pydynamicestimator.system

Attributes

`grid_est`
`grid_sim`
`dae_est`
`dae_sim`
`bus_init_sim`
`bus_unknown_est`
`line_sim`
`line_est`
`disturbance_sim`
`disturbance_est`
`device_list_sim`
`device_list_est`

Classes

`Grid`
`GridSim`
`GridEst`
`Dae`
`DaeSim`
`DaeEst`

Functions

stack_volt_power(→ numpy.ndarray)

Module Contents

class pydynamicestimator.system.Grid[source]

y_adm_matrix: numpy.ndarray | None = None

z_imp_matrix: numpy.ndarray | None = None

incident_matrix: numpy.ndarray | None = None

Bsum: numpy.ndarray | None = None

Gsum: numpy.ndarray | None = None

nb: int = 0

nn: int = 0

buses: list = []

Sb: float = 100

idx_i: list = []

idx_j: list = []

idx_i_re: list = []

idx_j_re: list = []

idx_i_im: list = []

idx_j_im: list = []

yinit: dict

yf: dict

sf: dict

line: pydynamicestimator.devices.device.Line | None = None

line_is_faulted: list[bool] = []

line_is_open: list[bool] = []

line_fault_adm: list[float] = []

bus_is_faulted: list[bool] = []

bus_fault_adm: list[float] = []

idx_branch: dict[Tuple[str, str], int]

idx_bus: dict[str, int]

idx_bus_re: dict[str, int]

idx_bus_im: dict[str, int]

C_branches_forward: numpy.ndarray | None = None

C_branches_reverse: numpy.ndarray | None = None

C_branches: numpy.ndarray | None = None

save_data(dae: Dae) → None[source]

Parameters:: dae (Dae)
Return type:: None

init_symbolic(dae: Dae) → None[source]

Parameters:: dae (Dae)
Return type:: None

gcall(dae: Dae) → None[source]

Parameters:: dae (Dae)
Return type:: None

guncall(dae: Dae) → None[source]

Parameters:: dae (Dae)
Return type:: None

add_lines(line: pydynamicestimator.devices.device.Line) → None[source]

Parameters:: line (pydynamicestimator.devices.device.Line)
Return type:: None

add_bus(bus: str, idx: list, idx_re: list, idx_im: list) → None[source]

Parameters:

bus (str)
idx (list)
idx_re (list)
idx_im (list)

Return type:

None

build_y() → None[source]

Return type:: None

get_branch_index(node1: list[str], node2: list[str]) → tuple[numpy.ndarray, numpy.ndarray][source]

Parameters:

() (node2) – List of starting nodes
() – List of receiving nodes
node1 (list[str])
node2 (list[str])

Return type:

tuple[numpy.ndarray, numpy.ndarray]

Returns: The order of the given branch and the order of the opposite direction branch

get_node_index(buses: list) → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]

Parameters:: buses (list)
Return type:: tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

class pydynamicestimator.system.GridSim[source]

Bases: Grid

init_from_power_flow(dae: DaeSim, static: pydynamicestimator.devices.device.BusInit) → None[source]

Parameters:

dae (DaeSim)
static (pydynamicestimator.devices.device.BusInit)

Return type:

None

print_init_power_flow(dae: DaeSim) → None[source]

Parameters:: dae (DaeSim)
Return type:: None

build_Gsum() → None[source]

Return type:: None

build_Bsum() → None[source]

Return type:: None

build_incident_matrix() → None[source]

Return type:: None

setup(dae: DaeSim, bus_init: pydynamicestimator.devices.device.BusInit) → None[source]

Parameters:

dae (DaeSim)
bus_init (pydynamicestimator.devices.device.BusInit)

Return type:

None

class pydynamicestimator.system.GridEst[source]

Bases: Grid

y_simulation = []

_init_from_simulation(other: GridSim, dae: Dae) → None[source]

Parameters:

other (GridSim)
dae (Dae)

Return type:

None

_get_results(other: GridSim) → None[source]

Parameters:: other (GridSim)
Return type:: None

setup(dae: DaeEst, other: GridSim) → None[source]

Parameters:

dae (DaeEst)
other (GridSim)

Return type:

None

init_symbolic(dae: DaeEst) → None[source]

Parameters:: dae (DaeEst)
Return type:: None

class pydynamicestimator.system.Dae[source]

nx: int = 0

ny: int = 0

ng: int = 0

np: int = 0

nts: int = 0

x: casadi.SX | None = None

y: casadi.SX | None = None

f: casadi.SX | None = None

g: casadi.SX | None = None

p: casadi.SX | None = None

p0: casadi.SX | None = None

s: casadi.SX | None = None

x_full: numpy.ndarray | None = None

y_full: numpy.ndarray | None = None

i_full: numpy.ndarray | None = None

T_start: float = 0.0

T_end: float = 10.0

time_steps: numpy.ndarray | None = None

Sb: float = 100

fn: Literal[50, 60] = 50

t: float = 0.02

xinit: list = []

yinit: list = []

iinit: list = []

sinit

xmin: list = []

xmax: list = []

grid: Grid | None = None

incl_lim: bool

FG: casadi.Function | None = None

__reduce__()[source]

__setstate__(state)[source]

setup(**kwargs) → None[source]

Return type:: None

fgcall() → None[source]

Return type:: None

init_symbolic() → None[source]

Return type:: None

dist_load(p: float, q: float, bus: list) → None[source]

Parameters:

p (float)
q (float)
bus (list)

Return type:

None

check_disturbance(dist: pydynamicestimator.devices.device.Disturbance, iter_forward: int) → None[source]

Parameters:

dist (pydynamicestimator.devices.device.Disturbance)
iter_forward (int)

Return type:

None

exec_dist()[source]

class pydynamicestimator.system.DaeSim[source]

Bases: Dae

int_scheme_sim = None

eigenvalues = None

t0: float

tf: float

init_symbolic() → None[source]

Return type:: None

fgcall() → None[source]

Return type:: None

simulate(dist: pydynamicestimator.devices.device.Disturbance) → None[source]

Parameters:: dist (pydynamicestimator.devices.device.Disturbance)
Return type:: None

eigenvalue_analysis() → None[source]

Return type:: None

class pydynamicestimator.system.DaeEst[source]

Bases: Dae

err_msg_est = Multiline-String

Show Value

"""Estimation failed
Possible reasons:
 - Not enough measurements specified
 - Initialization point very bad
 - Estimator diverged from true state
 - Check if the disturbance rendered system unestimable
Possible solutions:
More measurements, less noise, different disturbance, better initialization..."""

nm: int = 0

_schemes

int_scheme: str = 'backward'

kf: float = 0.0

kb: float = 1.0

filter = None

unknown = None

proc_noise_alg: float = 0.0001

proc_noise_diff: float = 0.0001

init_error_diff: float = 1.0

init_error_alg: bool = False

unknown_indices: list = []

known_indices: list = []

err_init: float = 0.001

r_meas_noise_cov_matrix: numpy.ndarray | None = None

r_meas_noise__inv_cov_matrix: numpy.ndarray | None = None

q_proc_noise_diff_cov_matrix: numpy.ndarray | None = None

q_proc_noise_alg_cov_matrix: numpy.ndarray | None = None

q_proc_noise_cov_matrix: numpy.ndarray | None = None

c_meas_matrix: numpy.ndarray | None = None

z_meas_points_matrix: numpy.ndarray | None = None

p_est_init_cov_matrix: numpy.ndarray | None = None

x0: numpy.ndarray | None = None

y0: numpy.ndarray | None = None

s0: numpy.ndarray | None = None

f_func: casadi.Function | None = None

g_func: casadi.Function | None = None

df_dxy_jac: casadi.Function | None = None

dg_dxy_jac: casadi.Function | None = None

inner_tol: float = 1e-06

cov_tol: float = 1e-10

iter_ful: numpy.ndarray | None = None

time_full: numpy.ndarray | None = None

property te

find_unknown_indices(grid: Grid) → None[source]

Parameters:: grid (Grid)
Return type:: None

init_symbolic() → None[source]

Return type:: None

fgcall() → None[source]

Return type:: None

_init_estimate() → None[source]

Return type:: None

estimate(dist: pydynamicestimator.devices.device.Disturbance, **kwargs) → None[source]

Parameters:: dist (pydynamicestimator.devices.device.Disturbance)
Return type:: None

pydynamicestimator.system.grid_est

pydynamicestimator.system.grid_sim

pydynamicestimator.system.dae_est

pydynamicestimator.system.dae_sim

pydynamicestimator.system.bus_init_sim

pydynamicestimator.system.bus_unknown_est

pydynamicestimator.system.line_sim

pydynamicestimator.system.line_est

pydynamicestimator.system.disturbance_sim

pydynamicestimator.system.disturbance_est

pydynamicestimator.system.device_list_sim = []

pydynamicestimator.system.device_list_est = []

pydynamicestimator.system.stack_volt_power(vre, vim) → numpy.ndarray[source]

Return type:: numpy.ndarray