement.element_type == "bus")]
if len(v_measurements):
bus_positions = mapping_table[v_measurements.bus.values.astype(
int)]
bus_append[bus_positions, 0] = v_measurements.value.values
bus_append[bus_positions, 1] = v_measurements.std_dev.values
p_measurements = self.net.measurement[
(self.net.measurement.type == "p")
& (self.net.measurement.element_type == "bus")]
if len(p_measurements):
bus_positions = mapping_table[p_measurements.bus.values.astype(
int)]
bus_append[bus_positions,
2] = p_measurements.value.values * 1e3 / self.s_ref
bus_append[bus_positions,
3] = p_measurements.std_dev.values * 1e3 / self.s_ref
q_measurements = self.net.measurement[
(self.net.measurement.type == "q")
& (self.net.measurement.element_type == "bus")]
if len(q_measurements):
bus_positions = mapping_table[q_measurements.bus.values.astype(
int)]
bus_append[bus_positions,
4] = q_measurements.value.values * 1e3 / self.s_ref
bus_append[bus_positions,
5] = q_measurements.std_dev.values * 1e3 / self.s_ref
# add virtual measurements for artificial buses, which were created because
# of an open line switch. p/q are 0. and std dev is 1. (small value)
new_in_line_buses = np.setdiff1d(np.arange(ppc["bus"].shape[0]),
mapping_table[mapping_table >= 0])
bus_append[new_in_line_buses, 2] = 0.
bus_append[new_in_line_buses, 3] = 1.
bus_append[new_in_line_buses, 4] = 0.
bus_append[new_in_line_buses, 5] = 1.
# add 12 columns to mpc[branch] for Im_from, Im_from std dev, Im_to, Im_to std dev,
# P_from, P_from std dev, P_to, P_to std dev, Q_from,Q_from std dev, Q_to, Q_to std dev
branch_append = np.full((ppc["branch"].shape[0], 12),
np.nan,
dtype=ppc["branch"].dtype)
i_measurements = self.net.measurement[
(self.net.measurement.type == "i")
& (self.net.measurement.element_type == "line")]
if len(i_measurements):
meas_from = i_measurements[(i_measurements.bus.values.astype(
int) == self.net.line.from_bus[i_measurements.element]).values]
meas_to = i_measurements[(i_measurements.bus.values.astype(
int) == self.net.line.to_bus[i_measurements.element]).values]
ix_from = meas_from.element.values.astype(int)
ix_to = meas_to.element.values.astype(int)
i_a_to_pu_from = (self.net.bus.vn_kv[meas_from.bus] * 1e3 /
self.s_ref).values
i_a_to_pu_to = (self.net.bus.vn_kv[meas_to.bus] * 1e3 /
self.s_ref).values
branch_append[ix_from, 0] = meas_from.value.values * i_a_to_pu_from
branch_append[ix_from,
1] = meas_from.std_dev.values * i_a_to_pu_from
branch_append[ix_to, 2] = meas_to.value.values * i_a_to_pu_to
branch_append[ix_to, 3] = meas_to.std_dev.values * i_a_to_pu_to
p_measurements = self.net.measurement[
(self.net.measurement.type == "p")
& (self.net.measurement.element_type == "line")]
if len(p_measurements):
meas_from = p_measurements[(p_measurements.bus.values.astype(
int) == self.net.line.from_bus[p_measurements.element]).values]
meas_to = p_measurements[(p_measurements.bus.values.astype(
int) == self.net.line.to_bus[p_measurements.element]).values]
ix_from = meas_from.element.values.astype(int)
ix_to = meas_to.element.values.astype(int)
branch_append[ix_from,
4] = meas_from.value.values * 1e3 / self.s_ref
branch_append[ix_from,
5] = meas_from.std_dev.values * 1e3 / self.s_ref
branch_append[ix_to, 6] = meas_to.value.values * 1e3 / self.s_ref
branch_append[ix_to, 7] = meas_to.std_dev.values * 1e3 / self.s_ref
q_measurements = self.net.measurement[
(self.net.measurement.type == "q")
& (self.net.measurement.element_type == "line")]
if len(q_measurements):
meas_from = q_measurements[(q_measurements.bus.values.astype(
int) == self.net.line.from_bus[q_measurements.element]).values]
meas_to = q_measurements[(q_measurements.bus.values.astype(
int) == self.net.line.to_bus[q_measurements.element]).values]
ix_from = meas_from.element.values.astype(int)
ix_to = meas_to.element.values.astype(int)
branch_append[ix_from,
8] = meas_from.value.values * 1e3 / self.s_ref
branch_append[ix_from,
9] = meas_from.std_dev.values * 1e3 / self.s_ref
branch_append[ix_to, 10] = meas_to.value.values * 1e3 / self.s_ref
branch_append[ix_to,
11] = meas_to.std_dev.values * 1e3 / self.s_ref
i_tr_measurements = self.net.measurement[
(self.net.measurement.type == "i")
& (self.net.measurement.element_type == "transformer")]
if len(i_tr_measurements):
meas_from = i_tr_measurements[(
i_tr_measurements.bus.values.astype(int) ==
self.net.trafo.hv_bus[i_tr_measurements.element]).values]
meas_to = i_tr_measurements[(
i_tr_measurements.bus.values.astype(int) ==
self.net.trafo.lv_bus[i_tr_measurements.element]).values]
ix_from = meas_from.element.values.astype(int)
ix_to = meas_to.element.values.astype(int)
i_a_to_pu_from = (self.net.bus.vn_kv[meas_from.bus] * 1e3 /
self.s_ref).values
i_a_to_pu_to = (self.net.bus.vn_kv[meas_to.bus] * 1e3 /
self.s_ref).values
branch_append[ix_from, 0] = meas_from.value.values * i_a_to_pu_from
branch_append[ix_from,
1] = meas_from.std_dev.values * i_a_to_pu_from
branch_append[ix_to, 2] = meas_to.value.values * i_a_to_pu_to
branch_append[ix_to, 3] = meas_to.std_dev.values * i_a_to_pu_to
p_tr_measurements = self.net.measurement[
(self.net.measurement.type == "p")
& (self.net.measurement.element_type == "transformer")]
if len(p_tr_measurements):
meas_from = p_tr_measurements[(
p_tr_measurements.bus.values.astype(int) ==
self.net.trafo.hv_bus[p_tr_measurements.element]).values]
meas_to = p_tr_measurements[(
p_tr_measurements.bus.values.astype(int) ==
self.net.trafo.lv_bus[p_tr_measurements.element]).values]
ix_from = len(self.net.line) + meas_from.element.values.astype(int)
ix_to = len(self.net.line) + meas_to.element.values.astype(int)
branch_append[ix_from,
4] = meas_from.value.values * 1e3 / self.s_ref
branch_append[ix_from,
5] = meas_from.std_dev.values * 1e3 / self.s_ref
branch_append[ix_to, 6] = meas_to.value.values * 1e3 / self.s_ref
branch_append[ix_to, 7] = meas_to.std_dev.values * 1e3 / self.s_ref
q_tr_measurements = self.net.measurement[
(self.net.measurement.type == "q")
& (self.net.measurement.element_type == "transformer")]
if len(q_tr_measurements):
meas_from = q_tr_measurements[(
q_tr_measurements.bus.values.astype(int) ==
self.net.trafo.hv_bus[q_tr_measurements.element]).values]
meas_to = q_tr_measurements[(
q_tr_measurements.bus.values.astype(int) ==
self.net.trafo.lv_bus[q_tr_measurements.element]).values]
ix_from = len(self.net.line) + meas_from.element.values.astype(int)
ix_to = len(self.net.line) + meas_to.element.values.astype(int)
branch_append[ix_from,
8] = meas_from.value.values * 1e3 / self.s_ref
branch_append[ix_from,
9] = meas_from.std_dev.values * 1e3 / self.s_ref
branch_append[ix_to, 10] = meas_to.value.values * 1e3 / self.s_ref
branch_append[ix_to,
11] = meas_to.std_dev.values * 1e3 / self.s_ref
ppc["bus"] = np.hstack((ppc["bus"], bus_append))
ppc["branch"] = np.hstack((ppc["branch"], branch_append))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ppc_i = ext2int(ppc)
p_bus_not_nan = ~np.isnan(ppc_i["bus"][:, bs_cols + 2])
p_line_f_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 4])
p_line_t_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 6])
q_bus_not_nan = ~np.isnan(ppc_i["bus"][:, bs_cols + 4])
q_line_f_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 8])
q_line_t_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 10])
v_bus_not_nan = ~np.isnan(ppc_i["bus"][:, bs_cols + 0])
i_line_f_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 0])
i_line_t_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 2])
# piece together our measurement vector z
z = np.concatenate(
(ppc_i["bus"][p_bus_not_nan,
bs_cols + 2], ppc_i["branch"][p_line_f_not_nan,
br_cols + 4],
ppc_i["branch"][p_line_t_not_nan,
br_cols + 6], ppc_i["bus"][q_bus_not_nan,
bs_cols + 4],
ppc_i["branch"][q_line_f_not_nan,
br_cols + 8], ppc_i["branch"][q_line_t_not_nan,
br_cols + 10],
ppc_i["bus"][v_bus_not_nan,
bs_cols + 0], ppc_i["branch"][i_line_f_not_nan,
br_cols + 0],
ppc_i["branch"][i_line_t_not_nan,
br_cols + 2])).real.astype(np.float64)
# number of nodes
n_active = len(np.where(ppc_i["bus"][:, 1] != 4)[0])
slack_buses = np.where(ppc_i["bus"][:, 1] == 3)[0]
# Check if observability criterion is fulfilled and the state estimation is possible
if len(z) < 2 * n_active - 1:
self.logger.error("System is not observable (cancelling)")
self.logger.error(
"Measurements available: %d. Measurements required: %d" %
(len(z), 2 * n_active - 1))
return False
# Set the starting values for all active buses
v_m = ppc_i["bus"][:, 7]
delta = ppc_i["bus"][:, 8] * np.pi / 180 # convert to rad
delta_masked = np.ma.array(delta, mask=False)
delta_masked.mask[slack_buses] = True
non_slack_buses = np.arange(len(delta))[~delta_masked.mask]
# Matrix calculation object
sem = wls_matrix_ops(ppc_i, slack_buses, non_slack_buses, self.s_ref,
bs_cols, br_cols)
# state vector
E = np.concatenate((delta_masked.compressed(), v_m))
# Covariance matrix R
r_cov = np.concatenate(
(ppc_i["bus"][p_bus_not_nan,
bs_cols + 3], ppc_i["branch"][p_line_f_not_nan,
br_cols + 5],
ppc_i["branch"][p_line_t_not_nan,
br_cols + 7], ppc_i["bus"][q_bus_not_nan,
bs_cols + 5],
ppc_i["branch"][q_line_f_not_nan,
br_cols + 9], ppc_i["branch"][q_line_t_not_nan,
br_cols + 11],
ppc_i["bus"][v_bus_not_nan,
bs_cols + 1], ppc_i["branch"][i_line_f_not_nan,
br_cols + 1],
ppc_i["branch"][i_line_t_not_nan,
br_cols + 3])).real.astype(np.float64)
r_inv = csr_matrix(np.linalg.inv(np.diagflat(r_cov)**2))
current_error = 100
current_iterations = 0
while current_error > self.tolerance and current_iterations < self.max_iterations:
self.logger.debug(" Starting iteration %d" %
(1 + current_iterations))
try:
# create h(x) for the current iteration
h_x = sem.create_hx(v_m, delta)
# Residual r
r = csr_matrix(z - h_x).T
# Jacobian matrix H
H = csr_matrix(sem.create_jacobian(v_m, delta))
# if not np.linalg.cond(H) < 1 / sys.float_info.epsilon:
# self.logger.error("Error in matrix H")
# Gain matrix G_m
# G_m = H^t * R^-1 * H
G_m = H.T * (r_inv * H)
# State Vector difference d_E
# d_E = G_m^-1 * (H' * R^-1 * r)
d_E = spsolve(G_m, H.T * (r_inv * r))
E += d_E
# Update V/delta
delta[non_slack_buses] = E[:len(non_slack_buses)]
v_m = np.squeeze(E[len(non_slack_buses):])
current_iterations += 1
current_error = np.max(np.abs(d_E))
self.logger.debug("Current error: %.4f" % current_error)
except np.linalg.linalg.LinAlgError:
self.logger.error(
"A problem appeared while using the linear algebra methods."
"Check and change the measurement set.")
return False
# Print output for results
if current_error <= self.tolerance:
successful = True
self.logger.info(
"WLS State Estimation successful (%d iterations)" %
current_iterations)
else:
successful = False
self.logger.info(
"WLS State Estimation not successful (%d/%d iterations" %
(current_iterations, self.max_iterations))
# write voltage into ppc
ppc_i["bus"][:, 7] = v_m
ppc_i["bus"][:, 8] = delta * 180 / np.pi # convert to degree
# calculate bus powers
v_cpx = v_m * np.exp(1j * delta)
bus_powers_conj = np.zeros(len(v_cpx), dtype=np.complex128)
for i in range(len(v_cpx)):
bus_powers_conj[i] = np.dot(sem.Y_bus[i, :], v_cpx) * np.conjugate(
v_cpx[i])
ppc_i["bus"][:, 2] = bus_powers_conj.real # saved in per unit
ppc_i["bus"][:, 3] = -bus_powers_conj.imag # saved in per unit
# convert to pandapower indices
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ppc = int2ext(ppc_i)
_set_buses_out_of_service(ppc)
# Store results, overwrite old results
self.net.res_bus_est = pd.DataFrame(
columns=["vm_pu", "va_degree", "p_kw", "q_kvar"],
index=self.net.bus.index)
self.net.res_line_est = pd.DataFrame(columns=[
"p_from_kw", "q_from_kvar", "p_to_kw", "q_to_kvar", "pl_kw",
"ql_kvar", "i_from_ka", "i_to_ka", "i_ka", "loading_percent"
],
index=self.net.line.index)
bus_idx = mapping_table[self.net["bus"].index.values]
self.net["res_bus_est"]["vm_pu"] = ppc["bus"][bus_idx][:, 7]
self.net["res_bus_est"]["va_degree"] = ppc["bus"][bus_idx][:, 8]
self.net.res_bus_est.p_kw = -get_values(
ppc["bus"][:, 2], self.net.bus.index,
mapping_table) * self.s_ref / 1e3
self.net.res_bus_est.q_kvar = -get_values(
ppc["bus"][:, 3], self.net.bus.index,
mapping_table) * self.s_ref / 1e3
self.net.res_line_est = calculate_line_results(self.net,
use_res_bus_est=True)
# Store some variables required for Chi^2 and r_N_max test:
self.R_inv = r_inv.toarray()
self.Gm = G_m.toarray()
self.r = r.toarray()
self.H = H.toarray()
self.Ht = self.H.T
self.hx = h_x
self.V = v_m
self.delta = delta
return successful
def estimate(self,
v_start=None,
delta_start=None,
calculate_voltage_angles=True):
"""
The function estimate is the main function of the module. It takes up to three input
arguments: v_start, delta_start and calculate_voltage_angles. The first two are the initial
state variables for the estimation process. Usually they can be initialized in a
"flat-start" condition: All voltages being 1.0 pu and all voltage angles being 0 degrees.
In this case, the parameters can be left at their default values (None). If the estimation
is applied continuously, using the results from the last estimation as the starting
condition for the current estimation can decrease the amount of iterations needed to
estimate the current state. The third parameter defines whether all voltage angles are
calculated absolutely, including phase shifts from transformers. If only the relative
differences between buses are required, this parameter can be set to False. Returned is a
boolean value, which is true after a successful estimation and false otherwise.
The resulting complex voltage will be written into the pandapower network. The result
fields are found res_bus_est of the pandapower network.
INPUT:
**net** - The net within this line should be created
**v_start** (np.array, shape=(1,), optional) - Vector with initial values for all
voltage magnitudes in p.u. (sorted by bus index)
**delta_start** (np.array, shape=(1,), optional) - Vector with initial values for all
voltage angles in degrees (sorted by bus index)
OPTIONAL:
**calculate_voltage_angles** - (bool) - Take into account absolute voltage angles and
phase shifts in transformers Default is True.
OUTPUT:
**successful** (boolean) - True if the estimation process was successful
Optional estimation variables:
The bus power injections can be accessed with *se.s_node_powers* and the estimated
values corresponding to the (noisy) measurement values with *se.hx*. (*hx* denotes h(x))
EXAMPLE:
success = estimate(np.array([1.0, 1.0, 1.0]), np.array([0.0, 0.0, 0.0]))
"""
if self.net is None:
raise UserWarning("Component was not initialized with a network.")
t0 = time()
# add initial values for V and delta
# node voltages
# V<delta
if v_start is None:
v_start = np.ones(self.net.bus.shape[0])
if delta_start is None:
delta_start = np.zeros(self.net.bus.shape[0])
# initialize result tables if not existent
_copy_power_flow_results(self.net)
# initialize ppc
ppc, ppci = _init_ppc(self.net, v_start, delta_start,
calculate_voltage_angles)
# add measurements to ppci structure
ppci = _add_measurements_to_ppc(self.net, ppci, self.s_ref)
# calculate relevant vectors from ppci measurements
z, self.pp_meas_indices, r_cov = _build_measurement_vectors(ppci)
# number of nodes
n_active = len(np.where(ppci["bus"][:, 1] != 4)[0])
slack_buses = np.where(ppci["bus"][:, 1] == 3)[0]
# Check if observability criterion is fulfilled and the state estimation is possible
if len(z) < 2 * n_active - 1:
self.logger.error("System is not observable (cancelling)")
self.logger.error(
"Measurements available: %d. Measurements required: %d" %
(len(z), 2 * n_active - 1))
return False
# set the starting values for all active buses
v_m = ppci["bus"][:, 7]
delta = ppci["bus"][:, 8] * np.pi / 180 # convert to rad
delta_masked = np.ma.array(delta, mask=False)
delta_masked.mask[slack_buses] = True
non_slack_buses = np.arange(len(delta))[~delta_masked.mask]
# matrix calculation object
sem = wls_matrix_ops(ppci, slack_buses, non_slack_buses, self.s_ref)
# state vector
E = np.concatenate((delta_masked.compressed(), v_m))
# invert covariance matrix
r_inv = csr_matrix(np.linalg.inv(np.diagflat(r_cov)**2))
current_error = 100.
cur_it = 0
G_m, r, H, h_x = None, None, None, None
while current_error > self.tolerance and cur_it < self.max_iterations:
self.logger.debug(" Starting iteration %d" % (1 + cur_it))
try:
# create h(x) for the current iteration
h_x = sem.create_hx(v_m, delta)
# residual r
r = csr_matrix(z - h_x).T
# jacobian matrix H
H = csr_matrix(sem.create_jacobian(v_m, delta))
# gain matrix G_m
# G_m = H^t * R^-1 * H
G_m = H.T * (r_inv * H)
# state vector difference d_E
# d_E = G_m^-1 * (H' * R^-1 * r)
d_E = spsolve(G_m, H.T * (r_inv * r))
E += d_E
# update V/delta
delta[non_slack_buses] = E[:len(non_slack_buses)]
v_m = np.squeeze(E[len(non_slack_buses):])
# prepare next iteration
cur_it += 1
current_error = np.max(np.abs(d_E))
self.logger.debug("Current error: %.7f" % current_error)
except np.linalg.linalg.LinAlgError:
self.logger.error(
"A problem appeared while using the linear algebra methods."
"Check and change the measurement set.")
return False
# print output for results
if current_error <= self.tolerance:
successful = True
self.logger.debug(
"WLS State Estimation successful (%d iterations)" % cur_it)
else:
successful = False
self.logger.debug(
"WLS State Estimation not successful (%d/%d iterations)" %
(cur_it, self.max_iterations))
# store results for all elements
# calculate bus power injections
v_cpx = v_m * np.exp(1j * delta)
bus_powers_conj = np.zeros(len(v_cpx), dtype=np.complex128)
for i in range(len(v_cpx)):
bus_powers_conj[i] = np.dot(sem.Y_bus[i, :], v_cpx) * np.conjugate(
v_cpx[i])
ppci["bus"][:, 2] = bus_powers_conj.real # saved in per unit
ppci["bus"][:, 3] = -bus_powers_conj.imag # saved in per unit
ppci["bus"][:, 7] = v_m
ppci["bus"][:, 8] = delta * 180 / np.pi # convert to degree
# calculate line results (in ppc_i)
s_ref, bus, gen, branch = _get_pf_variables_from_ppci(ppci)[0:4]
out = np.flatnonzero(branch[:,
BR_STATUS] == 0) # out-of-service branches
br = np.flatnonzero(branch[:, BR_STATUS]).astype(
int) # in-service branches
# complex power at "from" bus
Sf = v_cpx[np.real(branch[br, F_BUS]).astype(int)] * np.conj(
sem.Yf[br, :] * v_cpx) * s_ref
# complex power injected at "to" bus
St = v_cpx[np.real(branch[br, T_BUS]).astype(int)] * np.conj(
sem.Yt[br, :] * v_cpx) * s_ref
branch[np.ix_(br, [PF, QF, PT, QT])] = np.c_[Sf.real, Sf.imag, St.real,
St.imag]
branch[np.ix_(out, [PF, QF, PT, QT])] = np.zeros((len(out), 4))
et = time() - t0
ppci = _store_results_from_pf_in_ppci(ppci, bus, gen, branch,
successful, cur_it, et)
# convert to pandapower indices
ppc = _copy_results_ppci_to_ppc(ppci, ppc, mode="se")
# extract results from ppc
_add_pf_options(self.net,
tolerance_kva=1e-5,
trafo_loading="current",
numba=True,
ac=True,
algorithm='nr',
max_iteration="auto")
# writes res_bus.vm_pu / va_degree and res_line
_extract_results_se(self.net, ppc)
# restore backup of previous results
_rename_results(self.net)
# additionally, write bus power injection results (these are not written in _extract_results)
mapping_table = self.net["_pd2ppc_lookups"]["bus"]
self.net.res_bus_est.p_kw = -get_values(
ppc["bus"][:, 2], self.net.bus.index.values,
mapping_table) * self.s_ref / 1e3
self.net.res_bus_est.q_kvar = -get_values(
ppc["bus"][:, 3], self.net.bus.index.values,
mapping_table) * self.s_ref / 1e3
# store variables required for chi^2 and r_N_max test:
self.R_inv = r_inv.toarray()
self.Gm = G_m.toarray()
self.r = r.toarray()
self.H = H.toarray()
self.Ht = self.H.T
self.hx = h_x
self.V = v_m
self.delta = delta
# delete results which are not correctly calculated
for k in list(self.net.keys()):
if k.startswith("res_") and k.endswith("_est") and \
k not in ("res_bus_est", "res_line_est", "res_trafo_est", "res_trafo3w_est"):
del self.net[k]
return successful