GridCal.Engine.Simulations.Stochastic package¶

Submodules¶

GridCal.Engine.Simulations.Stochastic.blackout_driver module¶

class GridCal.Engine.Simulations.Stochastic.blackout_driver.CascadeType¶

Bases: enum.Enum

An enumeration.

LatinHypercube = 1¶

PowerFlow = (0,)¶

class GridCal.Engine.Simulations.Stochastic.blackout_driver.Cascading(grid: GridCal.Engine.Core.multi_circuit.MultiCircuit, options: GridCal.Engine.Simulations.PowerFlow.power_flow_driver.PowerFlowOptions, triggering_idx=None, max_additional_islands=1, cascade_type_: GridCal.Engine.Simulations.Stochastic.blackout_driver.CascadeType = <CascadeType.LatinHypercube: 1>, n_lhs_samples_=1000)¶

Bases: PySide2.QtCore.QThread

cancel()¶: Cancel the simulation :return:

done_signal = <PySide2.QtCore.Signal object>¶

get_failed_idx()¶: Return the array of all failed branches Returns:

array of all failed branches

get_table()¶: Get DataFrame of the failed elements :return: DataFrame

perform_step_run()¶: Perform only one step cascading Returns:

Nothing

progress_signal = <PySide2.QtCore.Signal object>¶

progress_text = <PySide2.QtCore.Signal object>¶

static remove_elements(circuit: GridCal.Engine.Core.multi_circuit.MultiCircuit, loading_vector, idx=None)¶: Remove branches based on loading Returns:

Nothing

static remove_probability_based(numerical_circuit: GridCal.Engine.Core.numerical_circuit.NumericalCircuit, results: GridCal.Engine.Simulations.Stochastic.monte_carlo_results.MonteCarloResults, max_val, min_prob)¶: Remove branches based on their chance of overload :param numerical_circuit: :param results: :param max_val: :param min_prob: :return: list of indices actually removed

run()¶: Run the monte carlo simulation @return:

staticMetaObject = <PySide2.QtCore.QMetaObject object>¶

class GridCal.Engine.Simulations.Stochastic.blackout_driver.CascadingReportElement(removed_idx, pf_results, criteria)¶: Bases: object

class GridCal.Engine.Simulations.Stochastic.blackout_driver.CascadingResults(cascade_type: GridCal.Engine.Simulations.Stochastic.blackout_driver.CascadeType)¶

Bases: object

get_failed_idx()¶: Return the array of all failed branches Returns:

array of all failed branches

get_table()¶: Get DataFrame of the failed elements :return: DataFrame

plot()¶

GridCal.Engine.Simulations.Stochastic.lhs_driver module¶

class GridCal.Engine.Simulations.Stochastic.lhs_driver.LatinHypercubeSampling(grid: GridCal.Engine.Core.multi_circuit.MultiCircuit, options: GridCal.Engine.Simulations.PowerFlow.power_flow_driver.PowerFlowOptions, sampling_points=1000)¶

Bases: PySide2.QtCore.QThread

cancel()¶: Cancel the simulation

done_signal = <PySide2.QtCore.Signal object>¶

get_steps()¶: Get time steps list of strings

progress_signal = <PySide2.QtCore.Signal object>¶

progress_text = <PySide2.QtCore.Signal object>¶

run()¶: Run the monte carlo simulation @return:

run_multi_thread()¶: Run the monte carlo simulation @return:

run_single_thread()¶: Run the monte carlo simulation @return:

staticMetaObject = <PySide2.QtCore.QMetaObject object>¶

GridCal.Engine.Simulations.Stochastic.monte_carlo_driver module¶

class GridCal.Engine.Simulations.Stochastic.monte_carlo_driver.MonteCarlo(grid: GridCal.Engine.Core.multi_circuit.MultiCircuit, options: GridCal.Engine.Simulations.PowerFlow.power_flow_driver.PowerFlowOptions, mc_tol=0.001, batch_size=100, max_mc_iter=10000)¶

Bases: PySide2.QtCore.QThread

cancel()¶: Cancel the simulation :return:

done_signal = <PySide2.QtCore.Signal object>¶

get_steps()¶: Get time steps list of strings

progress_signal = <PySide2.QtCore.Signal object>¶

progress_text = <PySide2.QtCore.Signal object>¶

run()¶: Run the monte carlo simulation @return:

run_multi_thread()¶: Run the monte carlo simulation @return:

run_single_thread()¶: Run the monte carlo simulation @return:

staticMetaObject = <PySide2.QtCore.QMetaObject object>¶

GridCal.Engine.Simulations.Stochastic.monte_carlo_driver.make_monte_carlo_input(numerical_input_island: GridCal.Engine.Core.calculation_inputs.CalculationInputs)¶: Generate a monte carlo input instance :param numerical_input_island: :return:

GridCal.Engine.Simulations.Stochastic.monte_carlo_input module¶

class GridCal.Engine.Simulations.Stochastic.monte_carlo_input.MonteCarloInput(n, Scdf, Icdf, Ycdf)¶

Bases: object

get_at(x)¶

Get samples at x Args:

x: values in [0, 1] to sample the CDF

Returns: Time series object

GridCal.Engine.Simulations.Stochastic.monte_carlo_results module¶

class GridCal.Engine.Simulations.Stochastic.monte_carlo_results.MonteCarloResults(n, m, p=0)¶

Bases: object

append_batch(mcres)¶: Append a batch (a MonteCarloResults object) to this object @param mcres: MonteCarloResults object @return:

compile()¶: Compiles the final Monte Carlo values by running an online mean and @return:

get_index_loading_cdf(max_val=1.0)¶: Find the elements where the CDF is greater or equal to a velue :param max_val: value to compare :return: indices, associated probability

get_results_dict()¶: Returns a dictionary with the results sorted in a dictionary :return: dictionary of 2D numpy arrays (probably of complex numbers)

get_voltage_sum()¶: Return the voltage summation @return:

open(fname)¶

open pickle Args:

fname: file name

Returns: true if succeeded, false otherwise

plot(result_type: GridCal.Engine.Simulations.result_types.ResultTypes, ax=None, indices=None, names=None)¶: Plot the results :param result_type: :param ax: :param indices: :param names: :return:

query_voltage(power_array)¶

Fantastic function that allows to query the voltage from the sampled points without having to run power flows Args:

power_array: power injections vector

Returns: Interpolated voltages vector

save(fname)¶: Export as pickle

GridCal.Engine.Simulations.Stochastic.reliability_driver module¶

class GridCal.Engine.Simulations.Stochastic.reliability_driver.ReliabilityStudy(circuit: GridCal.Engine.Core.multi_circuit.MultiCircuit, pf_options: GridCal.Engine.Simulations.PowerFlow.power_flow_driver.PowerFlowOptions)¶

Bases: PySide2.QtCore.QThread

cancel()¶

done_signal = <PySide2.QtCore.Signal object>¶

progress_callback(l)¶: Send progress report :param l: lambda value :return: None

progress_signal = <PySide2.QtCore.Signal object>¶

progress_text = <PySide2.QtCore.Signal object>¶

run()¶: run the voltage collapse simulation @return:

staticMetaObject = <PySide2.QtCore.QMetaObject object>¶

GridCal.Engine.Simulations.Stochastic.reliability_driver.get_failure_time(mttf)¶: Get an array of possible failure times :param mttf: mean time to failure

GridCal.Engine.Simulations.Stochastic.reliability_driver.get_reliability_events(horizon, mttf, mttr, tpe: GridCal.Engine.Devices.meta_devices.DeviceType)¶: Get random fail-repair events until a given time horizon in hours :param horizon: maximum horizon in hours :return: list of events, Each event tuple has: (time in hours, element index, activation state (True/False))

GridCal.Engine.Simulations.Stochastic.reliability_driver.get_reliability_scenario(nc: GridCal.Engine.Core.numerical_circuit.NumericalCircuit, horizon=10000)¶

Get reliability events Args:

nc: numerical circuit instance horizon: time horizon in hours

Returns: dictionary of events Each event tuple has: (time in hours, element index, activation state (True/False))

GridCal.Engine.Simulations.Stochastic.reliability_driver.get_repair_time(mttr)¶: Get an array of possible repair times :param mttr: mean time to recovery

GridCal.Engine.Simulations.Stochastic.reliability_driver.run_events(nc: GridCal.Engine.Core.numerical_circuit.NumericalCircuit, events_list: list)¶