def _get_p_q_gen_results_3ph(net, ppc0, ppc1, ppc2):
_is_elements = net["_is_elements"]
ac = net["_options"]["ac"]
gen_is_mask = _is_elements['gen']
gen_lookup = net["_pd2ppc_lookups"]["gen"]
gen_is_idx = net["gen"].index[gen_is_mask]
# indices of in service gens in the ppc
if np.any(_is_elements["gen"]):
gen_idx_ppc = gen_lookup[gen_is_idx]
else:
gen_idx_ppc = []
# read results from ppc for these buses
n_res_gen = len(net['gen'])
gen_idx_ppc = gen_lookup[gen_is_idx]
""" # 2 ext_grids Fix: Instead of the generator index, bus indices of the generators are used"""
gen_bus_idx_ppc = np.real(ppc1["gen"][gen_idx_ppc, GEN_BUS]).astype(int)
V012 = np.array(np.zeros((3, n_res_gen)))
V012[:, gen_is_idx] = np.array([
ppc["bus"][gen_bus_idx_ppc, VM] *
np.exp(1j * np.deg2rad(ppc["bus"][gen_bus_idx_ppc, VA]))
for ppc in [ppc0, ppc1, ppc2]
])
S012 = np.array(np.zeros((3, n_res_gen)))
S012[:, gen_is_idx] = np.array([
-(ppc["gen"][gen_idx_ppc, PG] + 1j * ppc["gen"][gen_idx_ppc, QG])
for ppc in [ppc0, ppc1, ppc2]
])
I012 = np.array(np.zeros((3, n_res_gen)))
I012[:, gen_is_idx] = I_from_SV_elementwise(S012[:, gen_is_idx],
V012[:, gen_is_idx])
Vabc = sequence_to_phase(V012)
Iabc = sequence_to_phase(I012)
Sabc = S_from_VI_elementwise(Vabc, Iabc) * 1e3
pA, pB, pC = map(lambda x: x.flatten(), np.real(Sabc))
qA, qB, qC = map(lambda x: x.flatten(), np.imag(Sabc))
net["res_gen_3ph"]["p_a_mw"] = pA
net["res_gen_3ph"]["p_b_mw"] = pB
net["res_gen_3ph"]["p_c_mw"] = pC
net["res_gen_3ph"]["q_a_mvar"] = qA
net["res_gen_3ph"]["q_b_mvar"] = qB
net["res_gen_3ph"]["q_c_mvar"] = qC
return pA, qA, pB, qB, pC, qC
def _get_trafo_results_3ph(net, ppc0, ppc1, ppc2, I012_f, V012_f, I012_t,
V012_t):
ac = net["_options"]["ac"]
trafo_loading = net["_options"]["trafo_loading"]
if not "trafo" in net._pd2ppc_lookups["branch"]:
return
f, t = net._pd2ppc_lookups["branch"]["trafo"]
I012_hv_ka = I012_f[:, f:t]
I012_lv_ka = I012_t[:, f:t]
trafo_df = net["trafo"]
Vabc_hv, Vabc_lv, Iabc_hv, Iabc_lv = [
sequence_to_phase(X012) for X012 in
[V012_f[:, f:t], V012_t[:, f:t], I012_f[:, f:t], I012_t[:, f:t]]
]
Sabc_hv, Sabc_lv = [
S_from_VI_elementwise(*Xabc_tup) / np.sqrt(3)
for Xabc_tup in [(Vabc_hv, Iabc_hv), (Vabc_lv, Iabc_lv)]
]
# Todo: Check why the sqrt(3) is necessary in the previous line as opposed to _get_line_results()
Pabc_hv_mw = Sabc_hv.real
Qabc_hv_mvar = Sabc_hv.imag
Pabc_lv_mw = Sabc_lv.real
Qabc_lv_mvar = Sabc_lv.imag
if ac:
Pabcl_mw = Pabc_hv_mw + Pabc_lv_mw
Qabcl_mvar = Qabc_hv_mvar + Qabc_lv_mvar
else:
Pabcl_mw = np.zeros_like(Pabc_hv_mw)
Qabcl_mvar = np.zeros_like(Qabc_lv_mvar)
Iabc_hv_ka = np.abs(sequence_to_phase(I012_hv_ka))
Iabc_lv_ka = np.abs(sequence_to_phase(I012_lv_ka))
#geting complex values of the sequence current
# Iabc_hv_ka_complex = sequence_to_phase(I012_hv_ka)
# Iabc_lv_ka_complex = sequence_to_phase(I012_lv_ka)
#
# Iabc_hv_ka = np.abs(Iabc_hv_ka_complex)
# Iabc_lv_ka = np.abs(Iabc_lv_ka_complex)
#
# In_hv_ka_complex = Iabc_hv_ka_complex.sum(axis=0)
# In_hv_ka = np.abs(In_hv_ka_complex)
# In_hv_ia_n_degree = np.angle(In_hv_ka_complex).flatten()*180/np.pi
# In_lv_ka_complex = Iabc_lv_ka_complex.sum(axis=0)
# In_lv_ka = np.abs(In_lv_ka_complex)
# In_lv_ia_n_degree = np.angle(In_lv_ka_complex).flatten()*180/np.pi
if trafo_loading == "current":
trafo_df = net["trafo"]
vns = np.vstack(
[trafo_df["vn_hv_kv"].values, trafo_df["vn_lv_kv"].values]).T
ld_trafo = np.maximum.reduce([
np.asarray(Iabc_hv_ka) * vns[:, 0],
np.asarray(Iabc_lv_ka) * vns[:, 1]
])
ld_trafo = ld_trafo * np.sqrt(3) / trafo_df["sn_mva"].values * 100.
elif trafo_loading == "power":
ld_trafo = np.maximum.reduce([np.abs(Sabc_hv), np.abs(Sabc_lv)])
ld_trafo = ld_trafo / net["trafo"][
"sn_mva"].values[:, np.newaxis] * 3. * 100.
else:
raise ValueError(
"Unknown transformer loading parameter %s - choose 'current' or 'power'"
% trafo_loading)
if any(net["trafo"]["df"].values <= 0):
raise UserWarning(
'Transformer rating factor df must be positive. Transformers with false '
'rating factors: %s' % net["trafo"].query('df<=0').index.tolist())
loading_percent = ld_trafo / net["trafo"]["parallel"].values / net[
"trafo"]["df"].values
# write results to trafo dataframe
res_trafo_df = net["res_trafo_3ph"]
res_trafo_df["p_a_hv_mw"] = Pabc_hv_mw[0, :].flatten()
res_trafo_df["p_b_hv_mw"] = Pabc_hv_mw[1, :].flatten()
res_trafo_df["p_c_hv_mw"] = Pabc_hv_mw[2, :].flatten()
res_trafo_df["q_a_hv_mvar"] = Qabc_hv_mvar[0, :].flatten()
res_trafo_df["q_b_hv_mvar"] = Qabc_hv_mvar[1, :].flatten()
res_trafo_df["q_c_hv_mvar"] = Qabc_hv_mvar[2, :].flatten()
res_trafo_df["p_a_lv_mw"] = Pabc_lv_mw[0, :].flatten()
res_trafo_df["p_b_lv_mw"] = Pabc_lv_mw[1, :].flatten()
res_trafo_df["p_c_lv_mw"] = Pabc_lv_mw[2, :].flatten()
res_trafo_df["q_a_lv_mvar"] = Qabc_lv_mvar[0, :].flatten()
res_trafo_df["q_b_lv_mvar"] = Qabc_lv_mvar[1, :].flatten()
res_trafo_df["q_c_lv_mvar"] = Qabc_lv_mvar[2, :].flatten()
res_trafo_df["p_a_l_mw"] = Pabcl_mw[0, :].flatten()
res_trafo_df["p_b_l_mw"] = Pabcl_mw[1, :].flatten()
res_trafo_df["p_c_l_mw"] = Pabcl_mw[2, :].flatten()
res_trafo_df["q_a_l_mvar"] = Qabcl_mvar[0, :].flatten()
res_trafo_df["q_b_l_mvar"] = Qabcl_mvar[1, :].flatten()
res_trafo_df["q_c_l_mvar"] = Qabcl_mvar[2, :].flatten()
res_trafo_df["i_a_hv_ka"] = Iabc_hv_ka[0, :].flatten()
res_trafo_df["i_b_hv_ka"] = Iabc_hv_ka[1, :].flatten()
res_trafo_df["i_c_hv_ka"] = Iabc_hv_ka[2, :].flatten()
# res_trafo_df["i_n_hv_ka"] = In_hv_ka.flatten()
res_trafo_df["i_a_lv_ka"] = Iabc_lv_ka[0, :].flatten()
res_trafo_df["i_b_lv_ka"] = Iabc_lv_ka[1, :].flatten()
res_trafo_df["i_c_lv_ka"] = Iabc_lv_ka[2, :].flatten()
# res_trafo_df["i_n_lv_ka"] = In_lv_ka.flatten()
res_trafo_df["loading_a_percent"] = loading_percent[0, :]
res_trafo_df["loading_b_percent"] = loading_percent[1, :]
res_trafo_df["loading_c_percent"] = loading_percent[2, :]
res_trafo_df["loading_percent"] = loading_percent.max(axis=0)
res_trafo_df.index = net["trafo"].index.values
def _get_line_results_3ph(net, ppc0, ppc1, ppc2, I012_f, V012_f, I012_t,
V012_t):
# create res_line_vals which are written to the pandas dataframe
ac = net["_options"]["ac"]
if not "line" in net._pd2ppc_lookups["branch"]:
return
f, t = net._pd2ppc_lookups["branch"]["line"]
I012_from_ka = I012_f[:, f:t]
I012_to_ka = I012_t[:, f:t]
line_df = net["line"]
i_max_phase = line_df["max_i_ka"].values * line_df["df"].values * line_df[
"parallel"].values
Vabc_f, Vabc_t, Iabc_f, Iabc_t = [
sequence_to_phase(X012) for X012 in
[V012_f[:, f:t], V012_t[:, f:t], I012_f[:, f:t], I012_t[:, f:t]]
]
Sabc_f, Sabc_t = [
S_from_VI_elementwise(*Xabc_tup) / np.sqrt(3)
for Xabc_tup in [(Vabc_f, Iabc_f), (Vabc_t, Iabc_t)]
]
# Todo: Check why the sqrt(3) is necessary in the previous line as opposed to _get_line_results()
Pabcf_mw = Sabc_f.real
Qabcf_mvar = Sabc_f.imag
Pabct_mw = Sabc_t.real
Qabct_mvar = Sabc_t.imag
if ac:
Pabcl_mw = Pabcf_mw + Pabct_mw
Qabcl_mvar = Qabcf_mvar + Qabct_mvar
else:
Pabcl_mw = np.zeros_like(Pabcf_mw)
Qabcl_mvar = np.zeros_like(Qabct_mvar)
#getting complex values of the sequence current line
Iabc_f_ka_complex = sequence_to_phase(I012_from_ka)
Iabc_t_ka_complex = sequence_to_phase(I012_to_ka)
Iabc_f_ka = np.abs(Iabc_f_ka_complex)
Iabc_t_ka = np.abs(Iabc_t_ka_complex)
Iabc_ka = np.maximum.reduce([Iabc_t_ka, Iabc_f_ka])
In_f_ka_complex = Iabc_f_ka_complex.sum(axis=0)
In_f_ka = np.abs(In_f_ka_complex)
# In_f_ia_n_degree = np.angle(In_f_ka_complex).flatten()*180/np.pi
In_t_ka_complex = Iabc_t_ka_complex.sum(axis=0)
# In_t_ia_n_degree = np.angle(In_t_ka_complex).flatten()*180/np.pi
In_t_ka = np.abs(In_t_ka_complex)
In_ka = np.maximum.reduce([In_t_ka, In_f_ka])
# write to line
net["res_line_3ph"]["p_a_from_mw"] = Pabcf_mw[0, :].flatten()
net["res_line_3ph"]["p_b_from_mw"] = Pabcf_mw[1, :].flatten()
net["res_line_3ph"]["p_c_from_mw"] = Pabcf_mw[2, :].flatten()
net["res_line_3ph"]["q_a_from_mvar"] = Qabcf_mvar[0, :].flatten()
net["res_line_3ph"]["q_b_from_mvar"] = Qabcf_mvar[1, :].flatten()
net["res_line_3ph"]["q_c_from_mvar"] = Qabcf_mvar[2, :].flatten()
net["res_line_3ph"]["p_a_to_mw"] = Pabct_mw[0, :].flatten()
net["res_line_3ph"]["p_b_to_mw"] = Pabct_mw[1, :].flatten()
net["res_line_3ph"]["p_c_to_mw"] = Pabct_mw[2, :].flatten()
net["res_line_3ph"]["q_a_to_mvar"] = Qabct_mvar[0, :].flatten()
net["res_line_3ph"]["q_b_to_mvar"] = Qabct_mvar[1, :].flatten()
net["res_line_3ph"]["q_c_to_mvar"] = Qabct_mvar[2, :].flatten()
net["res_line_3ph"]["p_a_l_mw"] = Pabcl_mw[0, :].flatten()
net["res_line_3ph"]["p_b_l_mw"] = Pabcl_mw[1, :].flatten()
net["res_line_3ph"]["p_c_l_mw"] = Pabcl_mw[2, :].flatten()
net["res_line_3ph"]["q_a_l_mvar"] = Qabcl_mvar[0, :].flatten()
net["res_line_3ph"]["q_b_l_mvar"] = Qabcl_mvar[1, :].flatten()
net["res_line_3ph"]["q_c_l_mvar"] = Qabcl_mvar[2, :].flatten()
net["res_line_3ph"]["i_a_from_ka"] = Iabc_f_ka[0, :].flatten()
net["res_line_3ph"]["i_b_from_ka"] = Iabc_f_ka[1, :].flatten()
net["res_line_3ph"]["i_c_from_ka"] = Iabc_f_ka[2, :].flatten()
net["res_line_3ph"]["i_a_to_ka"] = Iabc_t_ka[0, :].flatten()
net["res_line_3ph"]["i_b_to_ka"] = Iabc_t_ka[1, :].flatten()
net["res_line_3ph"]["i_c_to_ka"] = Iabc_t_ka[2, :].flatten()
net["res_line_3ph"]["i_a_ka"] = Iabc_ka[0, :]
net["res_line_3ph"]["i_b_ka"] = Iabc_ka[1, :]
net["res_line_3ph"]["i_c_ka"] = Iabc_ka[2, :]
net["res_line_3ph"]["i_n_from_ka"] = In_f_ka
net["res_line_3ph"]["i_n_to_ka"] = In_t_ka
net["res_line_3ph"]["i_n_ka"] = In_ka
net["res_line_3ph"]["loading_a_percent"] = Iabc_ka[
0, :] / i_max_phase * 100
net["res_line_3ph"]["loading_b_percent"] = Iabc_ka[
1, :] / i_max_phase * 100
net["res_line_3ph"]["loading_c_percent"] = Iabc_ka[
2, :] / i_max_phase * 100
net["res_line_3ph"]["loading_percent"] = Iabc_ka.max(
axis=0) / i_max_phase * 100
net["res_line_3ph"].index = net["line"].index
def runpp_3ph(net,
calculate_voltage_angles=True,
init="auto",
max_iteration="auto",
tolerance_mva=1e-8,
trafo_model='t',
trafo_loading="current",
enforce_q_lims=False,
numba=True,
recycle=None,
check_connectivity=True,
switch_rx_ratio=2.0,
delta_q=0,
v_debug=False,
**kwargs):
"""
runpp_3ph: Performs Unbalanced/Asymmetric/Three Phase Load flow
INPUT:
**net** - The pandapower format network
OPTIONAL:
**algorithm** (str, "nr") - algorithm that is used to solve the power
flow problem.
The following algorithms are available:
- "nr" Newton-Raphson (pypower implementation with numba accelerations)
Used only for positive sequence network
Zero and Negative sequence networks use Current Injection method
Vnew = Y.inv * Ispecified ( from s_abc/v_abc old)
Icalculated = Y * Vnew
**calculate_voltage_angles** (bool, "auto") - consider voltage angles
in loadflow calculation
If True, voltage angles of ext_grids and transformer shifts are
considered in the loadflow calculation. Considering the voltage
angles is only necessary in meshed networks that are usually
found in higher voltage levels. calculate_voltage_angles
in "auto" mode defaults to:
- True, if the network voltage level is above 70 kV
- False otherwise
The network voltage level is defined as the maximum rated voltage
of any bus in the network that is connected to a line.
**max_iteration** (int, "auto") - maximum number of iterations carried
out in the power flow algorithm.
In "auto" mode, the default value depends on the power flow solver:
- 10 for "nr"
For three phase calculations, its extended to 3 * max_iteration
**tolerance_mva** (float, 1e-8) - loadflow termination condition
referring to P / Q mismatch of node power in MVA
**trafo_model**
- transformer equivalent models
- "t" - transformer is modeled as equivalent with the T-model.
- "pi" - This is not recommended, since it is less exact than the T-model. So, for three phase load flow, its not implemented
**trafo_loading** (str, "current") - mode of calculation for
transformer loading
Transformer loading can be calculated relative to the rated
current or the rated power. In both cases the overall transformer
loading is defined as the maximum loading on the two sides of
the transformer.
- "current"- transformer loading is given as ratio of current
flow and rated current of the transformer. This is the recommended
setting, since thermal as well as magnetic effects in the
transformer depend on the current.
- "power" - transformer loading is given as ratio of apparent
power flow to the rated apparent power of the transformer.
**enforce_q_lims** (bool, False) (Not tested with 3 Phase load flow)
- respect generator reactive power limits
If True, the reactive power limits in net.gen.max_q_mvar/min_q_mvar
are respected in the loadflow. This is done by running a second
loadflow if reactive power limits are violated at any generator,
so that the runtime for the loadflow will increase if reactive
power has to be curtailed.
Note: enforce_q_lims only works if algorithm="nr"!
**check_connectivity** (bool, True) - Perform an extra connectivity
test after the conversion from pandapower to PYPOWER.
If True, an extra connectivity test based on SciPy Compressed
Sparse Graph Routines is perfomed. If check finds unsupplied buses,
they are set out of service in the ppc
**voltage_depend_loads** (bool, True)
(Not tested with 3 Phase load flow) - consideration of voltage-dependent loads.
If False, ``net.load.const_z_percent`` and ``net.load.const_i_percent``
are not considered, i.e. ``net.load.p_mw`` and ``net.load.q_mvar``
are considered as constant-power loads.
**consider_line_temperature** (bool, False) (Not tested with 3 Phase
load flow) - adjustment of line impedance based on provided line temperature.
If True, ``net.line`` must contain a column ``temperature_degree_celsius``.
The temperature dependency coefficient alpha must be provided in
the ``net.line.alpha`` column, otherwise the default value of 0.004 is used.
**KWARGS**:
**numba** (bool, True) - Activation of numba JIT compiler in the
newton solver
If set to True, the numba JIT compiler is used to generate
matrices for the powerflow, which leads to significant speed
improvements.
**switch_rx_ratio** (float, 2)
(Not tested with 3 Phase load flow) - rx_ratio of bus-bus-switches.
If impedance is zero, buses connected by a closed bus-bus switch
are fused to model an ideal bus. Otherwise, they are modelled
as branches with resistance defined as z_ohm column in switch
table and this parameter
**delta_q**
(Not tested with 3 Phase load flow) - Reactive power tolerance for option "enforce_q_lims"
in kvar - helps convergence in some cases.
**trafo3w_losses**
(Not tested with 3 Phase load flow) - defines where open loop losses of three-winding
transformers are considered. Valid options are "hv", "mv", "lv"
for HV/MV/LV side or "star" for the star point.
**v_debug** (bool, False) (Not tested with 3 Phase load flow) - if True,
voltage values in each newton-raphson iteration are logged in the ppc.
**init_vm_pu** (string/float/array/Series, None) (Not tested with 3
Phase load flow) - Allows to define initialization specifically for
voltage magnitudes. Only works with ``init == "auto"``!
- "auto": all buses are initialized with the mean value of all
voltage controlled elements in the grid
- "flat" for flat start from 1.0
- "results": voltage magnitude vector is taken from result table
- a float with which all voltage magnitudes are initialized
- an iterable with a voltage magnitude value for each bus
(length and order has to match with the buses in net.bus)
- a pandas Series with a voltage magnitude value for each bus
(indexes have to match the indexes in net.bus)
**init_va_degree** (string/float/array/Series, None) (Not tested with
3 Phase load flow) - Allows to define initialization specifically for voltage angles.
Only works with ``init == "auto"``!
- "auto": voltage angles are initialized from DC power flow
if angles are calculated or as 0 otherwise
- "dc": voltage angles are initialized from DC power flow
- "flat" for flat start from 0
- "results": voltage angle vector is taken from result table
- a float with which all voltage angles are initialized
- an iterable with a voltage angle value for each bus (length
and order has to match with the buses in net.bus)
- a pandas Series with a voltage angle value for each bus (indexes
have to match the indexes in net.bus)
**recycle** (dict, none) - Reuse of internal powerflow variables for
time series calculation.
Contains a dict with the following parameters:
bus_pq: If True PQ values of buses are updated
gen: If True Sbus and the gen table in the ppc are recalculated
Ybus: If True the admittance matrix (Ybus, Yf, Yt) is taken from
ppc["internal"] and not reconstructed
**neglect_open_switch_branches** (bool, False)
(Not tested with 3 Phase load flow) - If True no auxiliary
buses are created for branches when switches are opened at the branch.
Instead branches are set out of service
SEE ALSO:
pp.add_zero_impedance_parameters(net):
To add zero sequence parameters into network from the standard type
EXAMPLES:
Use this module like this:
.. code-block:: python
from pandapower.pf.runpp_3ph import runpp_3ph
runpp_3ph(net)
NOTES:
- Three phase load flow uses Sequence Frame for power flow solution.
- Three phase system is modelled with earth return.
- PH-E load type is called as wye since Neutral and Earth are considered same
- This solver has proved successful only for Earthed transformers (i.e Dyn,Yyn,YNyn & Yzn vector groups)
"""
# =============================================================================
# pandapower settings
# =============================================================================
overrule_options = {}
if "user_pf_options" in net.keys() and len(net.user_pf_options) > 0:
passed_parameters = _passed_runpp_parameters(locals())
overrule_options = {
key: val
for key, val in net.user_pf_options.items()
if key not in passed_parameters.keys()
}
if numba:
numba = _check_if_numba_is_installed(numba)
ac = True
mode = "pf_3ph" # TODO: Make valid modes (pf, pf_3ph, se, etc.) available in seperate file (similar to idx_bus.py)
# v_debug = kwargs.get("v_debug", False)
copy_constraints_to_ppc = False
if trafo_model == 'pi':
raise Not_implemented("Three phase Power Flow doesnot support pi model\
because of lack of accuracy")
# if calculate_voltage_angles == "auto":
# calculate_voltage_angles = False
# hv_buses = np.where(net.bus.vn_kv.values > 70)[0] # Todo: Where does that number come from?
# if len(hv_buses) > 0:
# line_buses = net.line[["from_bus", "to_bus"]].values.flatten()
# if len(set(net.bus.index[hv_buses]) & set(line_buses)) > 0:
# scipy spsolve options in NR power flow
use_umfpack = kwargs.get("use_umfpack", True)
permc_spec = kwargs.get("permc_spec", None)
calculate_voltage_angles = True
if init == "results" and net.res_bus_3ph.empty:
init = "auto"
if init == "auto":
init = "dc" if calculate_voltage_angles else "flat"
default_max_iteration = {
"nr": 10,
"bfsw": 10,
"gs": 10000,
"fdxb": 30,
"fdbx": 30
}
if max_iteration == "auto":
max_iteration = default_max_iteration["nr"]
neglect_open_switch_branches = kwargs.get("neglect_open_switch_branches",
False)
only_v_results = kwargs.get("only_v_results", False)
net._options = {}
_add_ppc_options(net, calculate_voltage_angles=calculate_voltage_angles,
trafo_model=trafo_model, check_connectivity=check_connectivity,
mode=mode, switch_rx_ratio=switch_rx_ratio,
init_vm_pu=init, init_va_degree=init,
enforce_q_lims=enforce_q_lims, recycle=None,
voltage_depend_loads=False, delta=delta_q,\
neglect_open_switch_branches=neglect_open_switch_branches
)
_add_pf_options(net, tolerance_mva=tolerance_mva, trafo_loading=trafo_loading,
numba=numba, ac=ac, algorithm="nr", max_iteration=max_iteration,\
only_v_results=only_v_results,v_debug=v_debug, use_umfpack=use_umfpack,
permc_spec=permc_spec)
net._options.update(overrule_options)
_check_bus_index_and_print_warning_if_high(net)
_check_gen_index_and_print_warning_if_high(net)
# =========================================================================
# pd2ppc conversion
# =========================================================================
_, ppci1 = _pd2ppc_recycle(net, 1, recycle=recycle)
_, ppci2 = _pd2ppc_recycle(net, 2, recycle=recycle)
gs_eg, bs_eg = _add_ext_grid_sc_impedance(net, ppci2)
_, ppci0 = _pd2ppc_recycle(net, 0, recycle=recycle)
_, bus0, gen0, branch0, _, _, _ = _get_pf_variables_from_ppci(ppci0)
base_mva, bus1, gen1, branch1, sl_bus, _, pq_bus = _get_pf_variables_from_ppci(
ppci1)
_, bus2, gen2, branch2, _, _, _ = _get_pf_variables_from_ppci(ppci2)
# initialize the results after the conversion to ppc is done, otherwise init=results does not work
init_results(net, "pf_3ph")
# =============================================================================
# P Q values aggragated and summed up for each bus to make s_abc matrix
# s_abc for wye connections ; s_abc_delta for delta connection
# =============================================================================
s_abc_delta, s_abc = _load_mapping(net, ppci1)
# =========================================================================
# Construct Sequence Frame Bus admittance matrices Ybus
# =========================================================================
ppci0, ppci1, ppci2, y_0_pu, y_1_pu, y_2_pu, y_0_f, y_1_f, _,\
y_0_t, y_1_t, _ = _get_y_bus(ppci0, ppci1, ppci2, recycle)
# =========================================================================
# Initial voltage values
# =========================================================================
nb = ppci1["bus"].shape[0]
# make sure flat start is always respected, even with other voltage data in recycled ppc
if init == "flat":
v_012_it = np.zeros((3, nb), dtype=np.complex128)
v_012_it[1, :] = 1.0
else:
v_012_it = np.concatenate([
np.array(ppc["bus"][:, VM] *
np.exp(1j * np.deg2rad(ppc["bus"][:, VA]))).reshape(
(1, nb)) for ppc in (ppci0, ppci1, ppci2)
],
axis=0).astype(np.complex128)
# For Delta transformation:
# Voltage changed from line-earth to line-line using V_T
# s_abc/v_abc will now give line-line currents. This is converted to line-earth
# current using I-T
v_del_xfmn = np.array([[1, -1, 0], [0, 1, -1], [-1, 0, 1]])
i_del_xfmn = np.array([[1, 0, -1], [-1, 1, 0], [0, -1, 1]])
v_abc_it = sequence_to_phase(v_012_it)
# =========================================================================
# Iteration using Power mismatch criterion
# =========================================================================
outer_tolerance_mva = 3e-8
count = 0
s_mismatch = np.array([[True], [True]], dtype=bool)
t0 = perf_counter()
while (s_mismatch >
outer_tolerance_mva).any() and count < 30 * max_iteration:
# =====================================================================
# Voltages and Current transformation for PQ and Slack bus
# =====================================================================
s_abc_pu = -np.divide(s_abc, ppci1["baseMVA"])
s_abc_delta_pu = -np.divide(s_abc_delta, ppci1["baseMVA"])
i_abc_it_wye = (np.divide(s_abc_pu, v_abc_it)).conjugate()
i_abc_it_delta = np.matmul(i_del_xfmn, (np.divide(
s_abc_delta_pu, np.matmul(v_del_xfmn, v_abc_it))).conjugate())
# For buses with both delta and wye loads we need to sum of their currents
# to sum up the currents
i_abc_it = i_abc_it_wye + i_abc_it_delta
i012_it = phase_to_sequence(i_abc_it)
v1_for_s1 = v_012_it[1, :]
i1_for_s1 = -i012_it[1, :]
v0_pu_it = X012_to_X0(v_012_it)
v2_pu_it = X012_to_X2(v_012_it)
i0_pu_it = X012_to_X0(i012_it)
i2_pu_it = X012_to_X2(i012_it)
s1 = np.multiply(v1_for_s1, i1_for_s1.conjugate())
# =============================================================================
# Current used to find S1 Positive sequence power
# =============================================================================
ppci1["bus"][pq_bus, PD] = np.real(s1[pq_bus]) * ppci1["baseMVA"]
ppci1["bus"][pq_bus, QD] = np.imag(s1[pq_bus]) * ppci1["baseMVA"]
# =============================================================================
# Conduct Positive sequence power flow
# =============================================================================
_run_newton_raphson_pf(ppci1, net._options)
# =============================================================================
# Conduct Negative and Zero sequence power flow
# =============================================================================
v0_pu_it = V_from_I(y_0_pu, i0_pu_it)
v2_pu_it = V_from_I(y_2_pu, i2_pu_it)
# =============================================================================
# Evaluate Positive Sequence Power Mismatch
# =============================================================================
i1_from_v_it = I1_from_V012(v_012_it, y_1_pu).flatten()
s_from_voltage = S_from_VI_elementwise(v1_for_s1, i1_from_v_it)
v1_pu_it = V1_from_ppc(ppci1)
v_012_new = combine_X012(v0_pu_it, v1_pu_it, v2_pu_it)
s_mismatch = np.abs(
np.abs(s1[pq_bus]) - np.abs(s_from_voltage[pq_bus]))
v_012_it = v_012_new
v_abc_it = sequence_to_phase(v_012_it)
count += 1
et = perf_counter() - t0
success = (count < 30 * max_iteration)
for ppc in [ppci0, ppci1, ppci2]:
ppc["et"] = et
ppc["success"] = success
# TODO: Add reference to paper to explain the following steps
# This is required since the ext_grid power results are not correct if its
# not done
ref, pv, pq = bustypes(ppci0["bus"], ppci0["gen"])
ref_gens = ppci0["internal"]["ref_gens"]
ppci0["bus"][ref, GS] -= gs_eg
ppci0["bus"][ref, BS] -= bs_eg
y_0_pu, y_0_f, y_0_t = makeYbus(ppci0["baseMVA"], ppci0["bus"],
ppci0["branch"])
# revert the change, otherwise repeated calculation with recycled elements will fail
ppci0["bus"][ref, GS] += gs_eg
ppci0["bus"][ref, BS] += bs_eg
# Bus, Branch, and Gen power values
bus0, gen0, branch0 = pfsoln(base_mva, bus0, gen0, branch0, y_0_pu, y_0_f,
y_0_t, v_012_it[0, :].flatten(), sl_bus,
ref_gens)
bus1, gen1, branch1 = pfsoln(base_mva, bus1, gen1, branch1, y_1_pu, y_1_f,
y_1_t, v_012_it[1, :].flatten(), sl_bus,
ref_gens)
bus2, gen2, branch2 = pfsoln(base_mva, bus2, gen2, branch2, y_1_pu, y_1_f,
y_1_t, v_012_it[2, :].flatten(), sl_bus,
ref_gens)
ppci0 = _store_results_from_pf_in_ppci(ppci0, bus0, gen0, branch0)
ppci1 = _store_results_from_pf_in_ppci(ppci1, bus1, gen1, branch1)
ppci2 = _store_results_from_pf_in_ppci(ppci2, bus2, gen2, branch2)
i_012_res = _current_from_voltage_results(y_0_pu, y_1_pu, v_012_it)
s_012_res = S_from_VI_elementwise(v_012_it, i_012_res) * ppci1["baseMVA"]
eg_is_mask = net["_is_elements"]['ext_grid']
ext_grid_lookup = net["_pd2ppc_lookups"]["ext_grid"]
eg_is_idx = net["ext_grid"].index.values[eg_is_mask]
eg_idx_ppc = ext_grid_lookup[eg_is_idx]
""" # 2 ext_grids Fix: Instead of the generator index, bus indices of the generators are used"""
eg_bus_idx_ppc = np.real(ppci1["gen"][eg_idx_ppc, GEN_BUS]).astype(int)
ppci0["gen"][eg_idx_ppc, PG] = s_012_res[0, eg_bus_idx_ppc].real
ppci1["gen"][eg_idx_ppc, PG] = s_012_res[1, eg_bus_idx_ppc].real
ppci2["gen"][eg_idx_ppc, PG] = s_012_res[2, eg_bus_idx_ppc].real
ppci0["gen"][eg_idx_ppc, QG] = s_012_res[0, eg_bus_idx_ppc].imag
ppci1["gen"][eg_idx_ppc, QG] = s_012_res[1, eg_bus_idx_ppc].imag
ppci2["gen"][eg_idx_ppc, QG] = s_012_res[2, eg_bus_idx_ppc].imag
ppc0 = net["_ppc0"]
ppc1 = net["_ppc1"]
ppc2 = net["_ppc2"]
# ppci doesn't contain out of service elements, but ppc does -> copy results accordingly
ppc0 = _copy_results_ppci_to_ppc(ppci0, ppc0, mode=mode)
ppc1 = _copy_results_ppci_to_ppc(ppci1, ppc1, mode=mode)
ppc2 = _copy_results_ppci_to_ppc(ppci2, ppc2, mode=mode)
_extract_results_3ph(net, ppc0, ppc1, ppc2)
# Raise error if PF was not successful. If DC -> success is always 1
if not ppci0["success"]:
net["converged"] = False
_clean_up(net, res=False)
raise LoadflowNotConverged("Power Flow {0} did not converge after\
{1} iterations!".format("nr", count))
else:
net["converged"] = True
_clean_up(net)
def _get_trafo_results_3ph(net, ppc0, ppc1, ppc2, I012_f, V012_f, I012_t,
V012_t):
ac = net["_options"]["ac"]
trafo_loading = net["_options"]["trafo_loading"]
if not "trafo" in net._pd2ppc_lookups["branch"]:
return
f, t = net._pd2ppc_lookups["branch"]["trafo"]
I012_hv_ka = I012_f[:, f:t]
I012_lv_ka = I012_t[:, f:t]
trafo_df = net["trafo"]
Vabc_hv, Vabc_lv, Iabc_hv, Iabc_lv = [
sequence_to_phase(X012) for X012 in
[V012_f[:, f:t], V012_t[:, f:t], I012_f[:, f:t], I012_t[:, f:t]]
]
Sabc_hv, Sabc_lv = [
S_from_VI_elementwise(*Xabc_tup) / np.sqrt(3)
for Xabc_tup in [(Vabc_hv, Iabc_hv), (Vabc_lv, Iabc_lv)]
]
# Todo: Check why the sqrt(3) is necessary in the previous line as opposed to _get_line_results()
Pabc_hv_mw = Sabc_hv.real
Qabc_hv_mvar = Sabc_hv.imag
Pabc_lv_mw = Sabc_lv.real
Qabc_lv_mvar = Sabc_lv.imag
if ac:
Pabcl_mw = Pabc_hv_mw + Pabc_lv_mw
Qabcl_mvar = Qabc_hv_mvar + Qabc_lv_mvar
else:
Pabcl_mw = np.zeros_like(Pabc_hv_mw)
Qabcl_mvar = np.zeros_like(Qabc_lv_mvar)
Iabc_hv_ka = np.abs(sequence_to_phase(I012_hv_ka))
Iabc_lv_ka = np.abs(sequence_to_phase(I012_lv_ka))
# current calculation for trafo lv side for vector groups with zero seq. gap (Dyn, Yzn)
# in this case, the currents of elemnts that go out from the trafo are summed and the sum applied to the trafo lv side
gap_trafo_index = np.where(I012_lv_ka[0] == 0)[0]
if len(gap_trafo_index > 0):
for i_trafo in gap_trafo_index:
Iabc_sum = [0, 0, 0]
lv_bus = net.trafo.lv_bus[i_trafo]
V_bus_abc = np.array([[
net.res_bus_3ph['vm_a_pu'][lv_bus] * net.bus['vn_kv'][lv_bus]
], [
net.res_bus_3ph['vm_b_pu'][lv_bus] * net.bus['vn_kv'][lv_bus]
], [net.res_bus_3ph['vm_c_pu'][lv_bus] * net.bus['vn_kv'][lv_bus]]
])
# Branch Elements
i_branch = np.where(ppc0['branch'][:, F_BUS] == lv_bus)[0]
if len(i_branch > 0):
I_branch_012 = I012_f[:, i_branch]
I_branch_abc = sequence_to_phase(I_branch_012)
for x in range(len(I_branch_abc[0])):
Iabc_sum += abs(I_branch_abc[:, x])
# Loads
load_index = np.where(net.asymmetric_load['bus'] == lv_bus)[0]
if len(load_index > 0):
S_load_abc = abs(
np.array([
net.res_asymmetric_load_3ph['p_a_mw'][load_index] +
(1j *
net.res_asymmetric_load_3ph['q_a_mvar'][load_index]),
net.res_asymmetric_load_3ph['p_b_mw'][load_index] +
(1j *
net.res_asymmetric_load_3ph['q_b_mvar'][load_index]),
net.res_asymmetric_load_3ph['p_c_mw'][load_index] +
(1j *
net.res_asymmetric_load_3ph['q_c_mvar'][load_index])
]))
I_load_abc = S_load_abc / (V_bus_abc / np.sqrt(3))
for x in range(len(I_load_abc[0])):
Iabc_sum += I_load_abc[:, x]
# Sgens
sgen_bus_index = np.where(net.asymmetric_sgen['bus'] == lv_bus)[0]
if len(sgen_bus_index > 0):
S_sgen_abc = abs(
np.array([
net.res_asymmetric_sgen_3ph['p_a_mw'][sgen_bus_index] +
(1j * net.res_asymmetric_sgen_3ph['q_a_mvar']
[sgen_bus_index]),
net.res_asymmetric_sgen_3ph['p_b_mw'][sgen_bus_index] +
(1j * net.res_asymmetric_sgen_3ph['q_b_mvar']
[sgen_bus_index]),
net.res_asymmetric_sgen_3ph['p_c_mw'][sgen_bus_index] +
(1j * net.res_asymmetric_sgen_3ph['q_c_mvar']
[sgen_bus_index])
]))
I_sgen_abc = S_sgen_abc / (V_bus_abc / np.sqrt(3))
for x in range(len(I_sgen_abc[0])):
Iabc_sum -= I_sgen_abc[:, x]
Iabc_lv_ka[:, i_trafo] = Iabc_sum
# geting complex values of the sequence current
# Iabc_hv_ka_complex = sequence_to_phase(I012_hv_ka)
# Iabc_lv_ka_complex = sequence_to_phase(I012_lv_ka)
#
# Iabc_hv_ka = np.abs(Iabc_hv_ka_complex)
# Iabc_lv_ka = np.abs(Iabc_lv_ka_complex)
#
# In_hv_ka_complex = Iabc_hv_ka_complex.sum(axis=0)
# In_hv_ka = np.abs(In_hv_ka_complex)
# In_hv_ia_n_degree = np.angle(In_hv_ka_complex).flatten()*180/np.pi
# In_lv_ka_complex = Iabc_lv_ka_complex.sum(axis=0)
# In_lv_ka = np.abs(In_lv_ka_complex)
# In_lv_ia_n_degree = np.angle(In_lv_ka_complex).flatten()*180/np.pi
if trafo_loading == "current":
trafo_df = net["trafo"]
vns = np.vstack(
[trafo_df["vn_hv_kv"].values, trafo_df["vn_lv_kv"].values]).T
ld_trafo = np.maximum.reduce([
np.asarray(Iabc_hv_ka) * vns[:, 0],
np.asarray(Iabc_lv_ka) * vns[:, 1]
])
ld_trafo = ld_trafo * np.sqrt(3) / trafo_df["sn_mva"].values * 100.
elif trafo_loading == "power":
ld_trafo = np.maximum.reduce([np.abs(Sabc_hv), np.abs(Sabc_lv)])
ld_trafo = ld_trafo / net["trafo"][
"sn_mva"].values[:, np.newaxis] * 3. * 100.
else:
raise ValueError(
"Unknown transformer loading parameter %s - choose 'current' or 'power'"
% trafo_loading)
if any(net["trafo"]["df"].values <= 0):
raise UserWarning(
'Transformer rating factor df must be positive. Transformers with false '
'rating factors: %s' % net["trafo"].query('df<=0').index.tolist())
loading_percent = ld_trafo / net["trafo"]["parallel"].values / net[
"trafo"]["df"].values
# write results to trafo dataframe
res_trafo_df = net["res_trafo_3ph"]
res_trafo_df["p_a_hv_mw"] = Pabc_hv_mw[0, :].flatten()
res_trafo_df["p_b_hv_mw"] = Pabc_hv_mw[1, :].flatten()
res_trafo_df["p_c_hv_mw"] = Pabc_hv_mw[2, :].flatten()
res_trafo_df["q_a_hv_mvar"] = Qabc_hv_mvar[0, :].flatten()
res_trafo_df["q_b_hv_mvar"] = Qabc_hv_mvar[1, :].flatten()
res_trafo_df["q_c_hv_mvar"] = Qabc_hv_mvar[2, :].flatten()
res_trafo_df["p_a_lv_mw"] = Pabc_lv_mw[0, :].flatten()
res_trafo_df["p_b_lv_mw"] = Pabc_lv_mw[1, :].flatten()
res_trafo_df["p_c_lv_mw"] = Pabc_lv_mw[2, :].flatten()
res_trafo_df["q_a_lv_mvar"] = Qabc_lv_mvar[0, :].flatten()
res_trafo_df["q_b_lv_mvar"] = Qabc_lv_mvar[1, :].flatten()
res_trafo_df["q_c_lv_mvar"] = Qabc_lv_mvar[2, :].flatten()
res_trafo_df["p_a_l_mw"] = Pabcl_mw[0, :].flatten()
res_trafo_df["p_b_l_mw"] = Pabcl_mw[1, :].flatten()
res_trafo_df["p_c_l_mw"] = Pabcl_mw[2, :].flatten()
res_trafo_df["q_a_l_mvar"] = Qabcl_mvar[0, :].flatten()
res_trafo_df["q_b_l_mvar"] = Qabcl_mvar[1, :].flatten()
res_trafo_df["q_c_l_mvar"] = Qabcl_mvar[2, :].flatten()
res_trafo_df["i_a_hv_ka"] = Iabc_hv_ka[0, :].flatten()
res_trafo_df["i_b_hv_ka"] = Iabc_hv_ka[1, :].flatten()
res_trafo_df["i_c_hv_ka"] = Iabc_hv_ka[2, :].flatten()
# res_trafo_df["i_n_hv_ka"] = In_hv_ka.flatten()
res_trafo_df["i_a_lv_ka"] = Iabc_lv_ka[0, :].flatten()
res_trafo_df["i_b_lv_ka"] = Iabc_lv_ka[1, :].flatten()
res_trafo_df["i_c_lv_ka"] = Iabc_lv_ka[2, :].flatten()
# res_trafo_df["i_n_lv_ka"] = In_lv_ka.flatten()
res_trafo_df["loading_a_percent"] = loading_percent[0, :]
res_trafo_df["loading_b_percent"] = loading_percent[1, :]
res_trafo_df["loading_c_percent"] = loading_percent[2, :]
res_trafo_df["loading_percent"] = loading_percent.max(axis=0)
res_trafo_df.index = net["trafo"].index.values