def run(dataset, sensor_class, coeff_type, N_exp, tau, freq, which_i, k_pcr,
qr, folder, pre_filtering):
"""
Run `N_exp` sensitivity coefficient estimation experiments using Model 1.
Model 1 is the linear model of the form: dx = du S + w.
Parameters
----------
dataset : str
The unique dataset ID (see `meng.constants`).
sensor_class : float
The PMU measurement sensor class (see `meng.constants`).
coeff_type : {'magn', 'real-imag'}
Which sensitivity coefficients to estimate.
N_exp : int
The number of experiments to run.
tau : int
The time window size.
freq : int
How often (in timesteps) to estimate the coefficients).
which_i : list of int or 'all'
Which coefficients to estimate (eg for voltage coefficients, at which
buses). Note: a value of 1 corresponds to the 1st bus after the slack.
k_pcr : int or None
How many principal components to keep in PCR; set to None to skip the
PCR step.
qr : bool
Set to True to solve the LS problem using Q-R decomposition.
folder : str
The name of the experiment folder in which the results are stored.
pre_filtering : bool
Whether to do pre-filtering or not.
"""
is_real_imag = coeff_type == 'real_imag'
# Create the folder in which the experiment results will be stored.
exp_logger = ExperimentLogger(folder, locals())
# Load true load flow.
V_org, I_org, P_org, Q_org, current_idx = load_true_data(dataset)
# Select the noise std that corresponds to the type of coefficient(s).
std_abs = constants.SENSOR_STD_ABS[sensor_class]
std_arg = constants.SENSOR_STD_ANG[sensor_class]
std_real, std_imag = noise.project(np.abs(V_org), std_abs, std_arg)
std_a = std_real[1:] if is_real_imag else std_abs
std_b = std_imag[1:] if is_real_imag else None
# Pre-filtering step.
if pre_filtering:
pf_mask = prefilter(np.abs(V_org), std_abs)
else:
pf_mask = np.ones(V_org.shape[1], dtype=np.bool)
# Extract the list of which coefficients to estimate.
which_i = utils.unpack_which_i(which_i, V_org.shape[0])
# Load the true sensitivity coefficients of interest.
coefficients = load_coefficients(dataset)
S_a_true, S_b_true = utils.coefficients_setup(coefficients, coeff_type,
which_i)
S_a_true = {k: v[:, pf_mask] for k, v in S_a_true.items()}
if S_b_true is not None:
S_b_true = {k: v[:, pf_mask] for k, v in S_b_true.items()}
del coefficients
# Transform voltage phasor measurements into either
# (|x|, Re{x}, or Im{x}) based on `coeff_type`.
X_org_a, X_org_b = utils.dependent_vars_setup(V_org, coeff_type)
# Remove slack bus measurements.
PQ_org = np.vstack((P_org[1:], Q_org[1:]))[:, pf_mask]
X_org_a = X_org_a[1:, pf_mask]
if is_real_imag:
X_org_b = X_org_b[1:, pf_mask]
# Extract the true bias term using the true coefficients and the true power
# injections (from the load flow).
V0_a_org, V0_b_org = {}, {}
for x_i, s_a in S_a_true.items():
v0 = X_org_a[x_i - 1] - np.sum(s_a * PQ_org, axis=0)
V0_a_org[x_i] = v0
if is_real_imag:
s_b = S_b_true[x_i]
v0 = X_org_b[x_i - 1] - np.sum(s_b * PQ_org, axis=0)
# Run `N_exp` experiments (noise realizations).
S_a_all, S_b_all, V_est_all, V_true_coeff_all, V0_a_all, V0_b_all = \
{}, {}, {}, {}, {}, {}
ts, cns_a_all, cns_b_all = [], [], []
for n in range(N_exp):
# Add noise to load flow data.
V_meas, _, P_meas, Q_meas, _, _, _, _, _, _ = \
simulate_noisy_meas(sensor_class, V_org, I_org, current_idx)
# Transform voltage phasor measurements into either
# (|x|, Re{x}, or Im{x}) based on `coeff_type`.
X_meas_a, X_meas_b = utils.dependent_vars_setup(V_meas, coeff_type)
# Remove slack bus measurements and add bias row (last one).
PQ_meas = np.vstack((P_meas[1:], Q_meas[1:]))[:, pf_mask]
PQ_meas = np.vstack((PQ_meas, np.ones(PQ_meas.shape[1])))
X_meas_a = X_meas_a[1:, pf_mask]
if is_real_imag:
X_meas_b = X_meas_b[1:, pf_mask]
else:
dX_meas_b = None
# Estimate the coefficients.
use_sigma = False
S_a, ts, cns_a = linear_model(X_meas_a, PQ_meas, use_sigma, tau, freq,
which_i, k_pcr, qr)
if is_real_imag:
S_b, _, cns_b = linear_model(X_meas_b, PQ_meas, use_sigma, tau,
freq, which_i, k_pcr, qr)
else:
S_b, cns_b = None, None
# Extract the bias terms V0 from the estimated coeefficients.
V0_a = {x_i: v[-1] for x_i, v in S_a.items()}
V0_b = {x_i: v[-1] for x_i, v in S_b.items()} if is_real_imag else None
S_a = {x_i: v[:-1] for x_i, v in S_a.items()}
S_b = {x_i: v[:-1] for x_i, v in S_b.items()} if is_real_imag else None
# Construct estimated |X| (using estimated coefficients).
PQ_meas = PQ_meas[:-1, ts]
V_est = _estimate_Vmagn(PQ_meas, V0_a, V0_b, S_a, S_b, is_real_imag)
# Construct estimated |X| (using true coefficients).
S_a_true_ts = {k: v[:, ts] for k, v in S_a_true.items()}
S_b_true_ts = {k: v[:, ts]
for k, v in S_b_true.items()} if is_real_imag else None
V0_a_org_ts = {k: v[ts] for k, v in V0_a_org.items()}
V0_b_org_ts = {k: v[ts]
for k, v in V0_b_org.items()} if is_real_imag else None
V_true_coeff = _estimate_Vmagn(PQ_meas, V0_a_org_ts, V0_b_org_ts,
S_a_true_ts, S_b_true_ts, is_real_imag)
# Store experiment results.
_add_results_to_dict(S_a, S_a_all)
if is_real_imag:
_add_results_to_dict(S_b, S_b_all)
_add_results_to_dict(V_est, V_est_all)
_add_results_to_dict(V_true_coeff, V_true_coeff_all)
cns_a_all.append(cns_a)
if is_real_imag:
cns_b_all.append(cns_b)
# Compute the mean of the estimated coefficients and predicted dx.
compute_dict_mean = lambda x: {k: np.mean(v, axis=0) for k, v in x.items()}
S_a_mean = compute_dict_mean(S_a_all)
S_b_mean = compute_dict_mean(S_b_all) if is_real_imag else None
V_mean = compute_dict_mean(V_est_all)
V_true_coeff_mean = compute_dict_mean(V_true_coeff_all)
V0_a_mean = compute_dict_mean(V0_a_all)
# Compute the std of the estimated coefficients and predicted dx.
compute_dict_std = lambda x: {k: np.std(v, axis=0) for k, v in x.items()}
S_a_std = compute_dict_std(S_a_all)
S_b_std = compute_dict_std(S_b_all) if is_real_imag else None
V_std = compute_dict_std(V_est_all)
V_true_coeff_std = compute_dict_std(V_true_coeff_all)
V0_b_std = compute_dict_std(V0_b_all)
# Compute the true voltage magnitude deviations (from the load flow).
V_load_flow = np.abs(V_org[1:])
V_load_flow = {i: V_load_flow[i - 1, ts] for i in which_i}
# Compute the mean of the condition numbers.
cns_a_mean = np.mean(cns_a_all, axis=0)
cns_b_mean = np.mean(cns_b_all, axis=0) if cns_b_all else []
# Compute Cramer-Rao lower bound on the coefficient estimations.
crlbs_a, crlbs_b, crlb_cns_a, crlb_cns_b = [], [], [], []
for t in ts:
# Compute the average std over the time window for real-imag coefficients.
if not is_real_imag:
std = std_a
else:
std = np.mean(std_a[:, t - tau:t], axis=1)
std = np.hstack((std, std))
H = PQ_org[:, pf_mask][:, t - tau:t].T
lb, cn = crlb.compute_crlb(H, std)
crlbs_a.append(lb)
crlb_cns_a.append(cn)
if is_real_imag:
std = np.mean(std_b[:, t - tau:t], axis=1)
std = np.hstack((std, std))
lb, cn = crlb.compute_crlb(H, std)
crlbs_b.append(lb)
crlb_cns_b.append(cn)
crlbs_a = np.vstack(crlbs_a).T
if is_real_imag:
crlbs_b = np.vstack(crlbs_b).T
# Keep the true coefficients for the timesteps of interest only.
S_a_true = {k: v[:, ts] for k, v in S_a_true.items()}
S_b_true = {k: v[:, ts]
for k, v in S_b_true.items()} if is_real_imag else {}
# Store numerical results to files.
data = {
'S_a_mean': S_a_mean,
'S_a_std': S_a_std,
'S_b_mean': S_b_mean,
'S_b_std': S_b_std,
'V_mean': V_mean,
'V_std': V_std,
'S_a_true': S_a_true,
'S_b_true': S_b_true,
'V_load_flow': V_load_flow,
'V_true_coeff_mean': V_true_coeff_mean,
'V_true_coeff_std': V_true_coeff_std,
'ts': ts,
'cns_a': cns_a_mean,
'cns_b': cns_b_mean,
'crlb_a': crlbs_a,
'crlb_b': crlbs_b
}
exp_logger.save_data(data, 'results')
#######################################
############## PLOTTING ###############
#######################################
xlabel = 'Time (s)'
# Estimated coefficients (for |.| or Re{.}).
fig, axs = plt.subplots(len(which_i) + 1,
1,
figsize=(10, 2.5 * len(which_i)),
sharex=True)
for ax, x_i in zip(axs[:-1], which_i):
y_true = (S_a_true[x_i][x_i - 1], np.zeros(len(ts)))
y_est = (S_a_mean[x_i][x_i - 1], S_a_std[x_i][x_i - 1])
if not is_real_imag:
labels = [
plotting_labels.magn_coeff(x_i, x_i, 'P', True),
plotting_labels.magn_coeff(x_i, x_i, 'P', False),
]
else:
labels = [
plotting_labels.real_coeff(x_i, x_i, 'P', True),
plotting_labels.real_coeff(x_i, x_i, 'P', False),
]
ylabel = 'Sens. Coeff.'
plotting.shaded_plot(ts, [y_true, y_est],
ylabel=ylabel,
ax=ax,
labels=labels)
ax = axs[-1]
ax.plot(ts, cns_a_mean, label='Condition number')
ax.set_yscale('log')
ax.set_xlabel(xlabel)
ax.set_ylabel('Condition number')
exp_logger.save_figure(fig, 'coefficients_a')
# Estimated coefficients (for Im{.}).
if is_real_imag:
fig, axs = plt.subplots(len(which_i) + 1,
1,
figsize=(10, 2.5 * len(which_i)),
sharex=True)
for ax, x_i in zip(axs[:-1], which_i):
y_true = (S_b_true[x_i][x_i - 1], np.zeros(len(ts)))
y_est = (S_b_mean[x_i][x_i - 1], S_b_std[x_i][x_i - 1])
labels = [
plotting_labels.imag_coeff(x_i, x_i, 'P', True),
plotting_labels.imag_coeff(x_i, x_i, 'P', False),
]
ylabel = 'Sens. Coeff.'
plotting.shaded_plot(ts, [y_true, y_est],
ylabel=ylabel,
ax=ax,
labels=labels)
ax = axs[-1]
ax.plot(ts, cns_b_mean, label='Condition number')
ax.set_yscale('log')
ax.set_xlabel(xlabel)
ax.set_ylabel('Condition number')
exp_logger.save_figure(fig, 'coefficients_b')
# Estimated d|X| (using (a) load flow, (b) true coefficients,
# (c) estimated coefficients).
fig, axs = plt.subplots(len(which_i) + 1,
1,
figsize=(10, 2.5 * len(which_i)),
sharex=True)
for ax, x_i in zip(axs[:-1], which_i):
y_lf = (V_load_flow[x_i], np.zeros(len(ts)))
y_true_coeff = (V_true_coeff_mean[x_i], V_true_coeff_std[x_i])
y_est = (V_mean[x_i], V_std[x_i])
labels = ['Load flow', 'Using true SCs', 'Using est. SCs']
ylabel = r'$|V_{%d}|$' % x_i
plotting.shaded_plot(ts, [y_lf, y_true_coeff, y_est],
labels=labels,
ylabel=ylabel,
ax=ax)
ax = axs[-1]
ax.plot(ts, cns_a_mean, label='Condition number')
ax.set_yscale('log')
ax.set_xlabel(xlabel)
ax.set_ylabel('Condition number')
exp_logger.save_figure(fig, 'X')
# Cramer-Rao lower bound and estimation variance (for |.| or Re{.}).
fig, axs = plt.subplots(len(which_i) + 1,
1,
figsize=(10, 2.5 * len(which_i)),
sharex=True)
for ax, x_i in zip(axs[:-1], which_i):
lb = crlbs_a[x_i - 1]
variance = S_a_std[x_i][x_i - 1]**2
labels = ['CRLB', 'Variance']
ylabel = 'Est. variance'
plotting.single_plot(ts, [lb, variance],
labels=labels,
ax=ax,
ylabel=ylabel)
ax = axs[-1]
ax.plot(ts, crlb_cns_a, label='CRLB condition number')
ax.set_yscale('log')
ax.set_xlabel(xlabel)
ax.set_ylabel('CRLB condition number')
exp_logger.save_figure(fig, 'crlb_a')
# Cramer-Rao lower bound and estimation variance (for Im{.}).
if is_real_imag:
fig, axs = plt.subplots(len(which_i) + 1,
1,
figsize=(10, 2.5 * len(which_i)),
sharex=True)
for ax, x_i in zip(axs[:-1], which_i):
lb = crlbs_b[x_i - 1]
variance = S_b_std[x_i][x_i - 1]**2
labels = ['CRLB', 'Variance']
ylabel = 'Est. variance'
plotting.single_plot(ts, [lb, variance],
labels=labels,
ax=ax,
ylabel=ylabel)
ax = axs[-1]
ax.plot(ts, crlb_cns_b, label='CRLB condition number')
ax.set_yscale('log')
ax.set_xlabel(xlabel)
ax.set_ylabel('CRLB condition number')
exp_logger.save_figure(fig, 'crlb_b')
return
def run(dataset, sensor_class, coeff_type, N_exp, use_sigma, tau, freq, k_nn,
which_i, k_pcr, qr, folder, pre_filtering, epochs):
"""
Run `N_exp` sensitivity coefficient estimation experiments using Model 1.
Model 1 is the linear model of the form: dx = du S + w.
Parameters
----------
dataset : str
The unique dataset ID (see `meng.constants`).
sensor_class : float
The PMU measurement sensor class (see `meng.constants`).
coeff_type : {'magn', 'real-imag'}
Which sensitivity coefficients to estimate.
N_exp : int
The number of experiments to run.
use_sigma : bool
Whether or not to use the correlation matrix :math:`\Sigma`. If False, it
is set to the identity matrix I.
tau : int
The time window size.
freq : int
How often (in timesteps) to estimate the coefficients).
k_nn : int
The number of past timesteps given as input to the neural network.
which_i : list of int or 'all'
Which coefficients to estimate (eg for voltage coefficients, at which
buses). Note: a value of 1 corresponds to the 1st bus after the slack.
k_pcr : int or None
How many principal components to keep in PCR; set to None to skip the
PCR step.
qr : bool
Set to True to solve the LS problem using Q-R decomposition.
folder : str
The name of the experiment folder in which the results are stored.
pre_filtering : bool
Whether to do pre-filtering or not.
epochs : int
The number of epochs during whith to train the neural network.
"""
is_real_imag = coeff_type == 'real_imag'
# Create the folder in which the experiment results will be stored.
exp_logger = ExperimentLogger(folder, locals())
# Load true load flow.
V_org, I_org, P_org, Q_org, current_idx = load_true_data(dataset)
# Select the noise std that corresponds to the type of coefficient(s).
std_abs = constants.SENSOR_STD_ABS[sensor_class]
std_arg = constants.SENSOR_STD_ANG[sensor_class]
std_real, std_imag = noise.project(np.abs(V_org), std_abs, std_arg)
std_a = std_real[1:] if is_real_imag else std_abs
std_b = std_imag[1:] if is_real_imag else None
# Pre-estimation pre-filtering step (doing nothing atm).
pf_mask = np.ones(V_org.shape[1], dtype=np.bool)
# Extract the list of which coefficients to estimate.
which_i = utils.unpack_which_i(which_i, V_org.shape[0])
# Load the true sensitivity coefficients of interest.
coefficients = load_coefficients(dataset)
S_a_true, S_b_true = utils.coefficients_setup(coefficients, coeff_type, which_i)
S_a_true = {k: v[:, pf_mask] for k, v in S_a_true.items()}
if S_b_true is not None:
S_b_true = {k: v[:, pf_mask] for k, v in S_b_true.items()}
del coefficients
# Transform voltage phasor measurements into either
# (|x|, Re{x}, or Im{x}) based on `coeff_type`.
X_org_a, X_org_b = utils.dependent_vars_setup(V_org, coeff_type)
# Remove slack bus measurements and create delta matrices.
dPQ_org = np.vstack((np.diff(P_org[1:], axis=1),
np.diff(Q_org[1:], axis=1)))[:, pf_mask[1:]]
dX_org_a = np.diff(X_org_a[1:], axis=1)[:, pf_mask[1:]]
if is_real_imag:
dX_org_b = np.diff(X_org_b[1:], axis=1)[:, pf_mask[1:]]
# Run `N_exp` experiments (noise realizations).
S_a_all, S_b_all, dV_est_all, dV_true_coeff_all = {}, {}, {}, {}
S_a_nn_all, dV_nn_all, ts_nn = {}, {}, []
ts, cns_a_all, cns_b_all = [], [], []
for n in range(N_exp):
# Add noise to load flow data.
V_meas, _, P_meas, Q_meas, _, _, _, _, _, _ = \
simulate_noisy_meas(sensor_class, V_org, I_org, current_idx)
# Transform voltage phasor measurements into either
# (|x|, Re{x}, or Im{x}) based on `coeff_type`.
X_meas_a, X_meas_b = utils.dependent_vars_setup(V_meas, coeff_type)
# Remove slack bus measurements and create delta matrices.
dPQ_meas = np.vstack((np.diff(P_meas[1:], axis=1),
np.diff(Q_meas[1:], axis=1)))[:, pf_mask[1:]]
dX_meas_a = np.diff(X_meas_a[1:], axis=1)[:, pf_mask[1:]]
if is_real_imag:
dX_meas_b = np.diff(X_meas_b[1:], axis=1)[:, pf_mask[1:]]
else:
dX_meas_b = None
# Pre-filtering step: check which measurements are valid.
if pre_filtering:
valid_timesteps = np.any(dPQ_meas > std_abs, axis=0)
else:
valid_timesteps = np.ones(dPQ_meas.shape[1])
# Estimate the coefficients using linear nn_models.
S_a, ts, cns_a = linear_model(dX_meas_a, dPQ_meas, use_sigma, tau,
freq, which_i, k_pcr, qr, valid_timesteps)
if is_real_imag:
S_b, _, cns_b = linear_model(dX_meas_b, dPQ_meas, use_sigma, tau,
freq, which_i, k_pcr, qr, valid_timesteps)
else:
S_b, cns_b = None, None
# Construct estimated |X| (using estimated coefficients).
dPQ_meas = dPQ_meas[:, ts]
dV_est = _estimate_dVmagn(dPQ_meas, S_a, S_b, is_real_imag)
# Construct estimated |X| (using true coefficients).
S_a_true_ts = {k: v[:, ts] for k, v in S_a_true.items()}
S_b_true_ts = {k: v[:, ts] for k, v in S_b_true.items()} if S_b is not None else None
dV_true_coeff = _estimate_dVmagn(dPQ_meas, S_a_true_ts, S_b_true_ts,
is_real_imag)
# Store experiment results.
_add_results_to_dict(S_a, S_a_all)
# _add_results_to_dict(S_a_nn, S_a_nn_all)
if is_real_imag:
_add_results_to_dict(S_b, S_b_all)
_add_results_to_dict(dV_est, dV_est_all)
_add_results_to_dict(dV_true_coeff, dV_true_coeff_all)
# _add_results_to_dict(dV_nn, dV_nn_all)
cns_a_all.append(cns_a)
if is_real_imag:
cns_b_all.append(cns_b)
# Compute the mean of the estimated coefficients and predicted dx.
compute_dict_mean = lambda x: {k: np.mean(v, axis=0) for k, v in x.items()}
S_a_mean = compute_dict_mean(S_a_all)
S_a_nn_mean = compute_dict_mean(S_a_nn_all)
S_b_mean = compute_dict_mean(S_b_all) if is_real_imag else None
dV_mean = compute_dict_mean(dV_est_all)
dV_true_coeff_mean = compute_dict_mean(dV_true_coeff_all)
dV_nn_mean = compute_dict_mean(dV_nn_all)
# Compute the std of the estimated coefficients and predicted dx.
# compute_dict_std = lambda x: {k: np.std(v, axis=0) for k, v in x.items()}
def _compute_dict_std(d):
answer = {}
for k, v in d.items():
answer[k] = np.std(v, axis=0)
return answer
S_a_std = _compute_dict_std(S_a_all)
S_a_nn_std = _compute_dict_std(S_a_nn_all)
S_b_std = _compute_dict_std(S_b_all) if is_real_imag else None
dV_std = _compute_dict_std(dV_est_all)
dV_true_coeff_std = _compute_dict_std(dV_true_coeff_all)
dV_nn_std = _compute_dict_std(dV_nn_all)
# Compute the true voltage magnitude deviations (from the load flow).
dV_load_flow = np.diff(np.abs(V_org[1:]), axis=1)
dV_load_flow = {i: dV_load_flow[i-1, ts] for i in which_i}
# Compute the mean of the condition numbers.
cns_a_mean = np.mean(cns_a_all, axis=0)
cns_b_mean = np.mean(cns_b_all, axis=0) if cns_b_all else []
# Compute Cramer-Rao lower bound on the coefficient estimations.
crlbs_a, crlbs_b, crlb_cns_a, crlb_cns_b = [], [], [], []
for t in ts:
# Compute the average std over the time window for real-imag coefficients.
if not is_real_imag:
std = std_a
else:
std = np.mean(std_a[:, t-tau: t], axis=1)
std = np.hstack((std, std))
H = dPQ_org[:, pf_mask[1:]][:, t-tau: t].T
lb, cn = crlb.compute_crlb(H, std, use_sigma)
crlbs_a.append(lb)
crlb_cns_a.append(cn)
if is_real_imag:
std = np.mean(std_b[:, t-tau: t], axis=1)
std = np.hstack((std, std))
lb, cn = crlb.compute_crlb(H, std, use_sigma)
crlbs_b.append(lb)
crlb_cns_b.append(cn)
crlbs_a = np.vstack(crlbs_a).T
if is_real_imag:
crlbs_b = np.vstack(crlbs_b).T
# Keep the true coefficients for the timesteps of interest only.
S_a_true = {k: v[:, ts] for k, v in S_a_true.items()}
S_b_true = {k: v[:, ts] for k, v in S_b_true.items()} if is_real_imag else {}
# Store numerical results to files.
data = {
'S_a_mean': S_a_mean,
'S_a_std': S_a_std,
'S_b_mean': S_b_mean,
'S_b_std': S_b_std,
'dV_mean': dV_mean,
'dV_std': dV_std,
'S_a_true': S_a_true,
'S_b_true': S_b_true,
'dV_load_flow': dV_load_flow,
'dV_true_coeff_mean': dV_true_coeff_mean,
'dV_true_coeff_std': dV_true_coeff_std,
'ts': ts,
'cns_a': cns_a_mean,
'cns_b': cns_b_mean,
'crlb_a': crlbs_a,
'crlb_b': crlbs_b,
'S_a_nn_mean': S_a_nn_mean,
'S_a_nn_std': S_a_nn_std,
'dV_nn_mean': dV_nn_mean,
'dV_nn_std': dV_nn_std,
'ts_nn': ts_nn
}
exp_logger.save_data(data, 'results')
#######################################
############## PLOTTING ###############
#######################################
xlabel = 'Time (s)'
# Select timesteps estimated by neural network to plot.
# nn_mask = np.array(ts) - ts_nn[0]
# nn_mask = nn_mask[nn_mask >= 0]
# ts_nn_plot = ts_nn_plot = ts_nn[nn_mask]
# Estimated coefficients (for |.| or Re{.}).
fig, axs = plt.subplots(len(which_i)+1, 1, figsize=(10, 2.5*len(which_i)),
sharex=True)
for ax, x_i in zip(axs[:-1], which_i):
y_true = (S_a_true[x_i][x_i-1], np.zeros(len(ts)))
y_est = (S_a_mean[x_i][x_i-1], S_a_std[x_i][x_i-1])
# y_nn = (S_a_nn_mean[x_i][x_i-1][nn_mask], S_a_nn_std[x_i][x_i-1][nn_mask])
if not is_real_imag:
labels = [plotting_labels.magn_coeff(x_i, x_i, 'P', 'true'),
plotting_labels.magn_coeff(x_i, x_i, 'P', 'M1')]
# plotting_labels.magn_coeff(x_i, x_i, 'P', 'NN')]
else:
labels = [plotting_labels.real_coeff(x_i, x_i, 'P', 'true'),
plotting_labels.real_coeff(x_i, x_i, 'P', 'M1')]
# plotting_labels.real_coeff(x_i, x_i, 'P', 'NN'),]
ylabel = 'Sens. Coeff.'
plotting.shaded_plot([ts, ts], [y_true, y_est],
ylabel=ylabel, ax=ax, labels=labels)
ax = axs[-1]
ax.plot(ts, cns_a_mean, label='Condition number')
ax.set_yscale('log')
ax.set_xlabel(xlabel)
ax.set_ylabel('Condition number')
exp_logger.save_figure(fig, 'coefficients_a')
# Estimated coefficients (for Im{.}).
if is_real_imag:
fig, axs = plt.subplots(len(which_i) + 1, 1,
figsize=(10, 2.5 * len(which_i)), sharex=True)
for ax, x_i in zip(axs[:-1], which_i):
y_true = (S_b_true[x_i][x_i - 1], np.zeros(len(ts)))
y_est = (S_b_mean[x_i][x_i - 1], S_b_std[x_i][x_i - 1])
labels = [plotting_labels.imag_coeff(x_i, x_i, 'P', True),
plotting_labels.imag_coeff(x_i, x_i, 'P', False),]
ylabel = 'Sens. Coeff.'
plotting.shaded_plot(ts, [y_true, y_est], ylabel=ylabel,
ax=ax, labels=labels)
ax = axs[-1]
ax.plot(ts, cns_b_mean, label='Condition number')
ax.set_yscale('log')
ax.set_xlabel(xlabel)
ax.set_ylabel('Condition number')
exp_logger.save_figure(fig, 'coefficients_b')
# Estimated d|X| (using (a) load flow, (b) true coefficients,
# (c) estimated coefficients).
fig, axs = plt.subplots(len(which_i)+1, 1, figsize=(10, 2.5*len(which_i)),
sharex=True)
for ax, x_i in zip(axs[:-1], which_i):
y_lf = (dV_load_flow[x_i], np.zeros(len(ts)))
y_true_coeff = (dV_true_coeff_mean[x_i], dV_true_coeff_std[x_i])
y_est = (dV_mean[x_i], dV_std[x_i])
# y_nn = (dV_nn_mean[x_i][nn_mask], dV_nn_std[x_i][nn_mask])
labels = ['Load flow', 'True SCs', 'M1', 'NN']
ylabel = r'$\Delta|V_{%d}|$' % x_i
plotting.shaded_plot([ts, ts, ts],
[y_lf, y_true_coeff, y_est],
labels=labels, ylabel=ylabel, ax=ax)
ax = axs[-1]
ax.plot(ts, cns_a_mean, label='Condition number')
ax.set_yscale('log')
ax.set_xlabel(xlabel)
ax.set_ylabel('Condition number')
exp_logger.save_figure(fig, 'dV')
# Cramer-Rao lower bound and estimation variance (for |.| or Re{.}).
fig, axs = plt.subplots(len(which_i)+1, 1, figsize=(10, 2.5*len(which_i)),
sharex=True)
for ax, x_i in zip(axs[:-1], which_i):
lb = crlbs_a[x_i-1]
variance = S_a_std[x_i][x_i-1] ** 2
labels = ['CRLB', 'Variance']
ylabel = 'Est. variance'
plotting.single_plot(ts, [lb, variance], labels=labels, ax=ax,
ylabel=ylabel)
ax = axs[-1]
ax.plot(ts, crlb_cns_a, label='CRLB condition number')
ax.set_yscale('log')
ax.set_xlabel(xlabel)
ax.set_ylabel('CRLB condition number')
exp_logger.save_figure(fig, 'crlb_a')
# Cramer-Rao lower bound and estimation variance (for Im{.}).
if is_real_imag:
fig, axs = plt.subplots(len(which_i) + 1, 1,
figsize=(10, 2.5 * len(which_i)),
sharex=True)
for ax, x_i in zip(axs[:-1], which_i):
lb = crlbs_b[x_i - 1]
variance = S_b_std[x_i][x_i - 1] ** 2
labels = ['CRLB', 'Variance']
ylabel = 'Est. variance'
plotting.single_plot(ts, [lb, variance], labels=labels, ax=ax,
ylabel=ylabel)
ax = axs[-1]
ax.plot(ts, crlb_cns_b, label='CRLB condition number')
ax.set_yscale('log')
ax.set_xlabel(xlabel)
ax.set_ylabel('CRLB condition number')
exp_logger.save_figure(fig, 'crlb_b')
plt.show()
return
def run(dataset, sensor_class, which_i, coeff_type, k_nn, epochs, folder,
T_train, freq):
# Create the folder in which the experiment results will be stored.
exp_logger = ExperimentLogger(folder, locals())
# Load true load flow.
V_org, I_org, P_org, Q_org, current_idx = load_true_data(dataset)
T = V_org.shape[1]
# Extract the list of which coefficients to estimate.
which_i = utils.unpack_which_i(which_i, V_org.shape[0])
# Select the noise std that corresponds to the type of coefficient(s).
std_abs = constants.SENSOR_STD_ABS[sensor_class]
std_arg = constants.SENSOR_STD_ANG[sensor_class]
# Transform voltage phasor measurements into either
# (|x|, Re{x}, or Im{x}) based on `coeff_type`.
X_org_a, X_org_b = utils.dependent_vars_setup(V_org, coeff_type)
# Remove slack bus measurements and create delta matrices.
dPQ_org = np.vstack((np.diff(P_org[1:], axis=1),
np.diff(Q_org[1:], axis=1)))
dX_org_a = np.diff(X_org_a[1:], axis=1)
# Add noise to load flow data.
V_meas, _, P_meas, Q_meas, _, _, _, _, _, _ = \
simulate_noisy_meas(sensor_class, V_org, I_org, current_idx)
# Transform voltage phasor measurements into either
# (|x|, Re{x}, or Im{x}) based on `coeff_type`.
X_meas_a, X_meas_b = utils.dependent_vars_setup(V_meas, coeff_type)
# Remove slack bus measurements and create delta matrices.
dPQ_meas = np.vstack((np.diff(P_meas[1:], axis=1),
np.diff(Q_meas[1:], axis=1)))
dX_meas_a = np.diff(X_meas_a[1:], axis=1)
# Load the true sensitivity coefficients of interest.
coefficients = load_coefficients(dataset)
S_a_true, S_b_true = utils.coefficients_setup(coefficients, coeff_type, which_i)
del coefficients
# Compute the true voltage magnitude deviations (from the load flow).
dV_load_flow = np.diff(np.abs(V_org[1:]), axis=1)
# T_plot = 200
t_nn = np.arange(T_train + k_nn, T) # which timesteps the NN estimates.
t_plot = np.arange(T_train + k_nn, T, freq) # which timesteps should be plotted.
t_nn_plot = np.arange(0, T - T_train - k_nn, freq) # the idx of the nn estimations that should be plotted
X_train, X_test = dX_meas_a[:, :T_train], dX_meas_a[:, T_train:]
PQ_train, PQ_test = dPQ_meas[:, :T_train], dPQ_meas[:, T_train:]
fig_1, axs_1 = plt.subplots(len(which_i), 1, sharex=True, figsize=(10, 2.5*len(which_i)))
fig_2, axs_2 = plt.subplots(len(which_i), 1, sharex=True, figsize=(10, 2.5*len(which_i)))
for idx, x_i in enumerate(which_i):
training_dataset = build_training_dataloader(X_train, PQ_train, x_i, k_nn)
in_shape = (dX_meas_a.shape[0] + dPQ_meas.shape[0]) * k_nn
hidden_shapes = [128, 128]
sc_matrix_shape = (dPQ_meas.shape[0])
model = FeedForward(in_shape, hidden_shapes, sc_matrix_shape, k_nn)
train_loss = nn.train(model, training_dataset, lr=1e-3, epochs=epochs, l2=0.)
# Use the neural net to estimate the coefficients for the last 12 hours.
testing_dataset = build_testing_dataloader(X_test, PQ_test, x_i, k_nn)
S_a_nn, dV_nn, dV_nn_true = model.predict(testing_dataset)
# Plot estimated coefficient.
ax = axs_1[idx]
ax.plot(t_plot, S_a_true[x_i][x_i - 1][t_plot], label='True')
ax.plot(t_plot, S_a_nn[x_i - 1][t_nn_plot], label='Neural net')
ax.legend(loc='upper right')
# Plot predicted d|X|.
ax = axs_2[idx]
ax.plot(t_plot, dV_load_flow[x_i-1][t_plot], label='True')
ax.plot(t_plot, dV_nn[t_nn_plot], label='Neural net')
ax.legend(loc='upper right')
exp_logger.save_figure(fig_1, 'coefficients')
exp_logger.save_figure(fig_2, 'dV')
plt.show()
print('Done!')
################### GENERATE NOISY DATA ###################
# Add noise to load flow data.
V_meas, _, P_meas, Q_meas, std_abs, std_ang, std_re, std_im, _, _ = \
simulate_noisy_meas(sensor_class, V_org, I_org, current_idx)
# Remove slack measurements and create measurement matrices used in Model 2.
V_meas = V_meas[1:]
PQ_meas = np.vstack((P_meas[1:], Q_meas[1:]))
################### SELECT TYPE OF COEFFICIENT ###################
V_meas = np.abs(V_meas)
# Load true coefficients.
coefficients = load_coefficients(dataset)
Kp_true = coefficients['vmagn_p'][x_i - 1, x_i - 1]
Kq_true = coefficients['vmagn_q'][x_i - 1, x_i - 1]
################### SPLIT TRAINING, VALIDATION AND TESTING DATA ###################
# Train matrices
V_meas_tr = V_meas[:, ts_train]
PQ_meas_tr = PQ_meas[:, ts_train]
X_train = np.vstack((V_meas_tr, PQ_meas_tr))
# Validation matrices.
V_meas_val = V_meas[:, ts_val]
PQ_meas_val = PQ_meas[:, ts_val]
X_val = np.vstack((V_meas_val, PQ_meas_val))
def run(x_i, sensor_class, seed):
####################### PARAMETERS #######################
# General parameters.
dataset = 'cigre13'
sc_train = sensor_class
# Training (12h), validation (6h), testing (6h) splits.
ts_train = np.arange(0, 12 * 3600)
ts_val = np.arange(12 * 3600, 18 * 3600)
ts_test = np.arange(18 * 3600, 24 * 3600 - 1)
T_train, T_val, T_test = len(ts_train), len(ts_val), len(ts_test)
# Feedforward neural network parameters.
k_nn = 10
nn_epoch_max = 10
hidden_shape = [128, 64]
# LSTM parameters.
k_lstm = 50
hidden_layer_size = 64
lstm_epoch_max = 10
batch_size = 64
# Least squares model.
tau = 1000
freq = 50
####################### SET RANDOM SEED #######################
torch.manual_seed(seed)
np.random.seed(seed)
####################### LOAD TRUE DATA #######################
# Load true load flow.
V_org, I_org, P_org, Q_org, current_idx = load_true_data(dataset)
# Remove slack measurements and create measurement matrices used in Model 2.
V_org_magn = np.abs(V_org[1:])
N, T = V_org_magn.shape
# Create measurement delta matrix to be used as target.
dV_org_magn = np.diff(V_org_magn, axis=1)
################### GENERATE NOISY DATA FOR TRAINING ###################
# Add noise to load flow data.
V_meas, _, P_meas, Q_meas, std_abs, std_ang, std_re, std_im, _, _ = \
simulate_noisy_meas(sc_train, V_org, I_org, current_idx)
# Remove slack measurements and create measurement matrices used in Model 2.
V_meas = V_meas[1:]
PQ_meas = np.vstack((P_meas[1:], Q_meas[1:]))
################### SELECT TYPE OF COEFFICIENT ###################
# Select type of dependent variable.
V_meas = np.abs(V_meas)
# Load true coefficients.
coefficients = load_coefficients(dataset)
Kp_true = coefficients['vmagn_p'][x_i - 1, x_i - 1]
Kq_true = coefficients['vmagn_q'][x_i - 1, x_i - 1]
################### SPLIT TRAINING, VALIDATION AND TESTING DATA ###################
# Train matrices
V_meas_tr = V_meas[:, ts_train]
PQ_meas_tr = PQ_meas[:, ts_train]
X_train = np.vstack((V_meas_tr, PQ_meas_tr))
# Validation matrices.
V_meas_val = V_meas[:, ts_val]
PQ_meas_val = PQ_meas[:, ts_val]
X_val = np.vstack((V_meas_val, PQ_meas_val))
################### PRE-PROCESS DATA ###################
# Normalize training input data.
norm_scaler = MinMaxScaler()
X_train = norm_scaler.fit_transform(X_train.T).T
X_val = norm_scaler.transform(X_val.T).T
################### FEEDFORWARD NEURAL NET ###################
print('Training feedforward neural net...')
in_shape = 3 * N * k_nn
out_shape = 2 * N
# Build training, validation, and test sets.
train_data = nn.build_training_dataloader(X_train, PQ_meas_tr, V_meas_tr, x_i, k_nn)
val_data = nn.build_training_dataloader(X_val, PQ_meas_val, V_meas_val, x_i, k_nn)
# Initialize and train the models.
nn_model = nn.FeedForward(in_shape, hidden_shape, out_shape, k_nn)
nn_model, _ = fc.nn.train(nn_model, train_data, val_data, epochs=nn_epoch_max)
################### LSTM NEURAL NET ###################
print('\nTraining LSTMs...')
in_shape = 3 * N
out_shape = 2 * N
# Build training and validation sets.
train_data = lstm.build_dataloader(X_train, PQ_meas_tr, V_meas_tr, x_i, k_lstm, batch_size)
val_data = lstm.build_dataloader(X_val, PQ_meas_val, V_meas_val, x_i, k_lstm, batch_size)
# Initialize and train the models.
lstm_model = lstm.LSTM(in_shape, hidden_layer_size, out_shape, batch_size)
lstm_model, _ = fc.lstm.train(lstm_model, train_data, val_data, lr=1e-3, epochs=lstm_epoch_max, l2=0.)
for sc_test in [0., 0.2, 0.5, 1.0]:
folder = f'cross_trained_{dataset}_train{sc_train}_test{sc_test}'
logger = ComparisonLogger(folder)
################### GENERATE NOISY DATA FOR TESTING ###################
# Add noise to load flow data.
V_meas, _, P_meas, Q_meas, std_abs, std_ang, std_re, std_im, _, _ = \
simulate_noisy_meas(sc_test, V_org, I_org, current_idx)
# Remove slack measurements and create measurement matrices used in Model 2.
V_meas = V_meas[1:]
PQ_meas = np.vstack((P_meas[1:], Q_meas[1:]))
PQ_meas_bias = np.vstack((PQ_meas, np.ones(T)))
################### SELECT TYPE OF COEFFICIENT ###################
# Select type of dependent variable.
V_meas = np.abs(V_meas)
dPQ_meas = np.diff(PQ_meas, axis=1)
################### SPLIT TESTING DATA ###################
# Testing matrices.
V_meas_test = V_meas[:, ts_test]
PQ_meas_test = PQ_meas[:, ts_test]
X_test = np.vstack((V_meas_test, PQ_meas_test))
PQ_meas_bias_test = PQ_meas_bias[:, ts_test]
dPQ_meas_test = dPQ_meas[:, ts_test]
################### PRE-PROCESS DATA ###################
# Normalize training input data.
X_test = norm_scaler.transform(X_test.T).T
################### INFERENCE WITH PRE-TRAINED MODELS ###################
# Feedforward model.
test_data = nn.build_testing_dataloader(X_test, PQ_meas_test, V_meas_test, x_i, k_nn)
S_nn, y_pred_nn, _ = nn_model.predict(test_data)
ts_nn = np.arange(k_nn-1, T_test-1)
# LSTM model.
test_data = lstm.build_dataloader(X_test, PQ_meas_test, V_meas_test, x_i, k_lstm, batch_size)
S_lstm, y_pred_lstm, _ = lstm.predict(lstm_model, test_data, batch_size)
ts_lstm = np.arange(k_nn-1, T_test-1)
################### LEAST SQUARES MODEL ###################
print('\tLeast squares estimation...')
which_i = np.array([x_i])
valid_timesteps = np.ones(T_test - 1).astype(np.bool)
use_sigma = False
k_pcr = None
qr = False
S_ls, ts_ls, _ = linear.linear_model(V_meas_test, PQ_meas_bias_test, use_sigma,
tau, freq, which_i, k_pcr, qr, valid_timesteps)
# Remove bias terms.
S_ls = {a: b[:-1] for a, b in S_ls.items()}
y_pred_ls = fc.linear.lm_estimate_dVmagn(dPQ_meas_test[:, ts_ls], S_ls, None, False)
S_ls, y_pred_ls = S_ls[x_i], y_pred_ls[x_i]
################### VISUALIZE RESULTS ON TEST SET ###################
ts_all = ts_test[ts_ls]
ts_all_hour = ts_all / 3600
x_nn = ts_ls - k_nn + 1
x_lstm = ts_ls - k_lstm + 1
fig = plt.figure(figsize=(10, 5))
gs = fig.add_gridspec(3, 4, hspace=0.05)
# Plot Kp coefficients.
ax = fig.add_subplot(gs[0, :-1])
ax.plot(ts_all_hour, Kp_true[ts_all], label='True')
ax.plot(ts_all_hour, S_ls[x_i - 1], label='LS')
ax.plot(ts_all_hour, S_nn[x_i - 1, x_nn], label='NN')
ax.plot(ts_all_hour, S_lstm[x_i - 1, x_lstm], label='LSTM')
ax.set_ylabel(r'$\partial |V_{%d}|/\partial P_{%d}$' % (x_i, x_i))
ax.set_xticks([])
# Plot Kq coefficients.
ax = fig.add_subplot(gs[1, :-1])
ax.plot(ts_all_hour, Kq_true[ts_all], label='True')
ax.plot(ts_all_hour, S_ls[x_i - 1 + N], label='LS')
ax.plot(ts_all_hour, S_nn[x_i - 1 + N, x_nn], label='NN')
ax.plot(ts_all_hour, S_lstm[x_i - 1 + N, x_lstm], label='LSTM')
ax.legend(loc='upper right')
ax.set_ylabel(r'$\partial |V_{%d}|/\partial Q_{%d}$' % (x_i, x_i))
ax.set_xticks([])
# Plot dV.
ax = fig.add_subplot(gs[2, :-1])
ax.plot(ts_all_hour[::2], dV_org_magn[x_i - 1, ts_all[::2]], label='True')
ax.plot(ts_all_hour[::2], y_pred_ls[::2], label='LS')
ax.plot(ts_all_hour[::2], y_pred_nn[x_nn[::2]], label='NN')
ax.plot(ts_all_hour[::2], y_pred_lstm[x_lstm[::2]], label='LSTM')
ax.set_ylabel(r'$\Delta |V_{%d}|$' % (x_i))
ax.set_xlabel('Time (h)')
# Plot Kp errors.
ax = fig.add_subplot(gs[0, -1])
e_ls = 100 * norm_e(Kp_true[ts_all], S_ls[x_i - 1])
e_nn = 100 * norm_e(Kp_true[ts_all], S_nn[x_i - 1, x_nn])
e_lstm = 100 * norm_e(Kp_true[ts_all], S_lstm[x_i - 1, x_lstm])
ax.boxplot([e_ls, e_nn, e_lstm], labels=['LS', 'NN', 'LSTM'])
ax.set_xticks([])
# Plot Kq errors.
ax = fig.add_subplot(gs[1, -1])
e_ls = 100 * norm_e(Kq_true[ts_all], S_ls[x_i - 1 + N])
e_nn = 100 * norm_e(Kq_true[ts_all], S_nn[x_i - 1 + N, x_nn])
e_lstm = 100 * norm_e(Kq_true[ts_all], S_lstm[x_i - 1 + N, x_lstm])
ax.boxplot([e_ls, e_nn, e_lstm], labels=['LS', 'NN', 'LSTM'])
ax.set_ylabel('Normalized error [%]')
ax.set_xticks([])
# Plot d|V| errors.
ax = fig.add_subplot(gs[2, -1])
e_ls = 100 * norm_e(dV_org_magn[x_i - 1, ts_all], y_pred_ls)
e_nn = 100 * norm_e(dV_org_magn[x_i - 1, ts_all], y_pred_nn[x_nn])
e_lstm = 100 * norm_e(dV_org_magn[x_i - 1, ts_all], y_pred_lstm[x_lstm])
ax.boxplot([e_ls, e_nn, e_lstm], labels=['LS', 'NN', 'LSTM'], showfliers=False)
gs.tight_layout(fig)
plt.show()
logger.save_fig(fig, f'x_{x_i}_s{seed}.png')
print('Done!')