def v(self):
if self._v is None:
self._v = [v1 if v1 is not None and not np.isnan(v1)
else v2
for v1, v2 in zip(self.optional.v, self.fallback.v)]
return self._v
def v(self):
if self._v is None:
self._v = [v1 if not np.isnan(v1)
else v2
for v1, v2 in zip(self.optional.v, self.fallback.v)]
self._v = np.array(self._v)
return self._v
def run(self, **kwargs):
"""
Full Newton-Raphson method.
Returns
-------
bool
convergence status
"""
system = self.system
self.summary()
self.init()
if system.dae.m == 0:
logger.error("Loaded case contains no power flow element.")
system.exit_code = 1
return False
t0, _ = elapsed()
self.niter = 0
while True:
mis = self.nr_step()
logger.info(f'{self.niter}: |F(x)| = {mis:<10g}')
if mis < self.config.tol:
self.converged = True
break
elif self.niter > self.config.max_iter:
break
elif np.isnan(mis).any():
logger.error('NaN found in solution. Convergence not likely')
self.niter = self.config.max_iter + 1
break
elif mis > 1e4 * self.mis[0]:
logger.error('Mismatch increased too fast. Convergence not likely.')
break
self.niter += 1
_, s1 = elapsed(t0)
if not self.converged:
if abs(self.mis[-1] - self.mis[-2]) < self.config.tol:
max_idx = np.argmax(np.abs(system.dae.xy))
name = system.dae.xy_name[max_idx]
logger.error('Mismatch is not correctable possibly due to large load-generation imbalance.')
logger.error(f'Largest mismatch on equation associated with <{name}>')
else:
logger.error(f'Power flow failed after {self.niter + 1} iterations for {system.files.case}.')
else:
logger.info(f'Converged in {self.niter+1} iterations in {s1}.')
if self.config.init_tds:
system.TDS.init()
if self.config.report:
system.PFlow.report()
system.exit_code = 0 if self.converged else 1
return self.converged
def test_init(self):
"""
Test if the TDS initialization is successful.
This function update ``dae.f`` and ``dae.g`` and checks if the residuals
are zeros.
"""
system = self.system
# fg_update is called in TDS.init()
system.j_update(models=system.exist.pflow_tds)
# reset diff. RHS where `check_init == False`
system.dae.f[system.no_check_init] = 0.0
# warn if variables are initialized at limits
if system.config.warn_limits:
for model in system.exist.pflow_tds.values():
for item in model.discrete.values():
item.warn_init_limit()
if np.max(np.abs(system.dae.fg)) < self.config.tol:
logger.debug('Initialization tests passed.')
return True
# otherwise, show suspect initialization error
fail_idx = np.ravel(np.where(abs(system.dae.fg) >= self.config.tol))
nan_idx = np.ravel(np.where(np.isnan(system.dae.fg)))
bad_idx = np.hstack([fail_idx, nan_idx])
fail_names = [system.dae.xy_name[int(i)] for i in fail_idx]
nan_names = [system.dae.xy_name[int(i)] for i in nan_idx]
bad_names = fail_names + nan_names
title = 'Suspect initialization issue! Simulation may crash!'
err_data = {
'Name': bad_names,
'Var. Value': system.dae.xy[bad_idx],
'Eqn. Mismatch': system.dae.fg[bad_idx],
}
tab = Tab(
title=title,
header=err_data.keys(),
data=list(map(list, zip(*err_data.values()))),
)
logger.error(tab.draw())
if system.options.get('verbose') == 1:
breakpoint()
system.exit_code += 1
return False
def check_var(self, *args, **kwargs):
"""
Set the switcher flags based on inputs. Uses cached flags if cache is set to True.
"""
if self.cache and self._eval:
return
for v in self.u.v:
if v not in self.options and not np.isnan(v):
raise ValueError(
f'option {v} is invalid for {self.owner.class_name}.{self.u.name}. '
f'Options are {self.options}.')
for i in range(len(self.options)):
self.__dict__[f's{i}'][:] = np.equal(self.u.v, self.options[i])
self._eval = True
def _implicit_step(self):
"""
Integrate for a single given step.
This function has an internal Newton-Raphson loop for algebraized semi-explicit DAE.
The function returns the convergence status when done but does NOT progress simulation time.
Returns
-------
bool
Convergence status in ``self.converged``.
"""
system = self.system
dae = self.system.dae
self.mis = []
self.niter = 0
self.converged = False
self.x0 = np.array(dae.x)
self.y0 = np.array(dae.y)
self.f0 = np.array(dae.f)
while True:
system.e_clear(models=self.pflow_tds_models)
system.l_update_var(models=self.pflow_tds_models)
system.f_update(models=self.pflow_tds_models)
system.g_update(models=self.pflow_tds_models)
system.l_check_eq(models=self.pflow_tds_models)
system.l_set_eq(models=self.pflow_tds_models)
system.fg_to_dae()
# lazy jacobian update
if dae.t == 0 or self.niter > 3 or (dae.t - self._last_switch_t < 0.2):
system.j_update(models=self.pflow_tds_models)
self.solver.factorize = True
# solve trapezoidal rule integration
In = spdiag([1] * dae.n)
self.Ac = sparse([[In - self.h * 0.5 * dae.fx, dae.gx],
[-self.h * 0.5 * dae.fy, dae.gy]], 'd')
# reset q as well
q = dae.x - self.x0 - self.h * 0.5 * (dae.f + self.f0)
for item in system.antiwindups:
if len(item.x_set) > 0:
for key, val in item.x_set:
np.put(q, key[np.where(item.zi == 0)], 0)
qg = np.hstack((q, dae.g))
inc = self.solver.solve(self.Ac, -matrix(qg))
# check for np.nan first
if np.isnan(inc).any():
logger.error(f'NaN found in solution. Convergence not likely')
self.niter = self.config.max_iter + 1
self.busted = True
break
# reset really small values to avoid anti-windup limiter flag jumps
inc[np.where(np.abs(inc) < 1e-12)] = 0
# set new values
dae.x += np.ravel(np.array(inc[:dae.n]))
dae.y += np.ravel(np.array(inc[dae.n: dae.n + dae.m]))
system.vars_to_models()
# calculate correction
mis = np.max(np.abs(inc))
self.mis.append(mis)
self.niter += 1
# converged
if mis <= self.config.tol:
self.converged = True
break
# non-convergence cases
if self.niter > self.config.max_iter:
logger.debug(f'Max. iter. {self.config.max_iter} reached for t={dae.t:.6f}, '
f'h={self.h:.6f}, mis={mis:.4g} '
f'({system.dae.xy_name[np.argmax(inc)]})')
break
if mis > 1000 and (mis > 1e8 * self.mis[0]):
logger.error(f'Error increased too quickly. Convergence not likely.')
self.busted = True
break
if not self.converged:
dae.x = np.array(self.x0)
dae.y = np.array(self.y0)
dae.f = np.array(self.f0)
system.vars_to_models()
return self.converged
def _itm_step(self):
"""
Integrate with Implicit Trapezoidal Method (ITM) to the current time.
This function has an internal Newton-Raphson loop for algebraized semi-explicit DAE.
The function returns the convergence status when done but does NOT progress simulation time.
Returns
-------
bool
Convergence status in ``self.converged``.
"""
system = self.system
dae = self.system.dae
self.mis = 1
self.niter = 0
self.converged = False
self.x0 = np.array(dae.x)
self.y0 = np.array(dae.y)
self.f0 = np.array(dae.f)
while True:
self._fg_update(models=system.exist.pflow_tds)
# lazy Jacobian update
if dae.t == 0 or \
self.config.honest or \
self.custom_event or \
not self.last_converged or \
self.niter > 4 or \
(dae.t - self._last_switch_t < 0.1):
system.j_update(models=system.exist.pflow_tds)
# set flag in `solver.worker.factorize`, not `solver.factorize`.
self.solver.worker.factorize = True
# `Tf` should remain constant throughout the simulation, even if the corresponding diff. var.
# is pegged by the anti-windup limiters.
# solve implicit trapezoidal method (ITM) integration
self.Ac = sparse([[self.Teye - self.h * 0.5 * dae.fx, dae.gx],
[-self.h * 0.5 * dae.fy, dae.gy]], 'd')
# equation `self.qg[:dae.n] = 0` is the implicit form of differential equations using ITM
self.qg[:dae.n] = dae.Tf * (dae.x - self.x0) - self.h * 0.5 * (dae.f + self.f0)
# reset the corresponding q elements for pegged anti-windup limiter
for item in system.antiwindups:
for key, _, eqval in item.x_set:
np.put(self.qg, key, eqval)
self.qg[dae.n:] = dae.g
if not self.config.linsolve:
inc = self.solver.solve(self.Ac, matrix(self.qg))
else:
inc = self.solver.linsolve(self.Ac, matrix(self.qg))
# check for np.nan first
if np.isnan(inc).any():
self.err_msg = 'NaN found in solution. Convergence is not likely'
self.niter = self.config.max_iter + 1
self.busted = True
break
# reset small values to reduce chattering
inc[np.where(np.abs(inc) < self.tol_zero)] = 0
# set new values
dae.x -= inc[:dae.n].ravel()
dae.y -= inc[dae.n: dae.n + dae.m].ravel()
# store `inc` to self for debugging
self.inc = inc
system.vars_to_models()
# calculate correction
mis = np.max(np.abs(inc))
# store initial maximum mismatch
if self.niter == 0:
self.mis = mis
self.niter += 1
# converged
if mis <= self.config.tol:
self.converged = True
break
# non-convergence cases
if self.niter > self.config.max_iter:
tqdm.write(f'* Max. iter. {self.config.max_iter} reached for t={dae.t:.6f}, '
f'h={self.h:.6f}, mis={mis:.4g} ')
# debug helpers
g_max = np.argmax(abs(dae.g))
inc_max = np.argmax(abs(inc))
self._debug_g(g_max)
self._debug_ac(inc_max)
break
if mis > 1e6 and (mis > 1e6 * self.mis):
self.err_msg = 'Error increased too quickly. Convergence not likely.'
self.busted = True
break
if not self.converged:
dae.x[:] = np.array(self.x0)
dae.y[:] = np.array(self.y0)
dae.f[:] = np.array(self.f0)
system.vars_to_models()
self.last_converged = self.converged
return self.converged
def run(self, **kwargs):
"""
Full Newton-Raphson method.
Returns
-------
bool
convergence status
"""
system = self.system
if self.config.check_conn == 1:
self.system.connectivity()
self.summary()
self.init()
if system.dae.m == 0:
logger.error("Loaded case contains no power flow element.")
system.exit_code = 1
return False
t0, _ = elapsed()
self.niter = 0
while True:
mis = self.nr_step()
logger.info('%d: |F(x)| = %.10g', self.niter, mis)
if mis < self.config.tol:
self.converged = True
break
if self.niter > self.config.max_iter:
break
if np.isnan(mis).any():
logger.error('NaN found in solution. Convergence not likely')
self.niter = self.config.max_iter + 1
break
if mis > 1e4 * self.mis[0]:
logger.error('Mismatch increased too fast. Convergence not likely.')
break
self.niter += 1
_, s1 = elapsed(t0)
if not self.converged:
if abs(self.mis[-1] - self.mis[-2]) < self.config.tol:
max_idx = np.argmax(np.abs(system.dae.xy))
name = system.dae.xy_name[max_idx]
logger.error('Mismatch is not correctable possibly due to large load-generation imbalance.')
logger.error('Largest mismatch on equation associated with <%s>', name)
else:
logger.error('Power flow failed after %d iterations for "%s".', self.niter + 1, system.files.case)
else:
logger.info('Converged in %d iterations in %s.', self.niter + 1, s1)
# make a copy of power flow solutions
self.x_sol = system.dae.x.copy()
self.y_sol = system.dae.y.copy()
if self.config.init_tds:
system.TDS.init()
if self.config.report:
system.PFlow.report()
system.exit_code = 0 if self.converged else 1
return self.converged
def _itm_step(self):
"""
Integrate with Implicit Trapezoidal Method (ITM) to the current time.
This function has an internal Newton-Raphson loop for algebraized semi-explicit DAE.
The function returns the convergence status when done but does NOT progress simulation time.
Returns
-------
bool
Convergence status in ``self.converged``.
"""
system = self.system
dae = self.system.dae
self.mis = 1
self.niter = 0
self.converged = False
self.x0 = np.array(dae.x)
self.y0 = np.array(dae.y)
self.f0 = np.array(dae.f)
while True:
self._fg_update(models=system.exist.pflow_tds)
# lazy Jacobian update
if dae.t == 0 or self.niter > 3 or (dae.t - self._last_switch_t < 0.2):
system.j_update(models=system.exist.pflow_tds)
self.solver.factorize = True
# TODO: set the `Tf` corresponding to the pegged anti-windup limiters to zero.
# Although this should not affect anything since corr. mismatches in `self.qg` are reset to zero
# solve implicit trapezoidal method (ITM) integration
self.Ac = sparse([[self.Teye - self.h * 0.5 * dae.fx, dae.gx],
[-self.h * 0.5 * dae.fy, dae.gy]], 'd')
# equation `self.qg[:dae.n] = 0` is the implicit form of differential equations using ITM
self.qg[:dae.n] = dae.Tf * (dae.x - self.x0) - self.h * 0.5 * (dae.f + self.f0)
# reset the corresponding q elements for pegged anti-windup limiter
for item in system.antiwindups:
for key, val in item.x_set:
np.put(self.qg, key, 0)
self.qg[dae.n:] = dae.g
if not self.config.linsolve:
inc = self.solver.solve(self.Ac, -matrix(self.qg))
else:
inc = self.solver.linsolve(self.Ac, -matrix(self.qg))
# check for np.nan first
if np.isnan(inc).any():
self.err_msg = 'NaN found in solution. Convergence not likely'
self.niter = self.config.max_iter + 1
self.busted = True
break
# reset small values to reduce chattering
inc[np.where(np.abs(inc) < self.tol_zero)] = 0
# set new values
dae.x += inc[:dae.n].ravel()
dae.y += inc[dae.n: dae.n + dae.m].ravel()
system.vars_to_models()
# calculate correction
mis = np.max(np.abs(inc))
if self.niter == 0:
self.mis = mis
self.niter += 1
# converged
if mis <= self.config.tol:
self.converged = True
break
# non-convergence cases
if self.niter > self.config.max_iter:
logger.debug(f'Max. iter. {self.config.max_iter} reached for t={dae.t:.6f}, '
f'h={self.h:.6f}, mis={mis:.4g} ')
# debug helpers
g_max = np.argmax(abs(dae.g))
inc_max = np.argmax(abs(inc))
self._debug_g(g_max)
self._debug_ac(inc_max)
break
if mis > 1000 and (mis > 1e8 * self.mis):
self.err_msg = 'Error increased too quickly. Convergence not likely.'
self.busted = True
break
if not self.converged:
dae.x = np.array(self.x0)
dae.y = np.array(self.y0)
dae.f = np.array(self.f0)
system.vars_to_models()
return self.converged
