phase_change.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_load(initial_load: np.ndarray, load_high: float, load_low: float, max_load_step: float)-> float:\n",
    "    \"\"\"Takes initual load perctentage and returns target load percentage\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    initial_load : np.ndarray\n",
    "        Initial load percentage\n",
    "    load_high : float\n",
    "        Upper load percentage limit.\n",
    "    load_low : float\n",
    "        Lower load percentage limit.\n",
    "    max_load_step : float\n",
    "        Max percentage difference between initial_load and target load\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float\n",
    "        target load percentage\n",
    "    \"\"\"\n",
    "    max_val = initial_load + max_load_step\n",
    "    min_val = initial_load - max_load_step\n",
    "    if initial_load > load_high - max_load_step:\n",
    "        return np.random.uniform(min_val, load_high)\n",
    "    elif initial_load < load_low + max_load_step:\n",
    "        return np.random.uniform(load_low, max_val)\n",
    "    else:\n",
    "        return np.random.uniform(min_val, max_val)\n",
    "\n",
    "target_load_vect = np.vectorize(target_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_values = target_load_vect(initial_load_values, load_high, load_low, max_load_step)\n",
    "final_values = np.around(final_values, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.where(final_values > load_high - max_load_step)[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.where(initial_load_values > load_high - max_load_step)[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "counts, bins = np.histogram(final_values)\n",
    "plt.hist(bins[:-1], bins, weights=counts)\n",
    "plt.title('Distribución valores finales')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "with open(\"IEEE34_form1_loads.atp\", \"r+\") as f:\n",
    "    lines = f.readlines()\n",
    "lines_copy = lines.copy()\n",
    "def loop():\n",
    "    for idx, line in enumerate(lines_copy):\n",
    "        if 'C Load' in line:\n",
    "            if '3f' in line:\n",
    "                print(line, end='')\n",
    "                print(f\" Fase A carga trifásica/bifásica: {lines_copy[idx + 1]}\", end='')\n",
    "                print(f\" Fase B carga trifásica/bifásica: {lines_copy[idx + 2]}\", end='')\n",
    "                print(f\" Fase C carga trifásica/bifásica: {lines_copy[idx + 3]}\", end='')\n",
    "            else:\n",
    "                print(line, end='')\n",
    "                print(f\" Fase A carga monofásica: {lines_copy[idx + 1]}\", end='')\n",
    "\n",
    "%timeit loop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import truncnorm\n",
    "\n",
    "\n",
    "# Distribución normal truncada\n",
    "def get_truncated_normal(\n",
    "    mean: float = 0, sd: int = 1, low: float = 0, upp: float = 10\n",
    ") -> np.ndarray:\n",
    "    \"\"\"Return array of truncated normal distribution with:\n",
    "    mean = mean\n",
    "    standard deviation = sd\n",
    "    lower limit = low\n",
    "    upper limit = upp\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    mean : float, optional\n",
    "        Truncated distribution Mean, by default 0\n",
    "    sd : int, optional\n",
    "        Truncated distribution standard deviation, by default 1\n",
    "    low : float, optional\n",
    "        Truncated distribution lower limit, by default 0\n",
    "    upp : float, optional\n",
    "        Truncated distribution upper limit, by default 10\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    np.ndarray\n",
    "        Array of truncated normal distribution\n",
    "    \"\"\"\n",
    "    return truncnorm((low - mean) / sd, (upp - mean) / sd, loc=mean, scale=sd)\n",
    "\n",
    "\n",
    "def load_split(\n",
    "    array: np.ndarray, len_a: int, len_b: int, len_c: int\n",
    ") -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
    "    \"\"\"Splits array of normal distribution load percentages into\n",
    "    three new arrays, one for each phase.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    array : np.ndarray\n",
    "        Array of percentages\n",
    "    len_a : int\n",
    "        Amount of loads on phase A\n",
    "    len_b : int\n",
    "        Amount of loads on phase B\n",
    "    len_c : int\n",
    "        Amount of loads on phase C\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tuple[np.ndarray, np.ndarray, np.ndarray]\n",
    "        load percentages split for phase A, B and C, respectively\n",
    "    \"\"\"\n",
    "    a = array[:len_a]\n",
    "    b = array[len_a : len_a + len_b]\n",
    "    c = array[len_a + len_b :]\n",
    "    return a, b, c\n",
    "\n",
    "\n",
    "# Graficar histograma de los nuevos datos\n",
    "def plot_hist(data: np.ndarray, title: str):\n",
    "    \"\"\"Plots histogram of distribution of percentages\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : np.ndarray\n",
    "        Numpy array with load percentages\n",
    "    title : str\n",
    "        Title for plot and prints\n",
    "    \"\"\"\n",
    "    counts, bins = np.histogram(data)\n",
    "    plt.hist(bins[:-1], bins, weights=counts)\n",
    "    plt.title(f\"Distribución valores {title}\")\n",
    "    plt.show()\n",
    "    print(f\"Promedio de las cargabilidades {title} individuales: {np.mean(data)}\")\n",
    "    print(f\"Cargabilidad mínima: {min(data):0.4}, cargabilidad máxima: {max(data):0.4}\")\n",
    "\n",
    "\n",
    "# Validación suma admitancias serie\n",
    "with open(\"2. Scenarios\\prueba_L125.77_122.15.atp\", \"r+\") as f:\n",
    "    lines = f.readlines()\n",
    "lines_copy = lines.copy()\n",
    "three_phase_loads = []\n",
    "Za = np.empty((0, 2))\n",
    "Zb = np.empty((0, 2))\n",
    "Zc = np.empty((0, 2))\n",
    "\n",
    "Ztotal = 0\n",
    "for idx, line in enumerate(lines_copy):\n",
    "    if \"/SWITCH\" in line:\n",
    "        switch_section_idx = idx\n",
    "    if \"C Load\" in line:\n",
    "        if \"3f\" in line:\n",
    "            # print(lines_copy[idx + 1], end=\"\")\n",
    "            # print(lines_copy[idx + 2], end=\"\")\n",
    "            # print(lines_copy[idx + 3], end=\"\")\n",
    "            Ra = float(lines_copy[idx + 1][30:42])\n",
    "            Xa = float(lines_copy[idx + 1][46:58])\n",
    "            za = Ra + Xa * 1j\n",
    "            if not np.abs(za) > 1e20:\n",
    "                Za = np.append(Za, [[idx + 1, za]], axis=0)\n",
    "\n",
    "            Rb = float(lines_copy[idx + 2][30:42])\n",
    "            Xb = float(lines_copy[idx + 2][46:58])\n",
    "            zb = Rb + Xb * 1j\n",
    "            if not np.abs(zb) > 1e20:\n",
    "                Zb = np.append(Zb, [[idx + 2, zb]], axis=0)\n",
    "\n",
    "            Rc = float(lines_copy[idx + 3][30:42])\n",
    "            Xc = float(lines_copy[idx + 3][46:58])\n",
    "            zc = Rc + Xc * 1j\n",
    "            if not np.abs(zc) > 1e20:\n",
    "                Zc = np.append(Zc, [[idx + 3, zc]], axis=0)\n",
    "\n",
    "        else:\n",
    "            R = float(lines_copy[idx + 1][30:42])\n",
    "            X = float(lines_copy[idx + 1][46:58])\n",
    "            z = R + X * 1j\n",
    "            if \" A\" in line:\n",
    "                Za = np.append(Za, [[idx + 1, z]], axis=0)\n",
    "            if \" B\" in line:\n",
    "                Zb = np.append(Zb, [[idx + 1, z]], axis=0)\n",
    "            if \" C\" in line:\n",
    "                Zc = np.append(Zc, [[idx + 1, z]], axis=0)\n",
    "    if \"C switch load tri\" in line:\n",
    "        \n",
    "        three_phase_switch = lines_copy[idx:idx+4].copy()\n",
    "    elif \"C switch load mono\" in line:\n",
    "        mono_switch = lines_copy[idx:idx+2].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Validación suma admitancias serie\n",
    "with open(\"2. Scenarios\\prueba_L125.77_122.15.atp\", \"r+\") as f:\n",
    "    lines = f.readlines()\n",
    "lines_copy = lines.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C switch load tri\\n', '  TRIINATRILDA       -1.      1.E3                                             0\\n', '  TRIINBTRILDB       -1.      1.E3                                             0\\n', '  TRIINCTRILDC       -1.      1.E3                                             0\\n']\n",
      "['C switch load mono\\n', '  MONOINMONOLD       -1.      1.E3                                             0\\n']\n",
      "1705\n"
     ]
    }
   ],
   "source": [
    "print(three_phase_switch)\n",
    "print(mono_switch)\n",
    "print(switch_section_idx)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'group'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[633], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m pattern_load_change \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mL(\u001b[39m\u001b[39m\\\u001b[39m\u001b[39md\u001b[39m\u001b[39m{3}\u001b[39;00m\u001b[39m\\\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\\\u001b[39m\u001b[39md\u001b[39m\u001b[39m{2}\u001b[39;00m\u001b[39m)_(\u001b[39m\u001b[39m\\\u001b[39m\u001b[39md\u001b[39m\u001b[39m{3}\u001b[39;00m\u001b[39m\\\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\\\u001b[39m\u001b[39md\u001b[39m\u001b[39m{2}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m      5\u001b[0m cargabilidad_inicial \u001b[39m=\u001b[39m \u001b[39mfloat\u001b[39m(re\u001b[39m.\u001b[39msearch(pattern_load, atp_file_name)\u001b[39m.\u001b[39mgroup(\u001b[39m1\u001b[39m))\n\u001b[1;32m----> 6\u001b[0m cargabilidad_final \u001b[39m=\u001b[39m \u001b[39mfloat\u001b[39m(re\u001b[39m.\u001b[39;49msearch(pattern_load_change, atp_file_name)\u001b[39m.\u001b[39;49mgroup(\u001b[39m2\u001b[39m))\n\u001b[0;32m      7\u001b[0m \u001b[39mprint\u001b[39m(cargabilidad_final)\n\u001b[0;32m      8\u001b[0m \u001b[39mif\u001b[39;00m cargabilidad_final:\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'group'"
     ]
    }
   ],
   "source": [
    "import re\n",
    "atp_file_name = \"L099.86atp\"\n",
    "pattern_load = \"L(\\d{3}\\.\\d{2})\"\n",
    "pattern_load_change = \"L(\\d{3}\\.\\d{2})_(\\d{3}\\.\\d{2})\"\n",
    "cargabilidad_inicial = float(re.search(pattern_load, atp_file_name).group(1))\n",
    "cargabilidad_final = float(re.search(pattern_load_change, atp_file_name).group(2))\n",
    "print(cargabilidad_final)\n",
    "if cargabilidad_final:\n",
    "    print('existe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEGIN NEW DATA CASE\n",
      "C ------------------------------------------------------------------------------\n",
      "C Generated by ATPDraw marzo, 15, 2023\n",
      "C from project C:\\Users\\aherrada\\OneDrive - Universidad del Norte\\Uninorte\\Detec\n",
      "C ATPDraw developed by H. K. Høidalen at NTNU - NORWAY 1999-2020\n",
      "C ------------------------------------------------------------------------------\n",
      "POWER FREQUENCY                      60.\n",
      "C  dT  >< Tmax >< Xopt >< Copt ><Epsiln>\n",
      "3.255E-5.0666667     60.     60.        \n",
      "     500       1       1       1       1       0       0       1       0\n",
      "/TACS\n"
     ]
    }
   ],
   "source": [
    "with open(\"Sistemas de prueba\\IEEE34.atp\", \"r+\") as f:\n",
    "    linesx = f.readlines()\n",
    "    lines = linesx.copy()\n",
    "for idx, line in enumerate(lines):\n",
    "    print(line, end='')\n",
    "    if idx == 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines[switch_section_idx+2:switch_section_idx+2] = three_phase_switch\n",
    "lines[switch_section_idx+2:switch_section_idx+2] = mono_switch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/SWITCH\\n',\n",
       " 'C < n 1>< n 2>< Tclose ><Top/Tde ><   Ie   ><Vf/CLOP ><  type  >\\n',\n",
       " 'C switch load mono\\n',\n",
       " '  MONOINMONOLD       -1.      1.E3                                             0\\n',\n",
       " 'C switch load tri\\n',\n",
       " '  TRIINATRILDA       -1.      1.E3                                             0\\n',\n",
       " '  TRIINBTRILDB       -1.      1.E3                                             0\\n',\n",
       " '  TRIINCTRILDC       -1.      1.E3                                             0\\n',\n",
       " '  R1A   N100A        -1.      1.E3                                             0\\n',\n",
       " '  R1B   N100B        -1.      1.E3                                             0\\n']"
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[switch_section_idx:switch_section_idx+10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promedio de las cargabilidades Initial individuales: 0.8997537236176226\n",
      "Cargabilidad mínima: 0.8784, cargabilidad máxima: 0.925\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promedio de las cargabilidades Target individuales: 0.04765752287814566\n",
      "Cargabilidad mínima: 0.02121, cargabilidad máxima: 0.07337\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promedio de las cargabilidades Target_2 individuales: 0.949168714289425\n",
      "Cargabilidad mínima: 0.9287, cargabilidad máxima: 0.972\n",
      "Porcentaje de cargabilidad inicial real: 89.977%\n",
      "Porcentaje de cargabilidad final real: 94.304%\n",
      "Porcentaje de cargabilidad final real 2: 95.235%\n",
      "Porcentaje de cambio entre inicial y final: 4.809%\n",
      "Porcentaje de cambio entre inicial y final 2: 5.844%\n"
     ]
    }
   ],
   "source": [
    "# Find Y total for each phase\n",
    "Ya = 1 / Za[:, 1]\n",
    "Yb = 1 / Zb[:, 1]\n",
    "Yc = 1 / Zc[:, 1]\n",
    "Ya_total = np.sum(Ya)\n",
    "Yb_total = np.sum(Yb)\n",
    "Yc_total = np.sum(Yc)\n",
    "\n",
    "Y_total = Ya_total + Yb_total + Yc_total\n",
    "\n",
    "\n",
    "len_a = Za.shape[0]\n",
    "len_b = Zb.shape[0]\n",
    "len_c = Zc.shape[0]\n",
    "load_amount = len_a + len_b + len_c\n",
    "\n",
    "cargabilidad_inicial = 0.90\n",
    "cargabilidad_final = 0.95\n",
    "delta_cargabilidad = cargabilidad_final - cargabilidad_inicial\n",
    "\n",
    "initial_load = (\n",
    "    get_truncated_normal(mean=cargabilidad_inicial * 100, sd=1, low=0, upp=200).rvs(\n",
    "        load_amount\n",
    "    )\n",
    "    / 100\n",
    ")\n",
    "target_load = (\n",
    "    get_truncated_normal(mean=delta_cargabilidad * 100, sd=1, low=-10, upp=10).rvs(\n",
    "        load_amount\n",
    "    )\n",
    "    / 100\n",
    ")\n",
    "target_load_2 = (\n",
    "    get_truncated_normal(mean=cargabilidad_final * 100, sd=1, low=30, upp=200).rvs(\n",
    "        load_amount\n",
    "    )\n",
    "    / 100\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "initial_a, initial_b, initial_c = load_split(initial_load, len_a, len_b, len_c)\n",
    "target_a, target_b, target_c = load_split(target_load, len_a, len_b, len_c)\n",
    "target_a_2, target_b_2, target_c_2 = load_split(target_load_2, len_a, len_b, len_c)\n",
    "plot_hist(initial_load, 'Initial')\n",
    "plot_hist(target_load, 'Target')\n",
    "plot_hist(target_load_2, 'Target_2')\n",
    "\n",
    "# Admitancias por fase iniciales y finales\n",
    "Ya_initial, Yb_initial, Yc_initial = Ya * initial_a, Yb * initial_b, Yc * initial_c\n",
    "Ya_target_2, Yb_target_2, Yc_target_2 = Ya * target_a_2, Yb * target_b_2, Yc * target_c_2\n",
    "Ya_target = Ya_initial * target_a + Ya_initial\n",
    "Yb_target = Yb_initial * target_b + Yb_initial\n",
    "Yc_target = Yc_initial * target_c + Yc_initial\n",
    "\n",
    "# Admitancia total luego de la variación de carga\n",
    "Y_initial_total = Ya_initial.sum() + Yb_initial.sum() + Yc_initial.sum()\n",
    "Y_target_total = Ya_target.sum() + Yb_target.sum() + Yc_target.sum()\n",
    "Y_target_total_2 = Ya_target_2.sum() + Yb_target_2.sum() + Yc_target_2.sum()\n",
    "\n",
    "# Procentaje de Cambio\n",
    "print(\n",
    "    f\"Porcentaje de cargabilidad inicial real: {abs(Y_initial_total) / abs(Y_total):.3%}\"\n",
    ")\n",
    "print(\n",
    "    f\"Porcentaje de cargabilidad final real: {abs(Y_target_total) / abs(Y_total):.3%}\"\n",
    ")\n",
    "print(\n",
    "    f\"Porcentaje de cargabilidad final real 2: {abs(Y_target_total_2) / abs(Y_total):.3%}\"\n",
    ")\n",
    "print(\n",
    "    f\"Porcentaje de cambio entre inicial y final: {abs(Y_target_total - Y_initial_total )/ abs(Y_initial_total):.3%}\"\n",
    ")\n",
    "print(\n",
    "    f\"Porcentaje de cambio entre inicial y final 2: {abs(Y_target_total_2 - Y_initial_total )/ abs(Y_initial_total):.3%}\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 675,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hallar impedancias a partir de las admitancias\n",
    "Za_initial = 1 / Ya_initial\n",
    "Zb_initial = 1 / Yb_initial\n",
    "Zc_initial = 1 / Yc_initial\n",
    "Za_target = 1 / Ya_target\n",
    "Zb_target = 1 / Yb_target\n",
    "Zc_target = 1 / Yc_target\n",
    "\n",
    "\n",
    "Za_ini = np.append([Za[:, 0]], [Za_initial], axis=0).T\n",
    "Za_tar = np.append([Za[:, 0]], [Za_target], axis=0).T\n",
    "Zb_ini = np.append([Zb[:, 0]], [Zb_initial], axis=0).T\n",
    "Zb_tar = np.append([Zb[:, 0]], [Zb_target], axis=0).T\n",
    "Zc_ini = np.append([Zc[:, 0]], [Zc_initial], axis=0).T\n",
    "Zc_tar = np.append([Zc[:, 0]], [Zc_target], axis=0).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_loads(Z_array: np.ndarray, lines_copy: list) -> list:\n",
    "    \"\"\"Returns list of lines with updated impedances\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    Z_array : np.ndarray\n",
    "        Array of indices and impedances (complex number).\n",
    "        Dimension (m, 2), where\n",
    "            m = number of impedances\n",
    "            (,0) = indices column\n",
    "            (,1) = impedances column\n",
    "    lines_copy : list\n",
    "        List with .atp file content\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    list\n",
    "        List of .atp file content with updated impedances\n",
    "    \"\"\"\n",
    "    for Z in Z_array:\n",
    "        idx = Z[0]\n",
    "        z = Z[1]\n",
    "\n",
    "        idx = int(idx)\n",
    "        R = f\"{z.real:.6f}\".center(12)\n",
    "        X = f\"{z.imag:.6f}\".center(12)\n",
    "        line = lines_copy[idx]\n",
    "        new_line = f\"{line[:30]}{R}{line[42:46]}{X}{line[58:]}\"\n",
    "\n",
    "        lines_copy[idx] = new_line\n",
    "    return lines_copy\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    # f.writelines(lines_copy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"IEEE34_form1_update_loads_prueba.atp\", \"w+\") as f:\n",
    "    lines_copy = update_loads(Za_ini, lines_copy)\n",
    "    lines_copy = update_loads(Zb_ini, lines_copy)\n",
    "    lines_copy = update_loads(Zc_ini, lines_copy)\n",
    "    f.writelines(lines_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promedio de las cargabilidades iniciales individuales: 0.7812627767545021\n",
      "Cargabilidad mínima: 0.7423, cargabilidad máxima: 0.8224\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promedio de las cargabilidades finales individuales: 0.04038158402647143\n",
      "Cargabilidad mínima: -0.001109, cargabilidad máxima: 0.08691\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promedio de las cargabilidades ideales individuales: 0.8223219799500543\n",
      "Cargabilidad mínima: 0.767, cargabilidad máxima: 0.8696\n",
      "0.03161426354636107\n",
      "Cargabilidad promedio método 1: 0.812877040300863\n",
      "Cargabilidad promedio ideal: 0.8223219799500543\n"
     ]
    }
   ],
   "source": [
    "cargabilidad_inicial = 0.78\n",
    "cargabilidad_final = 0.82\n",
    "delta_cargabilidad = cargabilidad_final - cargabilidad_inicial\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "initial_load = (\n",
    "    get_truncated_normal(mean=cargabilidad_inicial * 100, sd=2, low=30, upp=200).rvs(load_amount) / 100\n",
    ")\n",
    "target_load = (\n",
    "    get_truncated_normal(mean=delta_cargabilidad * 100, sd=2, low=-10, upp=10).rvs(load_amount) / 100\n",
    ")\n",
    "real_target_load = (\n",
    "    get_truncated_normal(mean=cargabilidad_final * 100, sd=2, low=-30, upp=200).rvs(load_amount) / 100\n",
    ")\n",
    "# Graficar la nueva distribución de datos\n",
    "plot_hist(initial_load, \"iniciales\")\n",
    "\n",
    "\n",
    "plot_hist(target_load, \"finales\")\n",
    "\n",
    "\n",
    "plot_hist(real_target_load, \"ideales\")\n",
    "\n",
    "\n",
    "print(np.mean(target_load*initial_load))\n",
    "print(f\"Cargabilidad promedio método 1: {np.mean(initial_load + target_load*initial_load)}\")\n",
    "print(f\"Cargabilidad promedio ideal: {np.mean(real_target_load)}\")"
   ]
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    "### Validación obtención admitancias \n",
    "\n",
    "##### Suposiciones:\n",
    "\n",
    "- El voltaje en todos los nodos es igual al voltaje nominal\n",
    "- Las fases están balanceadas\n",
    "- La cargabilidad del sistema está solo determinado por las cargas (i.e. no se consideran pérdidas en líneas ni otros elementos.)"
   ]
  }
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