def report(self, x_name=None, **kwargs):
"""
Save eigenvalue analysis reports.
Returns
-------
None
"""
if x_name is None:
x_name = self.x_name
text, header, rowname, data = list(), list(), list(), list()
neig = len(self.mu)
mu_real = self.mu.real()
mu_imag = self.mu.imag()
n_positive = sum(1 for x in mu_real if x > 0)
n_zeros = sum(1 for x in mu_real if x == 0)
n_negative = sum(1 for x in mu_real if x < 0)
numeral = []
for idx, item in enumerate(range(neig)):
if mu_real[idx] == 0:
marker = '*'
elif mu_real[idx] > 0:
marker = '**'
else:
marker = ''
numeral.append('#' + str(idx + 1) + marker)
# compute frequency, un-damped frequency and damping
freq = [0] * neig
ufreq = [0] * neig
damping = [0] * neig
for idx, item in enumerate(self.mu):
if item.imag == 0:
freq[idx] = 0
ufreq[idx] = 0
damping[idx] = 0
else:
ufreq[idx] = abs(item) / 2 / pi
freq[idx] = abs(item.imag / 2 / pi)
damping[idx] = -div(item.real, abs(item)) * 100
# obtain most associated variables
var_assoc = []
for prow in range(neig):
temp_row = self.part_fact[prow, :]
name_idx = list(temp_row).index(max(temp_row))
var_assoc.append(x_name[name_idx])
pf = []
for prow in range(neig):
temp_row = []
for pcol in range(neig):
temp_row.append(round(self.part_fact[prow, pcol], 5))
pf.append(temp_row)
# opening info section
text.append(report_info(self.system))
header.append(None)
rowname.append(None)
data.append(None)
text.append('')
header.append([''])
rowname.append(['EIGENVALUE ANALYSIS REPORT'])
data.append('')
text.append('STATISTICS\n')
header.append([''])
rowname.append(['Positives', 'Zeros', 'Negatives'])
data.append([n_positive, n_zeros, n_negative])
text.append('EIGENVALUE DATA\n')
header.append([
'Most Associated', 'Real', 'Imag.', 'Damped Freq.', 'Frequency',
'Damping [%]'
])
rowname.append(numeral)
data.append(
[var_assoc,
list(mu_real),
list(mu_imag), freq, ufreq, damping])
n_cols = 7 # columns per block
n_block = int(ceil(neig / n_cols))
if n_block <= 100:
for idx in range(n_block):
start = n_cols * idx
end = n_cols * (idx + 1)
text.append('PARTICIPATION FACTORS [{}/{}]\n'.format(
idx + 1, n_block))
header.append(numeral[start:end])
rowname.append(x_name)
data.append(pf[start:end])
dump_data(text, header, rowname, data, self.system.files.eig)
logger.info('Report saved to "%s".', self.system.files.eig)
def dump_results(self):
"""
Save eigenvalue analysis reports
Returns
-------
None
"""
system = self.system
mu = self.mu
partfact = self.part_fact
if system.files.no_output:
return
text = []
header = []
rowname = []
data = []
neig = len(mu)
mu_real = mu.real()
mu_imag = mu.imag()
npositive = sum(1 for x in mu_real if x > 0)
nzero = sum(1 for x in mu_real if x == 0)
nnegative = sum(1 for x in mu_real if x < 0)
numeral = []
for idx, item in enumerate(range(neig)):
if mu_real[idx] == 0:
marker = '*'
elif mu_real[idx] > 0:
marker = '**'
else:
marker = ''
numeral.append('#' + str(idx + 1) + marker)
# compute frequency, undamped frequency and damping
freq = [0] * neig
ufreq = [0] * neig
damping = [0] * neig
for idx, item in enumerate(mu):
if item.imag == 0:
freq[idx] = 0
ufreq[idx] = 0
damping[idx] = 0
else:
freq[idx] = abs(item) / 2 / pi
ufreq[idx] = abs(item.imag / 2 / pi)
damping[idx] = -div(item.real, abs(item)) * 100
# obtain most associated variables
var_assoc = []
for prow in range(neig):
temp_row = partfact[prow, :]
name_idx = list(temp_row).index(max(temp_row))
var_assoc.append(system.dae.x_name[name_idx])
pf = []
for prow in range(neig):
temp_row = []
for pcol in range(neig):
temp_row.append(round(partfact[prow, pcol], 5))
pf.append(temp_row)
text.append('')
header.append([''])
rowname.append(['EIGENVALUE ANALYSIS REPORT'])
data.append('')
text.append('STATISTICS\n')
header.append([''])
rowname.append(['Positives', 'Zeros', 'Negatives'])
data.append([npositive, nzero, nnegative])
text.append('EIGENVALUE DATA\n')
header.append([
'Most Associated', 'Real', 'Imag', 'Damped Freq.', 'Frequency',
'Damping [%]'
])
rowname.append(numeral)
data.append(
[var_assoc,
list(mu_real),
list(mu_imag), ufreq, freq, damping])
cpb = 7 # columns per block
nblock = int(ceil(neig / cpb))
if nblock <= 100:
for idx in range(nblock):
start = cpb * idx
end = cpb * (idx + 1)
text.append('PARTICIPATION FACTORS [{}/{}]\n'.format(
idx + 1, nblock))
header.append(numeral[start:end])
rowname.append(system.dae.x_name)
data.append(pf[start:end])
dump_data(text, header, rowname, data, system.files.eig)
logger.info(f'Report saved to <{system.files.eig}>.')
def write(self):
"""
Write report to file.
"""
system = self.system
if system.files.no_output is True:
return
text = list()
header = list()
row_name = list()
data = list()
self.update()
t, _ = elapsed()
# ----------------------------------------
# info section
text.append(self.info)
header.append(None)
row_name.append(None)
data.append(None)
# ----------------------------------------
# ----------------------------------------
# summary section
text.append(['Statistics:\n'])
header.append(None)
row_name.append(self.basic.keys())
data.append(list(self.basic.values()))
# ----------------------------------------
if len(self.extended):
text.append(['EXTENDED SUMMARY:\n'])
header.append(['P (pu)', 'Q (pu)'])
row_name.append(['Generation', 'Load'])
Pcol = [
self.extended['Pg'],
self.extended['Pl'],
]
Qcol = [
self.extended['Qg'],
self.extended['Ql'],
]
data.append([Pcol, Qcol])
if system.PFlow.converged:
# ----------------------------------------
# Bus data
angle_unit = 'deg.' if system.PFlow.config.degree else 'rad.'
angles = np.rad2deg(system.Bus.a.v) \
if system.PFlow.config.degree == 1 else system.Bus.a.v
text.append(['BUS DATA:\n'])
header.append(['Bus Name', 'Vm(pu)', f'Va({angle_unit})'])
row_name.append(system.Bus.idx.v)
data.append([system.Bus.name.v, system.Bus.v.v, angles])
# ----------------------------------------
# ----------------------------------------
# Node data
if hasattr(system, 'Node') and system.Node.n:
text.append(['NODE DATA:\n'])
header.append(['Node Name', 'V(pu)'])
row_name.append(system.Node.idx.v)
data.append([system.Node.name.v, system.Node.v.v])
# ----------------------------------------
# ----------------------------------------
# Line data
text.append(['LINE DATA:\n'])
header.append([
'Line Name', 'Fr. Bus (idx)', 'To Bus (idx)', 'P From (pu)',
'Q From (pu)', 'P To (pu)', 'Q To (pu)'
])
row_name.append(system.Line.idx.v)
data.append([
system.Line.name.v, system.Line.bus1.v, system.Line.bus2.v,
system.Line.a1.e, system.Line.v1.e, system.Line.a2.e,
system.Line.v2.e
])
# ----------------------------------------
# ----------------------------------------
# Additional Algebraic data
text.append(['OTHER ALGEBRAIC VARIABLES:\n'])
header.append([''])
row_name.append(system.dae.y_name[2 * system.Bus.n:system.dae.m])
data.append([round(i, 6) for i in system.dae.y[2 * system.Bus.n:]])
# Additional State variable data
if system.dae.n:
text.append(['OTHER STATE VARIABLES:\n'])
header.append([''])
row_name.append(system.dae.x_name[:])
data.append([round(i, 6) for i in system.dae.x[:]])
dump_data(text, header, row_name, data, system.files.txt)
_, s = elapsed(t)
logger.info(f'Report saved to "{system.files.txt}" in {s}.')
def report(self, x_name=None, **kwargs):
"""
Save eigenvalue analysis reports.
Returns
-------
None
"""
if x_name is None:
x_name = self.x_name
n_states = len(self.mu)
mu_real = self.mu.real
mu_imag = self.mu.imag
freq, ufreq, damping, numeral = self.post_process()
# obtain most associated variables
var_assoc = []
for prow in range(n_states):
temp_row = self.pfactors[prow, :]
name_idx = list(temp_row).index(max(temp_row))
var_assoc.append(x_name[name_idx])
text, header, rowname, data = list(), list(), list(), list()
# opening info section
text.append(report_info(self.system))
header.append(None)
rowname.append(None)
data.append(None)
text.append('')
text.append('EIGENVALUE ANALYSIS REPORT')
header.append([])
rowname.append([])
data.append([])
text.append('STATISTICS\n')
header.append([''])
rowname.append(['Positives', 'Zeros', 'Negatives'])
data.append((self.n_positive, self.n_zeros, self.n_negative))
text.append('EIGENVALUE DATA\n')
header.append([
'Most Associated', 'Real', 'Imag.', 'Damped Freq.', 'Frequency',
'Damping [%]'
])
rowname.append(numeral)
data.append(
[var_assoc,
list(mu_real),
list(mu_imag), freq, ufreq, damping])
n_cols = 7 # columns per block
n_block = int(ceil(n_states / n_cols))
if n_block <= 100:
for idx in range(n_block):
start = n_cols * idx
end = n_cols * (idx + 1)
text.append('PARTICIPATION FACTORS [{}/{}]\n'.format(
idx + 1, n_block))
header.append(numeral[start:end])
rowname.append(x_name)
data.append(self.pfactors[start:end, :])
dump_data(text, header, rowname, data, self.system.files.eig)
logger.info('Report saved to "%s".', self.system.files.eig)
