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Details for: "buck-boost converter"

Name: buck-boost converter (Key: OK5BV)
Path: ackrep_data/system_models/buck_boost_converter View on GitHub
Type: system_model
Short Description: DC-DC power converter with higher or lower output voltage (inverting topology)
Created: 29.08.2022
Compatible Environment: default_conda_environment (Key: CDAMA)
Source Code [ / ]
import numpy as np
import system_model
from scipy.integrate import solve_ivp

from ackrep_core import ResultContainer
from ackrep_core.system_model_management import save_plot_in_dir
import matplotlib.pyplot as plt
import os
from ipydex import IPS

# link to documentation with examples:

def simulate():
    simulate the system model with scipy.integrate.solve_ivp

    :return: result of solve_ivp, might contains input function

    model = system_model.Model()

    rhs_xx_pp_symb = model.get_rhs_symbolic()
    print("Computational Equations:\n")
    for i, eq in enumerate(rhs_xx_pp_symb):
        print(f"dot_x{i+1} =", eq)

    rhs = model.get_rhs_func()

    # initial state values
    xx0 = [0, 0]

    t_end = 3e-3
    tt = np.linspace(0, t_end, 10000)
    simulation_data = solve_ivp(rhs, (0, t_end), xx0, t_eval=tt)
    simulation_data.U_E = model.params.UE_sf
    simulation_data.u = model.uu_func(0, 0)[0]
    # print(simulation_data.y[:, -1])
    return simulation_data

def save_plot(simulation_data):
    plot your data and save the plot
    access to data via: simulation_data.t   array of time values
                        simulation_data.y   array of data components
                        simulation_data.uu  array of input values

    :param simulation_data: simulation_data of system_model
    :return: None
    # ---------start of edit section--------------------------------------
    # plot of your data
    fig, ax = plt.subplots(2, 1)
    ax[0].plot(simulation_data.t, simulation_data.y[0], label="$x_1=i_L$")
    ax[0].set_title(f"Buck-Boost Converter Simulation, Duty Ratio d={simulation_data.u}")
    ax[1].plot(simulation_data.t, np.ones_like(simulation_data.t) * simulation_data.U_E, label="$U_E$")
    ax[1].plot(simulation_data.t, simulation_data.y[1], label="$x_2=u_C$")
    ax[1].set_xlabel("Time [s]")

    # ---------end of edit section----------------------------------------



def evaluate_simulation(simulation_data):
    assert that the simulation results are as expected

    :param simulation_data: simulation_data of system_model
    # ---------start of edit section--------------------------------------
    # fill in final states of simulation to check your model
    # simulation_data.y[i][-1]
    expected_final_state = [8.99763414, -35.98971576]

    # ---------end of edit section----------------------------------------

    rc = ResultContainer(score=1.0)
    simulated_final_state = simulation_data.y[:, -1]
    rc.final_state_errors = [
        simulated_final_state[i] - expected_final_state[i] for i in np.arange(0, len(simulated_final_state))
    rc.success = np.allclose(expected_final_state, simulated_final_state, rtol=0, atol=1e-2)

    return rc
import sympy as sp
import symbtools as st
import importlib
import sys, os

# from ipydex import IPS, activate_ips_on_exception

from ackrep_core.system_model_management import GenericModel, import_parameters

# Import parameter_file
params = import_parameters()

# link to documentation with examples:

class Model(GenericModel):
    def initialize(self):
        this function is called by the constructor of GenericModel

        :return: None

        # ---------start of edit section--------------------------------------
        # Define number of inputs -- MODEL DEPENDENT
        self.u_dim = 1

        # Set "sys_dim" to constant value, if system dimension is constant
        self.sys_dim = 2

        # ---------end of edit section----------------------------------------

        # check existence of params file
        self.has_params = True
        self.params = params

    # ----------- SET DEFAULT INPUT FUNCTION ---------- #
    # --------------- Only for non-autonomous Systems
    def uu_default_func(self):
        define input function

        :return:(function with 2 args - t, xx_nv) default input function

        # ---------start of edit section--------------------------------------
        def uu_rhs(t, xx_nv):
            sequence of numerical input values

            :param t:(scalar or vector) time
            :param xx_nv:(vector or array of vectors) numeric state vector
            :return:(list) numeric inputs
            D = 0.6  # duty cycle
            return [D]

        # ---------end of edit section----------------------------------------

        return uu_rhs

    # ----------- SYMBOLIC RHS FUNCTION ---------- #

    def get_rhs_symbolic(self):
        define symbolic rhs function

        :return: matrix of symbolic rhs-functions
        if self.dxx_dt_symb is not None:
            return self.dxx_dt_symb

        # ---------start of edit section--------------------------------------
        x1, x2 = self.xx_symb  # state components
        L, C, R, U_E = self.pp_symb  # parameters

        u = self.uu_symb[0]  # inputs

        # define symbolic rhs functions
        dx1_dt = (1 - u) * x2 / L + u * U_E / L
        dx2_dt = -(1 - u) * x1 / C - x2 / R / C

        # rhs functions matrix
        self.dxx_dt_symb = sp.Matrix([dx1_dt, dx2_dt])
        # ---------end of edit section----------------------------------------

        return self.dxx_dt_symb
import sys
import os
import numpy as np
import sympy as sp

import tabulate as tab

# link to documentation with examples:

# set model name
model_name = "boostconverter"

# ---------- create symbolic parameters
pp_symb = [L, C, R, U_E] = sp.symbols("L, C, R, U_E", real=True)

# ---------- create symbolic parameter functions
# parameter values can be constant/fixed values OR set in relation to other parameters (for example: a = 2*b)
C_sf = 20 * 1e-6  # uF
R_sf = 10  # Ohm
L_sf = 180 * 1e-6  # uH
UE_sf = 24  # V

# list of symbolic parameter functions
# tailing "_sf" stands for "symbolic parameter function"
pp_sf = [L_sf, C_sf, R_sf, UE_sf]

#  ---------- list for substitution
# -- entries are tuples like: (independent symbolic parameter, numerical value)
pp_subs_list = []

# OPTONAL: Dictionary which defines how certain variables shall be written
# in the table - key: Symbolic Variable, Value: LaTeX Representation/Code
# useful for example for complex variables: {Z: r"\underline{Z}"}
latex_names = {U_E: r"U_E"}

# ---------- Define LaTeX table

# Define table header
tabular_header = ["Symbol", "Value"]

# Define column text alignments
col_alignment = ["center", "left"]

# Define Entries of all columns before the Symbol-Column
# --- Entries need to be latex code
col_1 = ["Inductiviy", "Capacity", "Resistence", "Input Voltage"]

# contains all lists of the columns before the "Symbol" Column
# --- Empty list, if there are no columns before the "Symbol" Column
start_columns_list = [col_1]

# Define Entries of the columns after the Value-Column
# --- Entries need to be latex code
col_4 = ["H", "F", "$\Omega$", "V"]

# contains all lists of columns after the FIX ENTRIES
# --- Empty list, if there are no columns after the "Value" column
end_columns_list = [col_4]

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