2.1_Curva_Característica_IV.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.1 Curva Característica IV de Módulos Fotovoltaicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
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       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
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       "\n",
       "    function on_error() {\n",
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       "\n",
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       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
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       "        element.crossOrigin = \"anonymous\";\n",
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       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
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       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
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       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
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       "    \n",
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       "  ];\n",
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       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
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       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    import photovoltaic as pv\n",
    "except ImportError:\n",
    "    !pip install photovoltaic\n",
    "    import photovoltaic as pv\n",
    "   \n",
    "from ipywidgets import interact\n",
    "\n",
    "try:\n",
    "    from bokeh.io import push_notebook, show, output_notebook\n",
    "except ImportError:\n",
    "    !pip install bokeh\n",
    "    from bokeh.io import push_notebook, show, output_notebook\n",
    "    \n",
    "from bokeh.plotting import figure\n",
    "output_notebook()\n",
    "\n",
    "try:\n",
    "    import matplotlib.pyplot as plt\n",
    "except ImportError:\n",
    "    !pip install matplotlib\n",
    "    import matplotlib.pyplot as plt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voc (V): 0.624\n"
     ]
    }
   ],
   "source": [
    "IL = 0.035 # A/cm²\n",
    "I0 = 1e-12 # A/cm²\n",
    "Voc = pv.cell.Voc(IL,I0)\n",
    "print(f'Voc (V): {Voc:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ideal Solar cell with no resistances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "V = np.linspace(0, Voc, 100)\n",
    "I = pv.cell.I_cell(V, IL, I0)\n",
    "#plt.ylim(0, 0.04)\n",
    "plt.xlabel('voltage (V)')\n",
    "plt.ylabel('current (A)')\n",
    "plt.grid(True)\n",
    "plt.plot(V, I, label=\"Ideal Cell\")\n",
    "plt.legend()\n",
    "#plt.savefig('plot_ideal_cell.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rshunt = 500 # ohm cm2\n",
    "I_Rshunt = pv.cell.I_cell_Rshunt(V, IL, I0, Rshunt)\n",
    "#plt.ylim(0, 0.04)\n",
    "plt.xlabel('voltage (V)')\n",
    "plt.ylabel('current (A)')\n",
    "plt.grid(True)\n",
    "plt.plot(V, I, label=\"Ideal Cell\")\n",
    "plt.plot(V, I_Rshunt, label=\"Rshunt\")\n",
    "plt.legend(loc='lower left')\n",
    "#plt.savefig('plot_ideal_cell.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add on series resistance losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rseries = 2.0 # ohm cm2\n",
    "V_Rseries = pv.cell.V_Rseries(V, I_Rshunt, Rseries)\n",
    "#plt.ylim(0, 0.04)\n",
    "plt.xlabel('voltage (V)')\n",
    "plt.ylabel('current (A)')\n",
    "plt.grid(True)\n",
    "plt.plot(V, I, label=\"Ideal Cell\")\n",
    "plt.plot(V, I_Rshunt, label=\"Rshunt\")\n",
    "plt.plot(V_Rseries, I_Rshunt, label=\"Rshunt and Rseries\")\n",
    "plt.legend(loc='lower left')\n",
    "#plt.savefig('plot_ideal_cell.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tarefa 1\n",
    "\n",
    "Refaça os gráficos acima incluindo o gráfico da potência. Identifique, também, os pontos de máxima potência, a tensão de circuito aberto e a corrente de curto circuito."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definido a Função"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para escrever usando Octave/Matlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import e\n",
    "\n",
    "def sm55(Va,Rad,TaC):\n",
    "## ms50.m model for the MS55 solar array\n",
    "## current given voltage, illumination and temperature\n",
    "## Ia = ms55(Va,G,TaC) = array voltage\n",
    "## Ia,Va = array current,voltage\n",
    "## G = num of Suns (1 Sun = 1000 W/m^2)\n",
    "## TaC = Temp ambient in Deg C\n",
    "    if Rad > 5:\n",
    "        Suns = Rad / 1000\n",
    "    else:\n",
    "        Suns = 0.005\n",
    "    k = 1.38e-23 # Boltzman´s const\n",
    "    q = 1.60e-19 # charge on an electron\n",
    "    # enter the following constants here, and the model will be\n",
    "    # calculated based on these. for 1000W/m^2\n",
    "    A = 1.9 # \"diode quality\" factor, =2 for crystaline, <2 for amorphous\n",
    "    Vg = 1.12 # band gap voltage, 1.12eV for xtal Si, ~1.75 for amorphous Si.\n",
    "    Ns = 36 # number of series connected cells (diodes)\n",
    "    T1 = 273 + 25\n",
    "    Voc_T1 = 21.7 /Ns # open cct voltage per cell at temperature T1\n",
    "    Isc_T1 = 3.40 # short cct current per cell at temp T1\n",
    "    T2 = 273 + 60\n",
    "    Voc_T2 = 18.30 /Ns # open cct voltage per cell at temperature T2\n",
    "    Isc_T2 = 3.45 # short cct current per cell at temp T2\n",
    "    TaK = 273 + TaC + 35 * Suns # array working temp\n",
    "    TrK = 273 + 25 # reference temp\n",
    "    # when Va = 0, light generated current Iph_T1 = array short cct current\n",
    "    # constant \"a\" can be determined from Isc vs T\n",
    "    Iph_T1 = Isc_T1 * Suns\n",
    "    a = (Isc_T2 - Isc_T1)/Isc_T1 * 1/(T2 - T1)\n",
    "    Iph = Iph_T1 * (1 + a*(TaK - T1))\n",
    "    Vt_T1 = k * T1 / q # = A * kT/q\n",
    "    Ir_T1 = Isc_T1 / (e**(Voc_T1/(A*Vt_T1))-1)\n",
    "    Ir_T2 = Isc_T2 / (e**(Voc_T2/(A*Vt_T1))-1)\n",
    "    b = Vg * q/(A*k)\n",
    "    Ir = Ir_T1 * (TaK/T1)**(3/A) * e**(-b*(1/TaK - 1/T1))\n",
    "    X2v = Ir_T1/(A*Vt_T1) * e**(Voc_T1/(A*Vt_T1))\n",
    "    dVdI_Voc = - 1.15/Ns / 2 # dV/dI at Voc per cell --\n",
    "    # from manufacturers graph\n",
    "    Rs = - dVdI_Voc - 1/X2v # series resistance per cell\n",
    "    # Ia = 0:0.01:Iph\n",
    "    Vt_Ta = A * k * TaK / q # = A * kT/q\n",
    "    # Ia1 = Iph - Ir*( e**((Vc+Ia*Rs)/Vt_Ta) -1)\n",
    "    # solve for Ia: f(Ia) = Iph - Ia - Ir*( e**((Vc+Ia*Rs)/Vt_Ta) -1) = 0\n",
    "    # Newton´s method: Ia2 = Ia1 - f(Ia1)/f´(Ia1)\n",
    "    Vc = Va/Ns\n",
    "    Ia = Vc*0\n",
    "    # Iav = Ia\n",
    "    for j in range(5):\n",
    "        Ia = Ia - (Iph - Ia - Ir*(e**((Vc+Ia*Rs)/Vt_Ta) -1)) / (-1 - (Ir*( e**((Vc+Ia*Rs)/Vt_Ta) -1))*Rs/Vt_Ta)\n",
    "        # Iav = [Iav;Ia] # to observe convergence for debugging.\n",
    "    return Ia"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráfico interativo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Va = np.linspace(0, 20, 100)\n",
    "y = np.sin(Va)\n",
    "p = figure(title=\"Curva Característica\", plot_height=300, plot_width=600, y_range=(0,4), x_range=(0,20),\n",
    "           background_fill_color='#efefef')\n",
    "r = p.line(Va, y, color=\"#8888cc\", line_width=1.5, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update(Rad = 1000, TaC = 25):\n",
    "    r.data_source.data['y'] = sm55(Va,Rad,TaC)\n",
    "    push_notebook()"
   ]
  },
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       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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       "    }, 10, root)\n",
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    "show(p, notebook_handle=True)"
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "152ba524037643c2a250e29b9c6ecfc6",
       "version_major": 2,
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      "text/plain": [
       "interactive(children=(IntSlider(value=1000, description='Rad', max=1300), IntSlider(value=25, description='TaC…"
      ]
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       "<function __main__.update(Rad=1000, TaC=25)>"
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     "execution_count": 10,
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    "interact(update, Rad=(0,1300), TaC=(0,70))"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tarefa 2\n",
    "\n",
    "Faça um gráfico interativo variando os 5 parâmetros elétricos do modelo elétrico de um módulo fotovoltaico. Semelhante ao realizado para as resistências neste site: https://www.pveducation.org/es/fotovoltaica/characterisation/measurement-of-series-resistance"
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   "source": []
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