コード例 #1
0
    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
コード例 #2
0
    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