def get_r(pre_event_volts, Y, branch, unspecified_inds, powerflowpreVY):
"""
Compute r (reduced version) as described in Equations __ and (8) of the paper.
"""
postY = Y.copy()
PowerNetwork.remove_line_from_Y(postY, branch)
r = powerflow(pre_event_volts, postY) - powerflowpreVY
r = r[unspecified_inds]
return r
def single_FLiER_test(power_network, pmus, noise, branch_to_fail, use_filter=True,
pre_event_volts = None, FLiER_type = "Jacobian",
verbose=False):
"""
Run a FLiER test for a specific line failure.
Inputs:
power_network - The power network in which the test will occur.
pmus - A list of bus indices with PMUs.
line_to_fail - The transmission line that will fail.
Outputs:
score_dict - A dictionary with keys that are indices into the 'edges' list
and values that are tij's. The "no line failure" case has
key -1.
ts_computed - The number of tij's that were computed (lines that got past
the filter).
"""
powernet = power_network.copy()
Y = powernet.Y.copy()
try:
if pre_event_volts is None:
V = powernet.simulate()
pre_event_volts = np.hstack([np.angle(V), np.abs(V)])
powernet.remove_branch(branch_to_fail)
V = powernet.simulate()
post_event_volts = np.hstack([np.angle(V), np.abs(V)])
except RuntimeError as e:
# If the solver for the power flow equations fails to converge.
if verbose:
print str(e)
return False, False, False
if noise > 0:
post_event_volts += np.random.normal(0, noise, len(post_event_volts))
# Get the matrix that projects a voltage vector onto the subspace
# rendered observable by the PMUs.
E = PowerNetwork.get_observable_projection_matrix(Y, pmus)
# Run FLiER
if FLiER_type == "Jacobian":
res = FLiER(Y, powernet.branches, pre_event_volts, post_event_volts,
E, powernet.buses, powernet.slack_inds,
use_filter=use_filter, verbose=verbose)
elif FLiER_type == "DC_Approximation":
res = DC_FLiER(powernet.dc_mat, powernet.branches, pre_event_volts,
post_event_volts, E, powernet.slack_inds, verbose=verbose)
else:
assert False
score_dict, initial_score_dict, fraction_ts_computed = res
return score_dict, initial_score_dict, fraction_ts_computed
def split_bus(powernet, sub, splitting_nodes):
"""Create a new Power_Network with a split substation.
Inputs:
powernet (Power_Network) - The original power network.
sub (Ring_Substation) - The substation that will split.
splitting_nodes (List of ints) - The substation nodes that will split from
the substation.
Output:
Power_Network. The new power network that results from the substation
splitting.
"""
new_buses = {bus.ID: bus.copy() for bus in powernet.buses}
newbusID = max([bus.ID for bus in powernet.buses]) + 1
newbusind = len(powernet.buses)
new_bus = Bus(newbusind, newbusID, voltage=sub.voltage)
new_buses[newbusID] = new_bus
old_bus = new_buses[sub.ID]
for node in splitting_nodes:
new_bus.load += sub.nodes[node].load
old_bus.load -= sub.nodes[node].load
new_bus.generation += sub.nodes[node].generation
old_bus.generation -= sub.nodes[node].generation
new_bus.shunt_adm += sub.nodes[node].shunt_adm
old_bus.shunt_adm -= sub.nodes[node].shunt_adm
splitting_branch_IDs = [
sub.branchID_at_node(sn) for sn in splitting_nodes
if sub.branchID_at_node(sn) is not None
]
new_branches = {
branch.ID: branch.copy()
for branch in powernet.branches.values()
}
for branch_ID in splitting_branch_IDs:
branch = new_branches[branch_ID]
assert len(branch.buses) == 2
if branch.buses[0].ID == old_bus.ID:
branch.buses = (new_bus, branch.buses[1])
elif branch.buses[1].ID == old_bus.ID:
branch.buses = (branch.buses[0], new_bus)
else:
assert False
return PowerNetwork(new_buses, new_branches)
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 single_FLiER_test(power_network,
pmus,
noise,
branch_to_fail,
use_filter=True,
pre_event_volts=None,
FLiER_type="Jacobian",
verbose=False):
"""
Run a FLiER test for a specific line failure.
Inputs:
power_network - The power network in which the test will occur.
pmus - A list of bus indices with PMUs.
line_to_fail - The transmission line that will fail.
Outputs:
score_dict - A dictionary with keys that are indices into the 'edges' list
and values that are tij's. The "no line failure" case has
key -1.
ts_computed - The number of tij's that were computed (lines that got past
the filter).
"""
powernet = power_network.copy()
Y = powernet.Y.copy()
try:
if pre_event_volts is None:
V = powernet.simulate()
pre_event_volts = np.hstack([np.angle(V), np.abs(V)])
powernet.remove_branch(branch_to_fail)
V = powernet.simulate()
post_event_volts = np.hstack([np.angle(V), np.abs(V)])
except RuntimeError as e:
# If the solver for the power flow equations fails to converge.
if verbose:
print str(e)
return False, False, False
if noise > 0:
post_event_volts += np.random.normal(0, noise, len(post_event_volts))
# Get the matrix that projects a voltage vector onto the subspace
# rendered observable by the PMUs.
E = PowerNetwork.get_observable_projection_matrix(Y, pmus)
# Run FLiER
if FLiER_type == "Jacobian":
res = FLiER(Y,
powernet.branches,
pre_event_volts,
post_event_volts,
E,
powernet.buses,
powernet.slack_inds,
use_filter=use_filter,
verbose=verbose)
elif FLiER_type == "DC_Approximation":
res = DC_FLiER(powernet.dc_mat,
powernet.branches,
pre_event_volts,
post_event_volts,
E,
powernet.slack_inds,
verbose=verbose)
else:
assert False
score_dict, initial_score_dict, fraction_ts_computed = res
return score_dict, initial_score_dict, fraction_ts_computed
def network_FLiER_test(network_filename,
pmus,
noise,
use_filter=True,
test_branches=None,
FLiER_type="Jacobian",
verbose=False):
"""
Simulate the failure of each line in the network one at a run, and run
FLiER for each case.
Inputs:
network_filename - The path to the file containing the network in IEEE CDF
format.
pmus - A list of bus indices with PMUs on them.
Outputs:
"""
if network_filename.endswith('.m'):
powernet = PowerNetwork.powernet_from_matpower(network_filename)
else:
powernet = PowerNetwork.powernet_from_IEEECDF(network_filename)
num_lines = len(powernet.branches)
solution_ranks = [] # Contains ranks of correct answers
score_list = [] # Contains tij's
fraction_ts_computed_list = [] # How many tij's computed per test.
dict_of_score_dicts = dict()
num_filtered_out = 0
V = powernet.simulate()
pre_event_volts = np.hstack([np.angle(V), np.abs(V)])
if test_branches is None:
test_branches = range(len(powernet.branches))
for i in test_branches:
branch = powernet.branches[i]
if verbose:
print "On line {0}".format([bus.index for bus in branch.buses])
out = single_FLiER_test(powernet,
pmus,
noise,
branch_to_fail=branch,
use_filter=use_filter,
pre_event_volts=pre_event_volts,
FLiER_type=FLiER_type,
verbose=verbose)
score_dict, initial_score_dict, fraction_ts_computed = out
if score_dict == False:
# then power flow equation solver did not converge
continue
fraction_ts_computed_list.append(fraction_ts_computed)
if i in score_dict:
score_of_solution = score_dict[i]
rank = np.sum(score_dict.values() <= score_of_solution)
solution_ranks.append(rank)
else:
# Then the correct line was filtered out.
# Just choose the rank as last, and some large tij
# filler value.
if verbose:
print "Solution filtered out."
score_of_solution = 100
rank = num_lines
solution_ranks.append(rank)
num_filtered_out += 1
dict_of_score_dicts[i] = (score_dict, initial_score_dict,
fraction_ts_computed)
# Produce a list of (key, value) dictionary elements sorted by value.
# Then split that list to create a sorted list of keys and a sorted list of
# values. Recall that the keys are indices into the list of edges.
branch_inds, scores = zip(
*sorted(score_dict.iteritems(), key=lambda x: x[1]))
if verbose:
print score_of_solution, rank, len(score_dict)
score_list.append((scores, score_of_solution))
if verbose:
print "{0} filtered out entirely.".format(num_filtered_out)
return score_list, dict_of_score_dicts, powernet.branches, solution_ranks, fraction_ts_computed_list
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 network_FLiER_test(network_filename, pmus, noise, use_filter=True,
test_branches = None, FLiER_type = "Jacobian",
verbose=False):
"""
Simulate the failure of each line in the network one at a run, and run
FLiER for each case.
Inputs:
network_filename - The path to the file containing the network in IEEE CDF
format.
pmus - A list of bus indices with PMUs on them.
Outputs:
"""
if network_filename.endswith('.m'):
powernet = PowerNetwork.powernet_from_matpower(network_filename)
else:
powernet = PowerNetwork.powernet_from_IEEECDF(network_filename)
num_lines = len(powernet.branches)
solution_ranks = [] # Contains ranks of correct answers
score_list = [] # Contains tij's
fraction_ts_computed_list = [] # How many tij's computed per test.
dict_of_score_dicts = dict()
num_filtered_out = 0
V = powernet.simulate()
pre_event_volts = np.hstack([np.angle(V), np.abs(V)])
if test_branches is None:
test_branches = range(len(powernet.branches))
for i in test_branches:
branch = powernet.branches[i]
if verbose:
print "On line {0}".format([bus.index for bus in branch.buses])
out = single_FLiER_test(powernet, pmus, noise, branch_to_fail=branch,
use_filter=use_filter, pre_event_volts=pre_event_volts,
FLiER_type=FLiER_type, verbose=verbose)
score_dict, initial_score_dict, fraction_ts_computed = out
if score_dict == False:
# then power flow equation solver did not converge
continue
fraction_ts_computed_list.append(fraction_ts_computed)
if i in score_dict:
score_of_solution = score_dict[i]
rank = np.sum(score_dict.values() <= score_of_solution)
solution_ranks.append(rank)
else:
# Then the correct line was filtered out.
# Just choose the rank as last, and some large tij
# filler value.
if verbose:
print "Solution filtered out."
score_of_solution = 100
rank = num_lines
solution_ranks.append(rank)
num_filtered_out += 1
dict_of_score_dicts[i] = (score_dict, initial_score_dict, fraction_ts_computed)
# Produce a list of (key, value) dictionary elements sorted by value.
# Then split that list to create a sorted list of keys and a sorted list of
# values. Recall that the keys are indices into the list of edges.
branch_inds, scores = zip(*sorted(score_dict.iteritems(), key=lambda x: x[1]))
if verbose:
print score_of_solution, rank, len(score_dict)
score_list.append((scores, score_of_solution))
if verbose:
print "{0} filtered out entirely.".format(num_filtered_out)
return score_list, dict_of_score_dicts, powernet.branches, solution_ranks, fraction_ts_computed_list