def test_ql_qc():
net = nw.case9()
net.sn_mva = 1.
pp.runpp(net)
add_virtual_meas_from_loadflow(net, p_std_dev=0.01, q_std_dev=0.01)
pf_vm_pu, pf_va_degree = net.res_bus.vm_pu, net.res_bus.va_degree
# give it a warm start
net, ppc, eppci = pp2eppci(net)
estimation_wls = WLSAlgorithm(1e-3, 5)
estimation_opt = OptAlgorithm(1e-6, 3000)
eppci = estimation_wls.estimate(eppci)
eppci = estimation_opt.estimate(eppci, estimator="ql", a=3, verbose=False)
if not estimation_opt.successful:
eppci = estimation_opt.estimate(eppci,
estimator="ql",
a=3,
opt_method="Newton-CG",
verbose=False)
if not estimation_opt.successful:
raise AssertionError("Estimation failed due to algorithm failing!")
net = eppci2pp(net, ppc, eppci)
if not np.allclose(pf_vm_pu, net.res_bus_est.vm_pu, atol=1e-2) or \
not np.allclose(pf_va_degree, net.res_bus_est.va_degree, atol=5e-2):
raise AssertionError("Estimation failed!")
# give it a warm start
net, ppc, eppci = pp2eppci(net)
estimation_wls = WLSAlgorithm(1e-6, 5)
estimation_opt = OptAlgorithm(1e-6, 3000)
eppci = estimation_wls.estimate(eppci)
eppci = estimation_opt.estimate(eppci, estimator="qc", a=3, verbose=False)
if not estimation_opt.successful:
eppci = estimation_opt.estimate(eppci,
estimator="qc",
a=3,
opt_method="Newton-CG",
verbose=False)
net = eppci2pp(net, ppc, eppci)
if not np.allclose(pf_vm_pu, net.res_bus_est.vm_pu, atol=1e-2) or \
not np.allclose(pf_va_degree, net.res_bus_est.va_degree, atol=5e-2):
raise AssertionError("Estimation failed!")
def test_case30_compare_classical_wls_opt_wls():
net = nw.case30()
pp.runpp(net)
add_virtual_meas_from_loadflow(net)
try:
success = estimate(net, init='flat', algorithm="opt", estimator='wls')
assert success
except:
# if failed give it a warm start
net, ppc, eppci = pp2eppci(net)
estimation_wls = WLSAlgorithm(1e-3, 3)
estimation_opt = OptAlgorithm(1e-6, 1000)
eppci = estimation_wls.estimate(eppci)
eppci = estimation_opt.estimate(eppci, estimator="wls")
assert estimation_opt.successful
net = eppci2pp(net, ppc, eppci)
net_wls = deepcopy(net)
estimate(net_wls)
assert np.allclose(net_wls.res_bus_est.vm_pu,
net.res_bus_est.vm_pu,
atol=1e-2)
assert np.allclose(net_wls.res_bus_est.va_degree,
net.res_bus_est.va_degree,
atol=1e-2)
def test_ql_qc():
net = nw.case9()
pp.runpp(net)
add_virtual_meas_from_loadflow(net, p_std_dev=0.01, q_std_dev=0.01)
pf_vm_pu, pf_va_degree = net.res_bus.vm_pu, net.res_bus.va_degree
# give it a warm start
net, ppc, eppci = pp2eppci(net)
estimation_wls = WLSAlgorithm(1e-3, 5)
estimation_opt = OptAlgorithm(1e-6, 3000)
eppci = estimation_wls.estimate(eppci)
try:
eppci = estimation_opt.estimate(eppci, estimator="ql", a=3)
assert estimation_opt.successful
except:
eppci = estimation_opt.estimate(eppci,
estimator="ql",
a=3,
opt_method="Newton-CG")
assert estimation_opt.successful
net = eppci2pp(net, ppc, eppci)
assert np.allclose(pf_vm_pu, net.res_bus_est.vm_pu, atol=1e-2)
assert np.allclose(pf_va_degree, net.res_bus_est.va_degree, atol=5e-2)
# give it a warm start
net, ppc, eppci = pp2eppci(net)
estimation_wls = WLSAlgorithm(1e-6, 5)
estimation_opt = OptAlgorithm(1e-6, 3000)
eppci = estimation_wls.estimate(eppci)
try:
eppci = estimation_opt.estimate(eppci, estimator="qc", a=3)
assert estimation_opt.successful
except:
eppci = estimation_opt.estimate(eppci,
estimator="qc",
a=3,
opt_method="Newton-CG")
assert estimation_opt.successful
net = eppci2pp(net, ppc, eppci)
assert np.allclose(pf_vm_pu, net.res_bus_est.vm_pu, atol=1e-2)
assert np.allclose(pf_va_degree, net.res_bus_est.va_degree, atol=5e-2)
def test_shgm_ps():
# we need an random eppci object to initialize estimator
net = nw.case14()
pp.runpp(net)
add_virtual_meas_from_loadflow(net)
_, _, eppci = pp2eppci(net)
# Using the example from Mili's paper
H = np.array([[10, -10], [1, 0], [-1, 0], [0, -1], [0, 1], [11, -10],
[-1, -1]])
estm = SHGMEstimatorIRWLS(eppci, a=3)
ps_estm = estm._ps(H)
assert np.allclose(ps_estm,
np.array([8.39, 0.84, 0.84, 0.84, 0.84, 8.82, 1.68]),
atol=0.005)
def test_opt_lav():
net = nw.case9()
pp.runpp(net)
add_virtual_meas_from_loadflow(net, with_random_error=False)
net, ppc, eppci = pp2eppci(net)
estimation_wls = WLSAlgorithm(1e-3, 5)
estimation_opt = OptAlgorithm(1e-6, 1000)
eppci = estimation_wls.estimate(eppci)
eppci = estimation_opt.estimate(eppci, estimator="lav", verbose=False)
net = eppci2pp(net, ppc, eppci)
if not np.allclose(net.res_bus.vm_pu, net.res_bus_est.vm_pu, atol=1e-2) or \
not np.allclose(net.res_bus.va_degree, net.res_bus_est.va_degree, atol=5e-2):
raise AssertionError("Estimation failed!")
def test_opt_lav():
net = nw.case9()
pp.runpp(net)
add_virtual_meas_from_loadflow(net, with_random_error=False)
net, ppc, eppci = pp2eppci(net)
estimation_wls = WLSAlgorithm(1e-3, 5)
estimation_opt = OptAlgorithm(1e-6, 1000)
eppci = estimation_wls.estimate(eppci)
eppci = estimation_opt.estimate(eppci, estimator="lav")
assert estimation_opt.successful
net = eppci2pp(net, ppc, eppci)
assert np.allclose(net.res_bus.vm_pu, net.res_bus_est.vm_pu, atol=1e-2)
assert np.allclose(net.res_bus.va_degree,
net.res_bus_est.va_degree,
atol=5e-2)
def test_lp_ortools_lav():
'''
If OR-Tools is installed, run this test.
'''
# Set the solver
LPAlgorithm.ortools_available = True
net = nw.case9()
pp.runpp(net)
add_virtual_meas_from_loadflow(net, with_random_error=False)
net, ppc, eppci = pp2eppci(net)
estimation_ortools_lp = LPAlgorithm(1e-3, 5)
estimation_ortools = estimation_ortools_lp.estimate(eppci,
with_ortools=True)
net = eppci2pp(net, ppc, eppci)
if not np.allclose(net.res_bus.vm_pu, net.res_bus_est.vm_pu, atol=1e-2) or \
not np.allclose(net.res_bus.va_degree, net.res_bus_est.va_degree, atol=5e-2):
raise AssertionError("Estimation failed!")
def test_case9_compare_classical_wls_opt_wls():
net = nw.case9()
pp.runpp(net)
add_virtual_meas_from_loadflow(net)
# give it a warm start
net, ppc, eppci = pp2eppci(net)
estimation_wls = WLSAlgorithm(1e-3, 3)
estimation_opt = OptAlgorithm(1e-6, 1000)
eppci = estimation_wls.estimate(eppci)
eppci = estimation_opt.estimate(eppci, estimator="wls", verbose=False)
if not estimation_opt.successful:
raise AssertionError("Estimation failed due to algorithm failing!")
net = eppci2pp(net, ppc, eppci)
net_wls = net.deepcopy()
estimate(net_wls)
if not np.allclose(net_wls.res_bus_est.vm_pu, net.res_bus_est.vm_pu, atol=1e-2) or \
not np.allclose(net_wls.res_bus_est.va_degree, net.res_bus_est.va_degree, atol=1e-2):
raise AssertionError("Estimation failed!")
def __init__(self, lcc_info, bnd_meas_info, key, fast_decoupled):
self.net, self.tie_line_param, self.bnd_meas, self.bnd_zi, self.bnd_Ri = lcc_info
self.bnd_meas_info = bnd_meas_info
self.key = key
self.secure = self.key is not None
if self.secure:
self.enc = Deg2PaillierThreshold(self.key)
self.fast_decoupled = fast_decoupled
self.net, self.ppc, self.eppci = pp2eppci(self.net, 'flat', 'flat',
True, None)
self.sem = ExtendedBaseAlgebra(self.eppci)
self.Ri_inv = csr_matrix(np.diagflat(1 / self.eppci.r_cov**2))
self.E = self.eppci.E
# If using the fast-decoupled model, bnd_flow_H can be computed locally
self.bnd_flow_Hi = self._compute_bnd_flow_H(
) if self.fast_decoupled else None
self.Hi, self.Gi, self.delta_yi = None, None, None
self.bnd_hi, self.bnd_Hi = None, None
self.ui = None
pass
def estimate(self,
v_start='flat',
delta_start='flat',
calculate_voltage_angles=True,
zero_injection=None,
fuse_buses_with_bb_switch='all',
**opt_vars):
"""
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:
**tolerance** - (float) - When the maximum state change between iterations is less than
tolerance, the process stops. Default is 1e-6
**maximum_iterations** - (integer) - Maximum number of iterations. Default is 10
**calculate_voltage_angles** - (boolean) - Take into account absolute voltage angles and phase
shifts in transformers, if init is 'slack'. Default is True
**zero_injection** - (str, iterable, None) - Defines which buses are zero injection bus or the method
to identify zero injection bus, with 'wls_estimator' virtual measurements will be added, with
'wls_estimator with zero constraints' the buses will be handled as constraints
"auto": all bus without p,q measurement, without p, q value (load, sgen...) and aux buses will be
identified as zero injection bus
"aux_bus": only aux bus will be identified as zero injection bus
None: no bus will be identified as zero injection bus
iterable: the iterable should contain index of the zero injection bus and also aux bus will be identified
as zero-injection bus
**fuse_buses_with_bb_switch** - (str, iterable, None) - Defines how buses with closed bb switches should
be handled, if fuse buses will only fused to one for calculation, if not fuse, an auxiliary bus and
auxiliary line will be automatically added to the network to make the buses with different p,q injection
measurements identifieble
"all": all buses with bb-switches will be fused, the same as the default behaviour in load flow
None: buses with bb-switches and individual p,q measurements will be reconfigurated
by auxiliary elements
iterable: the iterable should contain the index of buses to be fused, the behaviour is contigous e.g.
if one of the bus among the buses connected through bb switch is given, then all of them will still
be fused
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]))
"""
# check if all parameter are allowed
for var_name in opt_vars.keys():
if var_name not in ALLOWED_OPT_VAR:
self.logger.warning("Caution! %s is not allowed as parameter" % var_name \
+ " for estimate and will be ignored!")
if self.net is None:
raise UserWarning(
"SE Component was not initialized with a network.")
# change the configuration of the pp net to avoid auto fusing of buses connected
# through bb switch with elements on each bus if this feature enabled
bus_to_be_fused = None
if fuse_buses_with_bb_switch != 'all' and not self.net.switch.empty:
if isinstance(fuse_buses_with_bb_switch, str):
raise UserWarning(
"fuse_buses_with_bb_switch parameter is not correctly initialized"
)
elif hasattr(fuse_buses_with_bb_switch, '__iter__'):
bus_to_be_fused = fuse_buses_with_bb_switch
set_bb_switch_impedance(self.net, bus_to_be_fused)
self.net, self.ppc, self.eppci = pp2eppci(
self.net,
v_start=v_start,
delta_start=delta_start,
calculate_voltage_angles=calculate_voltage_angles,
zero_injection=zero_injection,
ppc=self.ppc,
eppci=self.eppci)
# Estimate voltage magnitude and angle with the given estimator
self.eppci = self.solver.estimate(self.eppci, **opt_vars)
if self.solver.successful:
self.net = eppci2pp(self.net, self.ppc, self.eppci)
else:
self.logger.warning(
"Estimation failed! Pandapower network failed to update!")
# clear the aux elements and calculation results created for the substitution of bb switches
if fuse_buses_with_bb_switch != 'all' and not self.net.switch.empty:
reset_bb_switch_impedance(self.net)
# if recycle is not wished, reset ppc, ppci
if not self.recycle:
self.ppc, self.eppci = None, None
return self.solver.successful
