{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 phase Bridge Diode Power 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 average voltage and input rms current.\n",
    "\n",
    "# SIMBA circuit\n",
    "\n",
    "![bridge diode](fig/diode_bridge.png)\n",
    "\n",
    "Simulation settings : \n",
    "* predictive time step (variable) \n",
    "* minimum time step: 1E-06\n",
    "* simulation time: 0.08s\n",
    "\n",
    "\n",
    "# Theory calculation\n",
    "\n",
    "* Average Output voltage\n",
    "\n",
    "$ V_{<out>} =  \\frac{V_{in} \\times 3 \\times \\sqrt{6}}{\\pi*\\sqrt{2}} $\n",
    "\n",
    "with V_in = 380\n",
    "\n",
    "* Input RMS current\n",
    "\n",
    "$ I_{input,rms} = I_{dc} \\times \\sqrt{\\frac{2}{3}}  $\n",
    "\n",
    "with I_dc=10\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 Average =  628.5149407808428\n",
      "Theory Input rms current =  8.16496580927726\n"
     ]
    }
   ],
   "source": [
    "#Theory calculation for output average voltage and input rms current\n",
    "\n",
    "Vinput = 380\n",
    "Vo_th = (Vinput * 3 * sqrt(6))/(pi*sqrt(2))\n",
    "print (\"Theory Output voltage Average = \", Vo_th)\n",
    "\n",
    "Idc_th=10\n",
    "Iinput_rms = Idc_th * sqrt(2/3)\n",
    "print (\"Theory Input rms current = \", Iinput_rms)"
   ]
  },
  {
   "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: 0,0092649 seconds.\n",
      "\n",
      "SIMBA Output Voltage Average =  628.5175247445637\n",
      "SIMBA Input current RMS =  8.164809015328022\n",
      " Diff_relative_mean for output voltage at steady state =  0.00041\n",
      " Diff_relative_rms for input current at steady state =  0.00192\n",
      "Total simulation Time in SIMBA 0.0092649\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(), \"3ph_bridge_diode.jsimba\")\n",
    "project = JsonProjectRepository(file_path)\n",
    "AC_DC_bridge_diode = project.GetDesignByName('AC_DC_bridge_diode')\n",
    "\n",
    "# Get the job object and solve the system\n",
    "job = AC_DC_bridge_diode.TransientAnalysis.NewJob()\n",
    "status = job.Run()\n",
    "print(job.Summary())\n",
    "\n",
    "# Get results\n",
    "t = job.TimePoints\n",
    "Vout = job.GetSignalByName('DC1 - Instantaneous Voltage').DataPoints\n",
    "I_AC1 = job.GetSignalByName('AC1 - Instantaneous Current').DataPoints\n",
    "\n",
    "\n",
    "# Plot graph for output voltage and input 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, I_AC1, \"r\")\n",
    "ax4.set_xlabel('time')\n",
    "ax4.set_ylabel('Input current I_AC1')\n",
    "ax4.set_title(\"Input current in SIMBA\")\n",
    "\n",
    "\n",
    "# steady state during 2 periods for the measurements comparison\n",
    "fsw=50\n",
    "horizon_time = 2 / fsw  \n",
    "t, Vout, I_AC1 = steadystate_signal(\n",
    "   horizon_time,\n",
    "   np.array(job.TimePoints),\n",
    "   np.array(job.GetSignalByName('DC1 - Instantaneous Voltage').DataPoints),\n",
    "   np.array(job.GetSignalByName('AC1 - Instantaneous Current').DataPoints))\n",
    "\n",
    "# Peform average calculation for DC signals and rms calculation for ac signals during 2 periods\n",
    "Vout_average = average_value(t, Vout)\n",
    "print(\"SIMBA Output Voltage Average = \", str(Vout_average))\n",
    "\n",
    "I_AC1_rms = rms_value(t, I_AC1)\n",
    "print(\"SIMBA Input current RMS = \", str(I_AC1_rms))\n",
    "\n",
    "# calculation of relative difference between SIMBA & theoretical calculation at steady state\n",
    "Diff_relative_mean = np.round(abs(Vo_th - Vout_average)*100/Vo_th, 5)\n",
    "print (\" Diff_relative_mean for output voltage at steady state = \", Diff_relative_mean)\n",
    "\n",
    "Diff_relative_rms = np.round(abs(Iinput_rms - I_AC1_rms)*100/Iinput_rms, 5)\n",
    "print (\" Diff_relative_rms for input 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_average],\n",
    "        Tab2 = [Vo_th], \n",
    "        largeur_barre = 0.1,\n",
    "        Etiquette = ['<Vout>'],\n",
    "        Legend = ['SIMBA', 'Theory'],\n",
    "        xlim = [0, 1], ylim = [0, 1000],\n",
    "        Tab1_abscisse = [0.5], \n",
    "        dxticks= 0.5,\n",
    "        FigAxe = ax2)\n",
    "\n",
    "\n",
    "ax3 = fig1.add_subplot(222)\n",
    "plot_bar(Tab1 = [Diff_relative_mean], \n",
    "        largeur_barre = 0.1,\n",
    "        Etiquette = ['<Vout>'],\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",
    "ax5.set_ylabel('Average or rms values')\n",
    "plot_bar(Tab1 = [I_AC1_rms],\n",
    "        Tab2 = [Iinput_rms], \n",
    "        largeur_barre = 0.1,\n",
    "        Etiquette = ['Input current rms'],\n",
    "        Legend = ['SIMBA', 'Theory'],\n",
    "        xlim = [0, 1], ylim = [0, 10],\n",
    "        Tab1_abscisse = [0.5], \n",
    "        dxticks= 0.5,\n",
    "        FigAxe = ax2)\n",
    "\n",
    "\n",
    "\n",
    "ax6 = fig1.add_subplot(224)\n",
    "plot_bar(Tab1 = [Diff_relative_rms], \n",
    "        largeur_barre = 0.1,\n",
    "        Etiquette = ['Input current rms'],\n",
    "        FigAxe = ax6, \n",
    "        mxticks = 32,\n",
    "        Tab1_abscisse = [0.5],\n",
    "        xlim = [0, 1],\n",
    "        dxticks= 0.5,\n",
    "        ylabel = 'Relative difference to Theory (%)',\n",
    "        color_tab1 = 'blue')\n",
    "\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
}