def get_post_event_volts(sub, splitting_nodes, powernet, curr_FLiER):
"""Simulate a contingency and return the resulting network voltages.
Inputs:
sub (Ring_Substation) - The substation where the contingency occurs.
splitting_nodes (List of ints) - The set of nodes splitting from the
substation.
powernet (Power_Network) - The power network.
curr_FLiER (FLiER_Substation) - The FLiER object that will run the FLiER
algorithm.
Output:
List of 2n doubles. The set of post-contingency voltages. Angles followed
by magnitudes.
"""
split_powernet = split_bus(powernet, sub, splitting_nodes)
V_split = split_powernet.simulate()
new_bus_volt = V_split[-1]
eVs = FLiER_Substation.extend_complex_vector(curr_FLiER.substations,
V_split[:-1], curr_FLiER.en)
eVs = np.hstack([np.angle(eVs), np.abs(eVs)])
for sn in splitting_nodes:
eVs[sub.nodes[sn].eind] = np.angle(new_bus_volt)
eVs[sub.nodes[sn].eind + curr_FLiER.en] = np.abs(new_bus_volt)
eVs = eVs[curr_FLiER.reindr2eindr]
post_event_volts = curr_FLiER.E[:, :2 * curr_FLiER.en].dot(eVs)
split_powernet.prep_for_delete()
return post_event_volts
def get_post_event_volts(sub, splitting_nodes, powernet, curr_FLiER):
"""Simulate a contingency and return the resulting network voltages.
Inputs:
sub (Ring_Substation) - The substation where the contingency occurs.
splitting_nodes (List of ints) - The set of nodes splitting from the
substation.
powernet (Power_Network) - The power network.
curr_FLiER (FLiER_Substation) - The FLiER object that will run the FLiER
algorithm.
Output:
List of 2n doubles. The set of post-contingency voltages. Angles followed
by magnitudes.
"""
split_powernet = split_bus(powernet, sub, splitting_nodes)
V_split = split_powernet.simulate()
new_bus_volt = V_split[-1]
eVs = FLiER_Substation.extend_complex_vector(curr_FLiER.substations, V_split[:-1],
curr_FLiER.en)
eVs = np.hstack([np.angle(eVs), np.abs(eVs)])
for sn in splitting_nodes:
eVs[sub.nodes[sn].eind] = np.angle(new_bus_volt)
eVs[sub.nodes[sn].eind+curr_FLiER.en] = np.abs(new_bus_volt)
eVs = eVs[curr_FLiER.reindr2eindr]
post_event_volts = curr_FLiER.E[:,:2*curr_FLiER.en].dot(eVs)
split_powernet.prep_for_delete()
return post_event_volts
def main(args):
"""Run a test of FLiER.
Parameters are passed to the function in pairs, where args[i] is a flag
and args[i+1] is a parameter value. Valid flag, value combinations are the
following:
-network [filename]: A .cdf or .m file containing the network to be read in.
A .cdf file is in the IEEE Common Data Format (see
https://www.ee.washington.edu/research/pstca/). A .m file is in MATPOWER
format.
-use_filter ["True", "False"]: Whether or not to use the FLiER filtering
procedure. Default is True.
-pmus [int,...,int]: A list of the indices of buses on which to place PMUs.
-test_type ["Full", "Single_Lines"]: If "Full", perform a test of a
substation splitting into two nodes. If "Single_Lines", perform a line
failure test. Default is "Full".
-test_scenarios [**]: A list of the scenarios to test.
-noise [double]: Add noise to pre- and post-event voltage readings. Value
specifies standard deviation of noise. Default is 0.0.
-write_file [filename]: Where to write the output data. Default is "out.txt".
-verbose ["True", "False"]: Turn verbose output to the command-line on or
off. Default is "False".
**test_scenarios format: A semicolon-separated list in which each item is a
scenario. Each item is a substation index, a comma, and then a
space-separated list of substation nodes that split from the substation.
For example, "86,1 2 3;176,1 2;539,7;702,4 5".
"""
print "Running FLiER test with the following options:"
print "\n".join([
"{0}: {1}".format(args[i], args[i + 1])
for i in range(1, len(args), 2)
])
out = read_inputs(args)
(filename, pmus, write_filename, test_type, noise, test_scenarios,
use_filter, verbose) = out
if filename.endswith('.m'):
powernet = PowerNetwork.powernet_from_matpower(filename)
else:
powernet = PowerNetwork.powernet_from_IEEECDF(filename)
# Simulate the pre-event network voltages.
V = powernet.simulate()
pre_event_volts = np.hstack([np.angle(V), np.abs(V)])
curr_FLiER = FLiER_Substation(powernet,
pre_event_volts,
pmus,
verbose=verbose)
solution_ranks = []
filter_ranks = []
num_tests = 0
num_missed = 0
file = open(write_filename, 'w')
# Iterate over test cases. For each case, make the specified change to the
# network and simulate the resulting voltages. Then run FLiER to attempt to
# identify the change that occurred.
for sub in curr_FLiER.substations:
# For each substation, iterate over possible tests. The set of tests
# returned by the iterator depends on test_type.
for splitting_nodes in sub.node_iterator(test_type):
key = (sub.index, tuple(splitting_nodes))
if test_scenarios is not None and key not in test_scenarios:
continue
if verbose:
if test_type == 'Full':
print "On bus {0}, splitting nbrs {1}".format(
sub.index, splitting_nodes)
else:
bab = sub.bus_across_branch(splitting_nodes[0])
print "On line {0}".format((sub.index, bab.index))
try:
# Make change according to test scenario, simulate results.
# Some changes result in simulation nonconvergence. In this
# case, skip the scenario.
post_event_volts = get_post_event_volts(
sub, splitting_nodes, powernet, curr_FLiER)
except RuntimeError as e:
print str(e)
continue
if noise > 0:
# Add noise if called for.
noisy_prev = pre_event_volts.copy()
noisy_prev += np.random.normal(0, noise, len(noisy_prev))
noisy_postv = post_event_volts.copy()
noisy_postv += np.random.normal(0, noise, len(noisy_postv))
noisy_FLiER = FLiER_Substation(powernet, noisy_prev, pmus)
out = noisy_FLiER.find_topology_error(noisy_postv, test_type,
use_filter)
else:
# Run FLiER
out = curr_FLiER.find_topology_error(post_event_volts,
test_type, use_filter)
scores, filter_scores, fraction_ts_computed = out
solkey = (sub.index, tuple(splitting_nodes)) # Solution key
num_tests += 1
if scores is False:
continue
if solkey not in scores:
if verbose:
print "Solution not present"
num_missed += 1
score_of_solution = np.Inf
else:
score_of_solution = scores[solkey]
rank = np.sum(scores.values() <= score_of_solution)
buses_ordered = [
y[0][0] if y[0] != -1 else -1
for y in sorted(scores.items(), key=lambda x: x[1])
]
# bus_rank is the rank of the correct answer if we only care about
# getting the substation index correct and not the specific nodes
# that split.
bus_rank = buses_ordered.index(sub.index) + 1
if score_of_solution == np.Inf:
# Bit of a hack here. Occasionally the filter removes the
# correct answer entirely from the list of possibilities. In
# this case we just set the rank of the solution to something
# large.
rank = 300
bus_rank = 300
if verbose:
print "Solution score: {0}".format(score_of_solution)
print "Solution rank: {0}".format(rank)
print "Correct bus rank: {0}".format(bus_rank)
print "Number scores not filtered: {0}".format(len(scores))
# Update scoring data structures. Write the results of this test to
# the output file.
solution_ranks.append(rank)
sol_filter_score = filter_scores[solkey]
filter_ranks.append(
np.sum(filter_scores.values() <= sol_filter_score))
write_to_file(file, solkey, out, curr_FLiER.substations)
file.close()
print num_tests
print "{0} missed entirely".format(num_missed)
sorted_solranklist, sorted_filtersolranklist = zip(
*sorted(zip(solution_ranks, filter_ranks), key=lambda x: x[0]))
print "Mean solution rank: {0}, Mean filtered solution rank: {1}".format(
np.mean(sorted_solranklist), np.mean(sorted_filtersolranklist))
print "Median sol rank: {0}, median filtered sol rank: {1}".format(
np.median(sorted_solranklist), np.median(sorted_filtersolranklist))
print "Frac correct: {0}, frac filtered correct: {1}".format(
np.sum(np.array(sorted_solranklist) == 1) / float(num_tests),
np.sum(np.array(sorted_filtersolranklist) == 1) / float(num_tests))
print "Frac in top 3: {0}, frac filtered in top 3: {1}".format(
np.sum(np.array(sorted_solranklist) <= 3) / float(num_tests),
np.sum(np.array(sorted_filtersolranklist) <= 3) / float(num_tests))
def main(args):
"""Run a test of FLiER.
Parameters are passed to the function in pairs, where args[i] is a flag
and args[i+1] is a parameter value. Valid flag, value combinations are the
following:
-network [filename]: A .cdf or .m file containing the network to be read in.
A .cdf file is in the IEEE Common Data Format (see
https://www.ee.washington.edu/research/pstca/). A .m file is in MATPOWER
format.
-use_filter ["True", "False"]: Whether or not to use the FLiER filtering
procedure. Default is True.
-pmus [int,...,int]: A list of the indices of buses on which to place PMUs.
-test_type ["Full", "Single_Lines"]: If "Full", perform a test of a
substation splitting into two nodes. If "Single_Lines", perform a line
failure test. Default is "Full".
-test_scenarios [**]: A list of the scenarios to test.
-noise [double]: Add noise to pre- and post-event voltage readings. Value
specifies standard deviation of noise. Default is 0.0.
-write_file [filename]: Where to write the output data. Default is "out.txt".
-verbose ["True", "False"]: Turn verbose output to the command-line on or
off. Default is "False".
**test_scenarios format: A semicolon-separated list in which each item is a
scenario. Each item is a substation index, a comma, and then a
space-separated list of substation nodes that split from the substation.
For example, "86,1 2 3;176,1 2;539,7;702,4 5".
"""
print "Running FLiER test with the following options:"
print "\n".join(["{0}: {1}".format(args[i], args[i+1]) for i in range(1, len(args), 2)])
out = read_inputs(args)
(filename, pmus, write_filename, test_type,
noise, test_scenarios, use_filter, verbose) = out
if filename.endswith('.m'):
powernet = PowerNetwork.powernet_from_matpower(filename)
else:
powernet = PowerNetwork.powernet_from_IEEECDF(filename)
# Simulate the pre-event network voltages.
V = powernet.simulate()
pre_event_volts = np.hstack([np.angle(V), np.abs(V)])
curr_FLiER = FLiER_Substation(powernet, pre_event_volts, pmus, verbose=verbose)
solution_ranks = []
filter_ranks = []
num_tests = 0
num_missed = 0
file = open(write_filename, 'w')
# Iterate over test cases. For each case, make the specified change to the
# network and simulate the resulting voltages. Then run FLiER to attempt to
# identify the change that occurred.
for sub in curr_FLiER.substations:
# For each substation, iterate over possible tests. The set of tests
# returned by the iterator depends on test_type.
for splitting_nodes in sub.node_iterator(test_type):
key = (sub.index, tuple(splitting_nodes))
if test_scenarios is not None and key not in test_scenarios:
continue
if verbose:
if test_type == 'Full':
print "On bus {0}, splitting nbrs {1}".format(sub.index,
splitting_nodes)
else:
bab = sub.bus_across_branch(splitting_nodes[0])
print "On line {0}".format((sub.index, bab.index))
try:
# Make change according to test scenario, simulate results.
# Some changes result in simulation nonconvergence. In this
# case, skip the scenario.
post_event_volts = get_post_event_volts(sub, splitting_nodes,
powernet, curr_FLiER)
except RuntimeError as e:
print str(e)
continue
if noise > 0:
# Add noise if called for.
noisy_prev = pre_event_volts.copy()
noisy_prev += np.random.normal(0, noise, len(noisy_prev))
noisy_postv = post_event_volts.copy()
noisy_postv += np.random.normal(0, noise, len(noisy_postv))
noisy_FLiER = FLiER_Substation(powernet, noisy_prev, pmus)
out = noisy_FLiER.find_topology_error(noisy_postv, test_type,
use_filter)
else:
# Run FLiER
out = curr_FLiER.find_topology_error(post_event_volts, test_type,
use_filter)
scores, filter_scores, fraction_ts_computed = out
solkey = (sub.index, tuple(splitting_nodes)) # Solution key
num_tests += 1
if scores is False:
continue
if solkey not in scores:
if verbose:
print "Solution not present"
num_missed += 1
score_of_solution = np.Inf
else:
score_of_solution = scores[solkey]
rank = np.sum(scores.values() <= score_of_solution)
buses_ordered = [y[0][0] if y[0] != -1 else -1 for y in
sorted(scores.items(), key = lambda x : x[1])]
# bus_rank is the rank of the correct answer if we only care about
# getting the substation index correct and not the specific nodes
# that split.
bus_rank = buses_ordered.index(sub.index) + 1
if score_of_solution == np.Inf:
# Bit of a hack here. Occasionally the filter removes the
# correct answer entirely from the list of possibilities. In
# this case we just set the rank of the solution to something
# large.
rank = 300
bus_rank = 300
if verbose:
print "Solution score: {0}".format(score_of_solution)
print "Solution rank: {0}".format(rank)
print "Correct bus rank: {0}".format(bus_rank)
print "Number scores not filtered: {0}".format(len(scores))
# Update scoring data structures. Write the results of this test to
# the output file.
solution_ranks.append(rank)
sol_filter_score = filter_scores[solkey]
filter_ranks.append(np.sum(filter_scores.values() <= sol_filter_score))
write_to_file(file, solkey, out, curr_FLiER.substations)
file.close()
print num_tests
print "{0} missed entirely".format(num_missed)
sorted_solranklist, sorted_filtersolranklist = zip(*sorted(zip(solution_ranks, filter_ranks), key = lambda x : x[0]))
print "Mean solution rank: {0}, Mean filtered solution rank: {1}".format(np.mean(sorted_solranklist), np.mean(sorted_filtersolranklist))
print "Median sol rank: {0}, median filtered sol rank: {1}".format(np.median(sorted_solranklist), np.median(sorted_filtersolranklist))
print "Frac correct: {0}, frac filtered correct: {1}".format(np.sum(np.array(sorted_solranklist) == 1) / float(num_tests), np.sum(np.array(sorted_filtersolranklist) == 1) / float(num_tests))
print "Frac in top 3: {0}, frac filtered in top 3: {1}".format(np.sum(np.array(sorted_solranklist) <= 3) / float(num_tests), np.sum(np.array(sorted_filtersolranklist) <= 3) / float(num_tests))
