def nr_step(self):
"""
Single stepping for Newton Raphson method
Returns
-------
"""
system = self.system
# evaluate discrete, differential, algebraic, and jacobians
system.e_clear()
system.l_update_var()
system.f_update()
system.g_update()
system.l_check_eq()
system.l_set_eq()
system.fg_to_dae()
system.j_update()
# prepare and solve linear equations
self.inc = -matrix([matrix(system.dae.f), matrix(system.dae.g)])
self.A = sparse([[system.dae.fx, system.dae.gx],
[system.dae.fy, system.dae.gy]])
self.inc = self.solver.solve(self.A, self.inc)
system.dae.x += np.ravel(np.array(self.inc[:system.dae.n]))
system.dae.y += np.ravel(np.array(self.inc[system.dae.n:]))
mis = np.max(np.abs(system.dae.fg))
self.mis.append(mis)
system.vars_to_models()
return mis
def reset(self):
"""
Reset internal states to pre-init condition.
"""
self.deltat = 0
self.deltatmin = 0
self.deltatmax = 0
self.h = 0
self.next_pc = 0.1
self.Teye = None
self.qg = np.array([])
self.converged = False
self.last_converged = False
self.busted = False
self.niter = 0
self._switch_idx = 0 # index into `System.switch_times`
self._last_switch_t = -999 # the last event time
self.custom_event = False
self.mis = [1, 1]
self.system.dae.t = np.array(0.0)
self.pbar = None
self.plotter = None
self.plt = None # short name for `plotter`
self.initialized = False
def __init__(self,
u,
mode='step',
delay=0,
name=None,
tex_name=None,
info=None):
Discrete.__init__(self, name=name, tex_name=tex_name, info=info)
if mode not in ('step', 'time'):
raise ValueError(
f'mode {mode} is invalid. Must be in "step" or "time"')
self.u = u
self.mode = mode
self.delay = delay
self.export_flags = ['v']
self.export_flags_tex = ['v']
self.t = np.array([0])
self.v = np.array([0])
self._v_mem = np.zeros((0, 1))
self.rewind = False
self.has_check_var = True
def to_array(self):
"""
Converts field ``v`` to the NumPy array type.
to enable array-based calculation.
Must be called after adding all elements.
Store a copy of original input values to field ``vin``.
Set ``pu_coeff`` to all ones.
Warnings
--------
After this call, `add` will not be allowed to avoid
unexpected issues.
"""
self.v = np.array(self.v, dtype=self.vtype)
# data quality check
# ----------------------------------------
# NOTE: temporarily disabled due to nested parameters
# if np.sum(np.isnan(self.v)) > 0:
# raise ValueError(f'Param <{self.name} contains NaN.')
if self.v.dtype != np.object:
self.v[self.v == np.inf] = 1e8
self.v[self.v == -np.inf] = -1e8
# ----------------------------------------
self.vin = np.array(self.v, dtype=self.vtype)
self.pu_coeff = np.ones_like(self.v, dtype=float)
def __init__(self,
u,
lower,
upper,
enable=True,
name=None,
tex_name=None,
info=None,
no_upper=False,
no_lower=False):
super().__init__(name=name, tex_name=tex_name, info=info)
self.u = u
self.lower = lower
self.upper = upper
self.enable = enable
self.no_upper = no_upper
self.no_lower = no_lower
self.zu = np.array([0])
self.zl = np.array([0])
self.zi = np.array([1])
self.export_flags = ['zi']
self.export_flags_tex = ['z_i']
self.has_check_var = True
if not self.no_lower:
self.export_flags.append('zl')
self.export_flags_tex.append('z_l')
self.warn_flags.append(('zl', 'lower'))
if not self.no_upper:
self.export_flags.append('zu')
self.export_flags_tex.append('z_u')
self.warn_flags.append(('zu', 'upper'))
def to_array(self):
"""
Convert ``v`` to np.ndarray after adding elements.
Store a copy if the input in `vin`.
Set ``pu_coeff`` to all ones.
The conversion enables array-based calculation.
Warnings
--------
After this call, `add` will not be allowed, because data will not be copied over to ``vin``.
"""
# data quality check
# ----------------------------------------
self.v = np.array(self.v, dtype=float)
# NOTE: temporarily disabled due to nested parameters
# if np.sum(np.isnan(self.v)) > 0:
# raise ValueError(f'Param <{self.name} contains NaN.')
self.v[self.v == np.inf] = 1e8
self.v[self.v == -np.inf] = -1e8
# ----------------------------------------
self.vin = np.array(self.v, dtype=float)
self.pu_coeff = np.ones_like(self.v)
def __init__(self,
u,
bound,
equal=False,
enable=True,
name=None,
tex_name=None,
info=None,
cache=False,
z0=0,
z1=1):
super().__init__(name=name, tex_name=tex_name, info=info)
self.u = u
self.bound = dummify(bound)
self.equal: bool = equal
self.enable: bool = enable
self.cache: bool = cache
self._eval: bool = False # if has been eval'ed and cached
self.z0 = np.array([z0]) # negation of `self.z1`
self.z1 = np.array(
[z1]) # if the less-than condition (u < bound) is True
self.export_flags = ['z0', 'z1']
self.export_flags_tex = ['z_0', 'z_1']
self.has_check_var = True
def set_address(self, addr: np.ndarray, contiguous=False):
"""
Set the address of internal variables.
Parameters
----------
addr : np.ndarray
The assigned address for this variable
contiguous : bool, optional
If the addresses are contiguous
"""
self.a = addr
self.n = len(self.a)
# NOT IN USE
self.ae = np.array(self.a)
self.av = np.array(self.a)
# -----------
self._contiguous = contiguous
if self._contiguous:
if self.e_setter is False:
self.e_inplace = True
if self.v_setter is False:
self.v_inplace = True
def __init__(self, system):
self.system = system
self.t = 0
self.ts = DAETimeSeries(self)
self.m, self.n, self.o = 0, 0, 0
self.x, self.y, self.z = np.array([]), np.array([]), np.array([])
self.f, self.g = np.array([]), np.array([])
self.fx = None
self.fy = None
self.gx = None
self.gy = None
self.tx = None
self.rx = None
self.fx_pattern = None
self.fy_pattern = None
self.gx_pattern = None
self.gy_pattern = None
self.tx_pattern = None
self.rx_pattern = None
self.x_name, self.x_tex_name = [], []
self.y_name, self.y_tex_name = [], []
self.z_name, self.z_tex_name = [], []
# ----- indices of sparse matrices -----
self.ifx, self.jfx, self.vfx = list(), list(), list()
self.ify, self.jfy, self.vfy = list(), list(), list()
self.igx, self.jgx, self.vgx = list(), list(), list()
self.igy, self.jgy, self.vgy = list(), list(), list()
self.itx, self.jtx, self.vtx = list(), list(), list()
self.irx, self.jrx, self.vrx = list(), list(), list()
def nr_step(self):
"""
Single step using Newton-Raphson method.
Returns
-------
float
maximum absolute mismatch
"""
system = self.system
# evaluate discrete, differential, algebraic, and Jacobians
system.dae.clear_fg()
system.l_update_var(self.models, niter=self.niter, err=self.mis[-1])
system.s_update_var(self.models)
system.f_update(self.models)
system.g_update(self.models)
system.l_update_eq(self.models)
system.fg_to_dae()
if self.config.method == 'NR':
system.j_update(models=self.models)
elif self.config.method == 'dishonest':
if self.niter < self.config.n_factorize:
system.j_update(self.models)
# prepare and solve linear equations
self.inc = -matrix([matrix(system.dae.f),
matrix(system.dae.g)])
self.A = sparse([[system.dae.fx, system.dae.gx],
[system.dae.fy, system.dae.gy]])
if not self.config.linsolve:
self.inc = self.solver.solve(self.A, self.inc)
else:
self.inc = self.solver.linsolve(self.A, self.inc)
system.dae.x += np.ravel(np.array(self.inc[:system.dae.n]))
system.dae.y += np.ravel(np.array(self.inc[system.dae.n:]))
# find out variables associated with maximum mismatches
fmax = 0
if system.dae.n > 0:
fmax_idx = np.argmax(np.abs(system.dae.f))
fmax = system.dae.f[fmax_idx]
logger.debug("Max. diff mismatch %.10g on %s", fmax, system.dae.x_name[fmax_idx])
gmax_idx = np.argmax(np.abs(system.dae.g))
gmax = system.dae.g[gmax_idx]
logger.debug("Max. algeb mismatch %.10g on %s", gmax, system.dae.y_name[gmax_idx])
mis = max(abs(fmax), abs(gmax))
if self.niter == 0:
self.mis[0] = mis
else:
self.mis.append(mis)
system.vars_to_models()
return mis
def setUp(self):
self.lower = NumParam()
self.upper = NumParam()
self.u = Algeb()
self.upper.v = np.array([2, 2, 2, 2, 2, 2, 2.8, 3.9])
self.u.v = np.array([-3, -1.1, -5, 0, 1, 2, 3, 10])
self.lower.v = np.array([-2, -1, 0.5, 0, 0.5, 1.5, 2, 3])
def linsolve(self, A, b):
"""
Solve linear equation set ``Ax = b`` and returns the solutions in a 1-D array.
This function performs both symbolic and numeric factorizations every time, and can be slower than
``Solver.solve``.
Parameters
----------
A
Sparse matrix
b
RHS of the equation
Returns
-------
The solution in a 1-D np array.
"""
if self.sparselib == 'umfpack':
try:
umfpack.linsolve(A, b)
except ArithmeticError:
logger.error('Singular matrix. Case is not solvable')
return np.ravel(b)
elif self.sparselib == 'klu':
try:
klu.linsolve(A, b)
except ArithmeticError:
logger.error('Singular matrix. Case is not solvable')
return np.ravel(b)
elif self.sparselib in ('spsolve', 'cupy'):
ccs = A.CCS
size = A.size
data = np.array(ccs[2]).reshape((-1,))
indices = np.array(ccs[1]).reshape((-1,))
indptr = np.array(ccs[0]).reshape((-1,))
A = csc_matrix((data, indices, indptr), shape=size)
if self.sparselib == 'spsolve':
x = spsolve(A, b)
return np.ravel(x)
elif self.sparselib == 'cupy':
# delayed import for startup speed
import cupy as cp # NOQA
from cupyx.scipy.sparse import csc_matrix as csc_cu # NOQA
from cupyx.scipy.sparse.linalg.solve import lsqr as cu_lsqr # NOQA
cu_A = csc_cu(A)
cu_b = cp.array(np.array(b).reshape((-1,)))
x = cu_lsqr(cu_A, cu_b)
return np.ravel(cp.asnumpy(x[0]))
def store_switch_times(self, models, eps=1e-4):
"""
Store event switching time in a sorted Numpy array in ``System.switch_times``
and an OrderedDict ``System.switch_dict``.
``System.switch_dict`` has keys as event times and values as the OrderedDict
of model names and instances associated with the event.
Parameters
----------
models : OrderedDict
model name : model instance
eps : float
The small time step size to use immediately before
and after the event
Returns
-------
array-like
self.switch_times
"""
out = np.array([], dtype=np.float)
if self.options.get('flat') is True:
return out
names = []
for instance in models.values():
times = np.array(instance.get_times()).ravel()
out = np.append(out, times)
out = np.append(out, times - eps)
out = np.append(out, times + eps)
names.extend([instance.class_name] * (3 * len(times)))
# sort
sort_idx = np.argsort(out).astype(int)
out = out[sort_idx]
names = [names[i] for i in sort_idx]
# select t > current time
ltzero_idx = np.where(out >= self.dae.t)[0]
out = out[ltzero_idx]
names = [names[i] for i in ltzero_idx]
# make into an OrderedDict with unique keys and model names combined
for i, j in zip(out, names):
if i not in self.switch_dict:
self.switch_dict[i] = {j: self.models[j]}
else:
self.switch_dict[i].update({j: self.models[j]})
self.switch_times = np.array(list(self.switch_dict.keys()))
# self.switch_times = out
self.n_switches = len(self.switch_times)
return self.switch_times
def __init__(self, dae=None):
self.dae = dae
self._data = OrderedDict()
self._z = OrderedDict()
self.t = np.array([])
self.xy = np.array([])
self.z = np.array([])
self.df = None
self.df_z = None
def __init__(
self,
name: Optional[str] = None,
tex_name: Optional[str] = None,
info: Optional[str] = None,
unit: Optional[str] = None,
v_str: Optional[Union[str, float]] = None,
v_iter: Optional[str] = None,
e_str: Optional[str] = None,
discrete: Optional[Discrete] = None,
v_setter: Optional[bool] = False,
e_setter: Optional[bool] = False,
addressable: Optional[bool] = True,
export: Optional[bool] = True,
diag_eps: Optional[float] = 0.0,
):
self.name = name
self.info = info
self.unit = unit
self.tex_name = tex_name if tex_name else name
self.owner = None # instance of the owner Model
self.id = None # variable internal index inside a model (assigned in run time) FIXME: not in use
self.v_str = v_str # equation string (v = v_str) for variable initialization
self.v_iter = v_iter # the implicit equation (0 = v_iter) for iterative initialization
self.e_str = e_str # string for symbolic equation
self.discrete = discrete
self.v_setter = v_setter # True if this variable sets the variable value
self.e_setter = e_setter # True if this var sets the equation value
self.addressable = addressable # True if this var needs to be assigned an address FIXME: not in use
self.export = export # True if this var's value needs to exported
self.diag_eps = diag_eps # small diagonal value (1e-6) to be added to `dae.gy`
# attributes assigned by `set_address`
self.n = 0
self.a: ndarray = np.array([], dtype=int) # address array
self.v: ndarray = np.array([], dtype=np.float) # variable value array
self.e: ndarray = np.array([], dtype=np.float) # equation value array
self.av: ndarray = np.array(
[], dtype=int) # FIXME: future var. address array
self.ae: ndarray = np.array(
[], dtype=int) # FIXME: future equation address array
# internal flags
# NOTE:
# contiguous is only True for internal variables of models with flag ``collate`` equal to ``False``.
self._contiguous = False # True if if address is contiguous to allow slicing into arrays without copy.
self.e_inplace = False # True if `self.e` is in-place access to `System.dae.__dict__[self.e_code]`
self.v_inplace = False # True if `self.v` is in-place access to `System.dae.__dict__[self.v_code]`
self.allow_none = False # True to allow None in address (NOT IN USE)
def link_external(self, ext_model):
"""
Update variable addresses provided by external models
This method sets attributes including `parent_model`,
`parent_instance`, `uid`, `a`, `n`, `e_code` and
`v_code`. It initializes the `e` and `v` to zero.
Returns
-------
None
Parameters
----------
ext_model : Model
Instance of the parent model
"""
self.parent = ext_model
if isinstance(ext_model, GroupBase):
if self.indexer.n > 0 and isinstance(self.indexer.v[0],
(list, np.ndarray)):
self._n = [len(i) for i in self.indexer.v
] # number of elements in each sublist
self._idx = np.concatenate(
[np.array(i) for i in self.indexer.v])
else:
self._n = [len(self.indexer.v)]
self._idx = self.indexer.v
self.a = ext_model.get(src=self.src, idx=self._idx,
attr='a').astype(int)
self.n = len(self.a)
self.v = np.zeros(self.n)
self.e = np.zeros(self.n)
else:
original_var = ext_model.__dict__[self.src]
if self.indexer is not None:
uid = ext_model.idx2uid(self.indexer.v)
else:
uid = np.arange(ext_model.n, dtype=int)
self._n = [len(uid)]
if len(uid) > 0:
self.a = original_var.a[uid]
else:
self.a = np.array([], dtype=int)
# set initial v and e values to zero
self.n = len(self.a)
self.v = np.zeros(self.n)
self.e = np.zeros(self.n)
def run(self, **kwargs):
succeed = False
system = self.system
self.singular_idx = np.array([], dtype=int)
if system.PFlow.converged is False:
logger.warning(
'Power flow not solved. Eig analysis will not continue.')
return succeed
else:
if system.TDS.initialized is False:
system.TDS.init()
system.TDS._itm_step()
if system.dae.n == 0:
logger.error('No dynamic model. Eig analysis will not continue.')
else:
if sum(system.dae.Tf != 0) != len(system.dae.Tf):
self.singular_idx = np.argwhere(np.equal(
system.dae.Tf, 0.0)).ravel().astype(int)
logger.info(
f"System contains {len(self.singular_idx)} zero time constants. "
)
logger.debug([system.dae.x_name[i] for i in self.singular_idx])
self.x_name = np.array(system.dae.x_name)
self.x_name = np.delete(self.x_name, self.singular_idx)
self.summary()
t1, s = elapsed()
self.calc_state_matrix()
self.remove_singular_rc()
self.calc_part_factor()
if not self.system.files.no_output:
self.report()
if system.options.get('state_matrix') is True:
self.export_state_matrix()
if self.config.plot:
self.plot()
_, s = elapsed(t1)
logger.info('Eigenvalue analysis finished in {:s}.'.format(s))
succeed = True
system.exit_code = 0 if succeed else 1
return succeed
def __init__(self, dae=None):
self.dae = dae
# internal dict storage
self._xy = OrderedDict()
self._z = OrderedDict()
self.t = np.array([])
self.xy = np.array([]).reshape((-1, 1))
self.z = np.array([]).reshape((-1, 1))
# data frame members
self.df = None
self.df_z = None
def store_sparse_pattern(self,
models: Optional[Union[str, List,
OrderedDict]] = None):
models = self._get_models(models)
self._call_models_method('store_sparse_pattern', models)
# add variable jacobian values
for j_name in self.dae.jac_name:
ii, jj, vv = list(), list(), list()
# for `gy` matrix, always make sure the diagonal is reserved
# It is a safeguard if the modeling user omitted the diagonal
# term in the equations
if j_name == 'gy':
ii.extend(np.arange(self.dae.m))
jj.extend(np.arange(self.dae.m))
vv.extend(np.zeros(self.dae.m))
# logger.debug(f'Jac <{j_name}>, row={ii}')
for mdl in models.values():
row_idx = mdl.row_of(f'{j_name}')
col_idx = mdl.col_of(f'{j_name}')
# logger.debug(f'Model <{name}>, row={row_idx}')
ii.extend(row_idx)
jj.extend(col_idx)
vv.extend(np.zeros(len(np.array(row_idx))))
# add the constant jacobian values
for row, col, val in mdl.zip_ijv(f'{j_name}c'):
ii.extend(row)
jj.extend(col)
if isinstance(val, (float, int)):
vv.extend(val * np.ones(len(row)))
elif isinstance(val, (list, np.ndarray)):
vv.extend(val)
else:
raise TypeError(
f'Unknown type {type(val)} in constant jacobian {j_name}'
)
if len(ii) > 0:
ii = np.array(ii).astype(int)
jj = np.array(jj).astype(int)
vv = np.array(vv).astype(float)
self.dae.store_sparse_ijv(j_name, ii, jj, vv)
self.dae.build_pattern(j_name)
def nr_step(self):
"""
Single step using Newton-Raphson method.
Returns
-------
float
maximum absolute mismatch
"""
system = self.system
# evaluate discrete, differential, algebraic, and Jacobians
system.dae.clear_fg()
system.l_update_var(self.models, niter=self.niter, err=self.mis[-1])
system.s_update_var(self.models)
system.f_update(self.models)
system.g_update(self.models)
system.l_update_eq(self.models)
system.fg_to_dae()
if self.config.method == 'NR':
system.j_update(models=self.models)
elif self.config.method == 'dishonest':
if self.niter < self.config.n_factorize:
system.j_update(self.models)
# prepare and solve linear equations
self.inc = -matrix([matrix(system.dae.f), matrix(system.dae.g)])
self.A = sparse([[system.dae.fx, system.dae.gx],
[system.dae.fy, system.dae.gy]])
if not self.config.linsolve:
self.inc = self.solver.solve(self.A, self.inc)
else:
self.inc = self.solver.linsolve(self.A, self.inc)
system.dae.x += np.ravel(np.array(self.inc[:system.dae.n]))
system.dae.y += np.ravel(np.array(self.inc[system.dae.n:]))
mis = np.max(np.abs(system.dae.fg))
if self.niter == 0:
self.mis[0] = mis
else:
self.mis.append(mis)
system.vars_to_models()
return mis
def __init__(self, system=None, config=None):
super().__init__(system, config)
self.config.add(
OrderedDict((
('tol', 1e-6),
('t0', 0.0),
('tf', 20.0),
('fixt', 1),
('shrinkt', 1),
('tstep', 1 / 30),
('max_iter', 15),
)))
self.config.add_extra(
"_help",
tol="convergence tolerance",
t0="simulation starting time",
tf="simulation ending time",
fixt="use fixed step size (1) or variable (0)",
shrinkt='shrink step size for fixed method if not converged',
tstep='the initial step step size',
max_iter='maximum number of iterations',
)
self.config.add_extra(
"_alt",
tol="float",
t0=">=0",
tf=">t0",
fixt=(0, 1),
shrinkt=(0, 1),
tstep='float',
max_iter='>=10',
)
# overwrite `tf` from command line
if system.options.get('tf') is not None:
self.config.tf = system.options.get('tf')
# to be computed
self.deltat = 0
self.deltatmin = 0
self.deltatmax = 0
self.h = 0
self.next_pc = 0
self.eye = None
self.Teye = None
self.qg = np.array([])
self.tol_zero = self.config.tol / 100
# internal status
self.converged = False
self.busted = False
self.err_msg = ''
self.niter = 0
self._switch_idx = 0 # index into `System.switch_times`
self._last_switch_t = -999 # the last critical time
self.mis = 1
self.pbar = None
self.callpert = None
self.plotter = None
self.plt = None
self.initialized = False
def unpack(self, df=False):
"""
Unpack stored data in `_xy` and `_z` into arrays `t`, `xy`, and `z`.
Parameters
----------
df : bool
True to construct DataFrames `self.df` and `self.df_z` (time-consuming).
"""
if df is True:
self.df = pd.DataFrame.from_dict(self._xy,
orient='index',
columns=self.dae.xy_name)
self.t = self.df.index.to_numpy()
self.xy = self.df.to_numpy()
self.df_z = pd.DataFrame.from_dict(self._z,
orient='index',
columns=self.dae.z_name)
self.z = self.df_z.to_numpy()
else:
n_steps = len(self._xy)
self.t = np.array(list(self._xy.keys()))
self.xy = np.zeros((n_steps, self.dae.m + self.dae.n))
self.z = np.zeros((n_steps, self.dae.o))
for idx, xy in enumerate(self._xy.values()):
self.xy[idx, :] = xy
for idx, z in enumerate(self._z.values()):
self.z[idx, :] = z
def __init__(self, u, center, lower, upper, enable=True):
"""
"""
super().__init__(u, lower, upper, enable=enable)
self.center = center
# default state if not enabled
self.zi = np.array([0.])
self.zl = np.array([0.])
self.zu = np.array([0.])
self.zur = np.array([0.])
self.zlr = np.array([0.])
self.export_flags = ['zl', 'zi', 'zu', 'zur', 'zlr']
self.export_flags_tex = ['z_l', 'z_i', 'z_u', 'z_ur', 'z_lr']
def __init__(self,
u,
lower,
upper,
enable=True,
no_lower=False,
no_upper=False,
lower_cond=None,
upper_cond=None,
name=None,
tex_name=None,
info=None):
Discrete.__init__(self, name=name, tex_name=tex_name, info=info)
self.u = u
self.rate_lower = dummify(lower)
self.rate_upper = dummify(upper)
# `lower_cond` and `upper_cond` are arrays of 0/1 indicating whether
# the corresponding rate limit should be *enabled*.
# 0 - disabled, 1 - enabled.
# If is `None`, all rate limiters will be enabled.
self.rate_lower_cond = dummify(lower_cond)
self.rate_upper_cond = dummify(upper_cond)
self.rate_no_lower = no_lower
self.rate_no_upper = no_upper
self.enable = enable
self.zur = np.array([0])
self.zlr = np.array([0])
self.has_check_eq = True
# Note: save ops by not calculating `zir`
# self.zir = np.array([1])
# self.export_flags = ['zir']
# self.export_flags_tex = ['z_{ir}']
if not self.rate_no_lower:
self.export_flags.append('zlr')
self.export_flags_tex.append('z_{lr}')
self.warn_flags.append(('zlr', 'lower'))
if not self.rate_no_upper:
self.export_flags.append('zur')
self.export_flags_tex.append('z_{ur}')
self.warn_flags.append(('zur', 'upper'))
class System(object):
"""
New power system class
"""
def __init__(self,
case: Optional[str] = None,
name: Optional[str] = None,
config_path: Optional[str] = None,
options: Optional[Dict] = None,
**kwargs):
self.name = name
self.options = {} if options is None else options
if kwargs:
self.options.update(kwargs)
self.calls = OrderedDict()
self.models = OrderedDict()
self.groups = OrderedDict()
self.programs = OrderedDict()
self.switch_times = np.array([])
# get and load default config file
self.config = Config(self.__class__.__name__)
self._config_path = get_config_path(
) if not config_path else config_path
self._config_from_file = self.load_config(self._config_path)
self.config.load(self._config_from_file)
# custom configuration for system goes after this line
self.config.add(
OrderedDict((
('freq', 60),
('mva', 100),
('store_z', 0),
)))
self.files = FileMan()
self.files.set(case=case, **self.options)
self.dae = DAE(system=self)
# dynamic imports: routine import need to query model flags
self._group_import()
self._model_import()
self._routine_import()
self._models_with_flag = {
'pflow': self.get_models_with_flag('pflow'),
'tds': self.get_models_with_flag('tds'),
'pflow_and_tds': self.get_models_with_flag(('tds', 'pflow')),
}
# ------------------------------
# FIXME: reduce clutter with defaultdict `adders` and `setters`, each with `x`, `y`, `f`, and `g`
self.f_adders, self.f_setters = list(), list()
self.g_adders, self.g_setters = list(), list()
self.x_adders, self.x_setters = list(), list()
self.y_adders, self.y_setters = list(), list()
self.antiwindups = list()
def __init__(self, dae=None):
self.dae = dae
# internal dict storage
self._data = OrderedDict()
self._z = OrderedDict()
self.t = np.array([])
self.xy = np.array([])
self.z = np.array([])
# data frame members
self.df = None
self.df_z = None
# flags
self._need_unpack = True
def __init__(self, system, config):
ModelData.__init__(self)
self.bus = IdxParam(
model='Bus',
info="linked bus idx",
mandatory=True,
)
self.tf = TimerParam(
info='Fault start time for the bus',
mandatory=True,
callback=self.apply_fault,
)
self.tc = TimerParam(
info='Fault end time for the bus',
callback=self.clear_fault,
)
self.xf = NumParam(
info='Fault to ground impedance',
default=1e-4,
tex_name='x_f',
)
self.rf = NumParam(
info='Fault to ground resistance',
default=0,
tex_name='x_f',
)
Model.__init__(self, system, config)
self.flags.update({'tds': True})
self.group = 'TimedEvent'
self.gf = ConstService(
tex_name='g_{f}',
v_str='re(1/(rf + 1j * xf))',
)
self.bf = ConstService(
tex_name='b_{f}',
v_str='im(1/(rf + 1j * xf))',
)
self.uf = ConstService(
tex_name='u_f',
v_str='0',
)
self.a = ExtAlgeb(
model='Bus',
src='a',
indexer=self.bus,
tex_name=r'\theta',
e_str='u * uf * (v ** 2 * gf)',
)
self.v = ExtAlgeb(
model='Bus',
src='v',
indexer=self.bus,
tex_name=r'V',
e_str='u * uf * (v ** 2 * bf)',
)
self._vstore = np.array([])
def __init__(self,
v_str: Optional[str] = None,
v_numeric: Optional[Callable] = None,
vtype: Optional[type] = None,
name: Optional[str] = None, tex_name=None, info=None):
super().__init__(name=name, vtype=vtype, tex_name=tex_name, info=info)
self.v_str = v_str
self.v_numeric = v_numeric
self.v: Union[float, int, ndarray] = np.array([0.])
def init(self, routine):
"""
Set values for the very first time step.
"""
Model.init(self, routine)
self.apply_exact(np.array(self.system.TDS.config.t0))
logger.debug('<%s>: Initialization done', self.class_name)
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
