def test_deviation_from_std_type(test_net, diag_params, diag_errors,
report_methods):
net = copy.deepcopy(test_net)
check_function = 'deviation_from_std_type'
diag_params = copy.deepcopy(diag_params)
report_methods = copy.deepcopy(report_methods)
net.line.r_ohm_per_km.loc[0] += 1
net.line.x_ohm_per_km.loc[6] -= 1
net.line.c_nf_per_km.loc[14] *= -1
net.line.max_i_ka.loc[21] = '5'
pp.change_std_type(net, 0, element='trafo', name='160 MVA 380/110 kV')
net.trafo.vk_percent.loc[0] *= 2
check_result = pp.deviation_from_std_type(net)
if check_result:
diag_results = {check_function: check_result}
else:
diag_results = {}
assert diag_results[check_function] == \
{'line': {0: {'e_value': 1.1571, 'param': 'r_ohm_per_km', 'std_type_in_lib': True,
'std_type_value': 0.1571},
6: {'e_value': -0.883, 'param': 'x_ohm_per_km', 'std_type_in_lib': True,
'std_type_value': 0.117},
14: {'e_value': -264.0, 'param': 'c_nf_per_km', 'std_type_in_lib': True,
'std_type_value': 264},
21: {'e_value': '5', 'param': 'max_i_ka', 'std_type_in_lib': True,
'std_type_value': 0.105}},
'trafo': {0: {'e_value': 24.4, 'param': 'vk_percent', 'std_type_in_lib': True,
'std_type_value': 12.2}}
}
for bool_value in [True, False]:
diag_report = DiagnosticReports(net,
diag_results,
diag_errors,
diag_params,
compact_report=bool_value)
report_check = None
try:
eval(report_methods[check_function])
report_check = True
except:
report_check = False
assert report_check
def pp2csv_data(net1,
export_pp_std_types=False,
drop_inactive_elements=True,
highest_existing_coordinate_number=-1,
round_qLoad_by_voltLvl=False,
reserved_aux_node_names=None):
""" Internal functionality of pp2csv, but without writing the determined dict to csv files.
For parameter explanations, please have a look at the pp2csv() docstring. """
# --- initializations
net = deepcopy(
net1) # necessary because in net will be changed in converter function
csv_data = _init_csv_tables(
['elements', 'profiles', 'types', 'res_elements'])
aux_nodes_are_reserved = reserved_aux_node_names is not None
# --- net data preparation for converting
_extend_pandapower_net_columns(net)
if drop_inactive_elements:
# attention: trafo3ws are not considered in current version of drop_inactive_elements()
pp.drop_inactive_elements(net, respect_switches=False)
check_results = pp.deviation_from_std_type(net)
if check_results:
logger.warning(
"There are deviations from standard types in elements: " +
str(["%s" % elm for elm in check_results.keys()]) +
". Only the standard " + "type values are converted to csv.")
convert_parallel_branches(net)
if net.bus.shape[0] and not net.bus_geodata.shape[0] or (
net.bus_geodata.shape[0] != net.bus.shape[0]):
logger.info(
"Since there are no or incomplete bus_geodata, generic geodata are assumed."
)
net.bus_geodata.drop(net.bus_geodata.index, inplace=True)
create_generic_coordinates(net)
merge_busbar_coordinates(net)
move_slack_gens_to_ext_grid(net)
scaling_is_not_1 = []
for i in pp.pp_elements():
# prevent elements without name
net[i] = ensure_full_column_data_existence(net, i, 'name')
avoid_duplicates_in_column(net, i, 'name')
# log scaling factor different from 1
if "scaling" in net[i].columns:
if not np.allclose(net[i]["scaling"].values, 1):
scaling_is_not_1 += [i]
if len(scaling_is_not_1):
logger.warning("In elements " + str(scaling_is_not_1) +
", the parameter 'scaling' " +
"differs from 1, which is not converted.")
# log min_e_mwh
if not np.allclose(net.storage["min_e_mwh"].dropna().values, 0.):
logger.warning(
"Storage parameter 'min_e_mwh' is not converted but differs from 0."
)
# further preparation
provide_subnet_col(net)
provide_voltLvl_col(net)
provide_substation_cols(net)
convert_node_type(net)
_add_dspf_calc_type_and_phys_type_columns(net)
_add_vm_va_setpoints_to_buses(net)
_prepare_res_bus_table(net)
reserved_aux_node_names = replace_branch_switches(net,
reserved_aux_node_names)
_convert_measurement(net)
_add_coordID(net, highest_existing_coordinate_number)
if not net["trafo"]["autoTapSide"].isnull().all():
net["trafo"]["autoTapSide"] = net["trafo"]["autoTapSide"].str.upper()
# --- convert net
_pp_profiles_to_csv(net, csv_data)
if "loadcases" in net:
csv_data["StudyCases"] = net["loadcases"]
else:
csv_data["StudyCases"] = pd.DataFrame()
_pp_types_to_csv1(net, export_pp_std_types)
_multi_parameter_determination(net)
_convert_elements_and_types(net, csv_data)
_pp_types_to_csv2(csv_data)
if round_qLoad_by_voltLvl:
_round_qLoad_by_voltLvl(csv_data)
# --- post_conversion_checks
_check_id_voltLvl_subnet(csv_data)
if aux_nodes_are_reserved:
return csv_data, reserved_aux_node_names
else:
return csv_data