utils

Functions

assert_limits(net, settings)	Check that the network operates within the operation limits and assert an error if not
check_limits(net, settings)	Check that the network operates within the operation limits and print it
check_limits_bool(net, settings)	Check that the network operates within the operation limits and return True(feasible)/False(not feasible)
check_line_current_limits(net[, upper_limit])	Check if the power flow result on the network caused respects the loading limitations in all lines
check_trafo_current_limits(net[, upper_limit])	Check if the power flow result on the network caused respects the loading limitations in all transformers
check_voltage_limits(voltages, upper_limit, ...)	Check if the power flow result on the network caused respects the voltage limitations in all lines
create_result_df(x_flexible, x_non_flexible, ...)	Sve Monte Carlo simulation result on the folder :param x_flexible: feasible P, :type x_flexible: list of floats
fix_missing_point(mat)	Filter in case the sampling of FSP shifts was wrong and left a point behind, it is observed in the power flow
fix_missing_pointsv2(mat)	Filter in case the sampling of FSP shifts was wrong and left a point behind, it is observed in the power flow
fix_net(net)	Fix the initial network structure
get_input_busses_pq(net, input_buses)	Get P and Q of all bus indices in {input_buses} list
get_input_busses_v(net, input_buses)	Get voltage magnitude v and angle θ of all bus indices in {input_buses} list
get_input_lines_pq(net, input_lines)	Get P and Q of all line indices in {input_lines} list
kumaraswamymontecarlo(a, b, c, LB, UB, ...)	Create samples using the Kumaraswamy Monte Carlo distribution
tensor_convolve_nd_torch(image, kernel)	Apply tensor convolution
tensor_convolve_nd_torch_half(image, kernel)	Apply tensor convolution, but with float32 types (might run into issues exceeding the maximum number)
update_pqs(net[, flex_wt, flex_pv, profile, ...])	Update network DG FSP P,Q based on the input values
update_pqs2(net[, flex_dg, profile])	Update network DG FSP P,Q based on the input profile
update_pqs_wl(net[, flex_wt, flex_pv, ...])	Update network FSP P,Q including loads based on the input values
update_pqs_wl2(net, profile[, load_ind, dg_ind])	Update load and distributed generator output values.
update_pqs_wl2_aliander(net, profile[, ...])	Update PQ values using Aliander's PGM
write_conv_result(df, name)	Write results from convolution simulations
write_result(x_flexible, x_non_flexible, ...)	Sve Monte Carlo simulation result on the folder