{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DC-DC Buck converter \n",
    "\n",
    "The goal is to compare the SIMBA simulation results against theory calculation for this topology.\n",
    "\n",
    "Let's compare the output voltage and rms current through the inductance.\n",
    "\n",
    "# SIMBA circuit\n",
    "![buck](fig/buck.png)\n",
    "\n",
    "\n",
    "\n",
    "Simulation settings : \n",
    "* predictive time step (variable) \n",
    "* minimum time step: 1E-06\n",
    "* simulation time: we use the \"stop at steady state\" function\n",
    "\n",
    "\n",
    "# Theory calculation\n",
    "\n",
    "* Output voltage\n",
    "\n",
    "$ V_{out} =  V_{in} \\times D $\n",
    "\n",
    "with:\n",
    "\n",
    "V_in = 50\n",
    "\n",
    "D = duty cycle = 0.5 \n",
    "\n",
    "* Inductance RMS current\n",
    "\n",
    "$ I_{ind,rms} = \\sqrt{\\frac{(Iind,moy)² + (deltaIl)²}{12}}  $\n",
    "\n",
    "with \n",
    "\n",
    "R = 5\n",
    "\n",
    "L = 0.001\n",
    "\n",
    "fswitch = 5000\n",
    "\n",
    "$ Iind,moy = \\frac{D \\times Vin}{R} $\n",
    "\n",
    "$ deltaIl= \\frac{D \\times (1-D) \\times Vin}{L*fswitch} $\n",
    "\n",
    "\n",
    "### Run Simulation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load modules\n",
    "from aesim.simba import JsonProjectRepository\n",
    "from math import *\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Theory Output voltage for continous conduction =  25.0\n",
      "Theory Inductance current rms =  5.0518148554092255\n"
     ]
    }
   ],
   "source": [
    "#Theory calculation for output average voltage and rms current through the inductance\n",
    "\n",
    "Vinput = 50\n",
    "Duty=0.5\n",
    "Vo_th = Vinput * Duty\n",
    "print (\"Theory Output voltage for continous conduction = \", Vo_th)\n",
    "\n",
    "R_th = 5\n",
    "L_th = 0.001\n",
    "fswitch_th = 5000\n",
    "Iinductance_moy = (Duty*Vinput)/R_th\n",
    "deltaIl= Duty * (1-Duty) * Vinput/(L_th*fswitch_th)\n",
    "Iind_rms_th = sqrt((Iinductance_moy*Iinductance_moy) + (deltaIl*deltaIl)/12)\n",
    "print (\"Theory Inductance current rms = \", Iind_rms_th)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load functions\n",
    "\n",
    "# get steady state signals\n",
    "def steadystate_signal(horizon_time, time, *signals):\n",
    "    \"\"\"steadystate_signal() returns time ndarray and a list of signals on the horizon_time\"\"\"\n",
    "\n",
    "    steadystate_maskarray = np.ma.where(time > time[-1] - horizon_time)\n",
    "    steadystate_time = time[steadystate_maskarray]\n",
    "    steadystate_signal_list = [signal[steadystate_maskarray] \n",
    "for signal in signals]\n",
    "    return steadystate_time, *steadystate_signal_list\n",
    "\n",
    "# calculate average value\n",
    "def average_value(time, waveform):\n",
    "    \"\"\"average_value() returns the average value of a time waveform equal time steps are not required\"\"\"\n",
    "\n",
    "    cum_sum = 0\n",
    "    range_idx = range(0, len(time)-1, 1)\n",
    "\n",
    "    for idx in range_idx:\n",
    "        cum_sum += (time[idx + 1] - time[idx]) * (waveform[idx+1] + waveform[idx]) /2\n",
    "    return (1 / (time[-1] - time[0]) * cum_sum)\n",
    "\n",
    "# calculate rms value\n",
    "def rms_value(time, waveform):\n",
    "    \"\"\"rms_value() returns the rms value of a time waveform equal time steps are not required\"\"\"\n",
    "\n",
    "    cum_sum = 0\n",
    "    range_idx = range(0, len(time)-1, 1)\n",
    "\n",
    "    for idx in range_idx:\n",
    "        cum_sum += (time[idx + 1] - time[idx]) * (waveform[idx+1]**2 + waveform[idx]**2) /2\n",
    "    return (np.sqrt(1 / (time[-1] - time[0]) * cum_sum))\n",
    "\n",
    "# plot histogram\n",
    "def plot_bar(Tab1 = [], \n",
    "        Tab2 = [], \n",
    "        largeur_barre = 0.3,\n",
    "        Etiquette = [],\n",
    "        FigAxe = \"ax1\",\n",
    "        show = False,\n",
    "        plot = plt, Tab1_abscisse = [], \n",
    "        dxticks = 0, mxticks = 2, \n",
    "        xlim = [], ylim = [],\n",
    "        color_tab1 = 'orange',\n",
    "        Legend = ['SIMBA', 'PSIM'],\n",
    "        ylabel='Average or rms values'):\n",
    "        \"\"\"\n",
    "        Tab1: donnée SIMBA\n",
    "        Tab2: Donnée PSIM\n",
    "        largeur_barre = 0.3 # Largeur de chaque barre :\n",
    "        \"\"\"\n",
    "\n",
    "        if Tab1_abscisse == []:\n",
    "                Tab1_abscisse = range(len(Tab1)) # Position des barres de la categorie 1\n",
    "        \n",
    "        if Tab2 != []:\n",
    "                Tab2_abscisse = [i + largeur_barre for i in Tab1_abscisse] # Position des barres de la cat 2\n",
    "\n",
    "        plot.bar(Tab1_abscisse, Tab1, width = largeur_barre, color = color_tab1, # Barres cat 1\n",
    "                edgecolor = 'black', linewidth = 2)\n",
    "        \n",
    "        if Tab2 != []:\n",
    "                plot.bar(Tab2_abscisse, Tab2, width = largeur_barre, color = 'yellow', # Barres cat 2\n",
    "                        edgecolor = ['black' for i in Tab1], linewidth = 2)\n",
    "\n",
    "        plot.xticks([dxticks+r + largeur_barre / mxticks for r in range(len(Tab1))], # Etiquettes\n",
    "                Etiquette)\n",
    "\n",
    "        FigAxe.set_ylabel(ylabel)\n",
    "\n",
    "        if ylim !=[]:\n",
    "                plt.ylim(ylim)\n",
    "        if xlim !=[]:\n",
    "                plt.xlim(xlim)\n",
    "\n",
    "        if Tab2 != []:\n",
    "            plot.legend(Legend)\n",
    "\n",
    "        if show == True :\n",
    "                plot.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== 0 Error(s), 0 Warning(s) ==========\n",
      "Job duration: 0,006636 seconds.\n",
      "\n",
      "SIMBA Output Voltage rms in steady state =  24.997433885144634\n",
      "SIMBA Inductance rms current in steady state =  5.053641840309184\n",
      " Diff_relative_rms for output voltage at steady state=  0.01026\n",
      " Diff_relative_rms for inductance current at steady state=  0.03616\n",
      "Total simulation Time in SIMBA 0.006636\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load SIMBA project\n",
    "file_path = os.path.join(os.getcwd(), \"buck.jsimba\")\n",
    "project = JsonProjectRepository(file_path)\n",
    "dcdc_buck = project.GetDesignByName('dcdc_buck')\n",
    "\n",
    "# Get the job object and solve the system\n",
    "job = dcdc_buck.TransientAnalysis.NewJob()\n",
    "status = job.Run()\n",
    "print(job.Summary())\n",
    "\n",
    "# Get results\n",
    "t = job.TimePoints\n",
    "Vout = job.GetSignalByName('R1 - Instantaneous Voltage').DataPoints\n",
    "L1_current = job.GetSignalByName('L1 - Instantaneous Current').DataPoints\n",
    "\n",
    "\n",
    "# Plot graph for output voltage and inductance current during all simulation time\n",
    "fig = plt.figure(figsize = (16, 9))\n",
    "\n",
    "ax1 = fig.add_subplot(211)\n",
    "plot1 = ax1.plot(t, Vout, \"r\")\n",
    "ax1.set_xlabel('time')\n",
    "ax1.set_ylabel('Vout')\n",
    "ax1.set_title(\"Vout SIMBA\")\n",
    "\n",
    "ax4 = fig.add_subplot(212)\n",
    "plot1 = ax4.plot(t, L1_current, \"r\")\n",
    "ax4.set_xlabel('time')\n",
    "ax4.set_ylabel('L1_current')\n",
    "ax4.set_title(\"Current through Inductance in SIMBA\")\n",
    "\n",
    "\n",
    "# steady state during 2 periods for the measurements comparison\n",
    "fsw=5000\n",
    "horizon_time = 2 / fsw  \n",
    "t, Vout, L1_current = steadystate_signal(\n",
    "   horizon_time,\n",
    "   np.array(job.TimePoints),\n",
    "   np.array(job.GetSignalByName('R1 - Instantaneous Voltage').DataPoints),\n",
    "   np.array(job.GetSignalByName('L1 - Instantaneous Current').DataPoints))\n",
    "\n",
    "# Perform rms calculation during 2 periods\n",
    "Vout_rms = rms_value(t, Vout)\n",
    "print(\"SIMBA Output Voltage rms in steady state = \", str(Vout_rms))\n",
    "\n",
    "L1_current_rms =  rms_value (t,L1_current)\n",
    "print(\"SIMBA Inductance rms current in steady state = \", str(L1_current_rms))\n",
    "\n",
    "# calculation of relative difference between SIMBA & theoretical calculation at steady state\n",
    "Diff_relative_rms1 = np.round(abs(Vo_th - Vout_rms)*100/Vo_th, 5)\n",
    "print (\" Diff_relative_rms for output voltage at steady state= \", Diff_relative_rms1)\n",
    "\n",
    "Diff_relative_rms = np.round(abs(Iind_rms_th - L1_current_rms)*100/Iind_rms_th, 5)\n",
    "print (\" Diff_relative_rms for inductance current at steady state= \", Diff_relative_rms)\n",
    "\n",
    "\n",
    "#Retrieve total simulation time for SIMBA simulation\n",
    "job.RunTime\n",
    "print (\"Total simulation Time in SIMBA\", str(job.RunTime))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot figure with both graphs and Histogram at steady state\n",
    "fig1 = plt.figure(figsize = (16, 9))\n",
    "ax2 = fig1.add_subplot(221)\n",
    "plot_bar(Tab1 = [Vout_rms],\n",
    "        Tab2 = [Vo_th], \n",
    "        largeur_barre = 0.1,\n",
    "        Etiquette = ['Vout_rms'],\n",
    "        Legend = ['SIMBA', 'Theory'],\n",
    "        xlim = [0, 1], ylim = [0, 30],\n",
    "        Tab1_abscisse = [0.5], \n",
    "        dxticks= 0.5,\n",
    "        FigAxe = ax2)\n",
    "\n",
    "\n",
    "\n",
    "ax3 = fig1.add_subplot(222)\n",
    "plot_bar(Tab1 = [Diff_relative_rms1], \n",
    "        largeur_barre = 0.1,\n",
    "        Etiquette = ['Vout_rms'],\n",
    "        FigAxe = ax3, \n",
    "        mxticks = 32,\n",
    "        ylabel = 'Relative difference to Theory (%)',\n",
    "        Tab1_abscisse = [0.5],\n",
    "        xlim = [0, 1],\n",
    "        dxticks= 0.5,\n",
    "        color_tab1 = 'blue')\n",
    "\n",
    "\n",
    "ax5 = fig1.add_subplot(223)\n",
    "plot_bar(Tab1 = [L1_current_rms],\n",
    "        Tab2 = [Iind_rms_th], \n",
    "        largeur_barre = 0.1,\n",
    "        Etiquette = ['I_inductance_rms'],\n",
    "        Legend = ['SIMBA', 'Theory'],\n",
    "        xlim = [0, 1], ylim = [0, 6],\n",
    "        Tab1_abscisse = [0.5], \n",
    "        dxticks= 0.5,\n",
    "        FigAxe = ax5)\n",
    "\n",
    "\n",
    "\n",
    "ax6 = fig1.add_subplot(224)\n",
    "plot_bar(Tab1 = [Diff_relative_rms], \n",
    "        largeur_barre = 0.1,\n",
    "        Etiquette = ['I_inductance_rms'],\n",
    "        FigAxe = ax6, \n",
    "        mxticks = 32,\n",
    "        ylabel = 'Relative difference to Theory (%)',\n",
    "        Tab1_abscisse = [0.5],\n",
    "        xlim = [0, 1],\n",
    "        dxticks= 0.5,\n",
    "        color_tab1 = 'blue')\n",
    "\n",
    "fig.tight_layout()\n",
    "fig1.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "c16a552a28c1f3e011cf5bf08dd8767fe3bdcc0a585d197bf4032d58ac6f1740"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}