light_jv_parameters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PV parameter extraction from a Si solar cell and µSMU\n",
    "\n",
    "- Measure the JV-curve of solar cells using the micro-SMU under light\n",
    "- Import data into the Jupyter Notebok and analyse using python3\n",
    "- Plot the JV curve\n",
    "- Extract parameters of the solar cell: Voc, Jsc, FF, Vmax,  Imax \n",
    "\n",
    "Note: The polycristaline Si mini-modules were adquired from Aliexpress (Cheap devices) Parameters:  Voc = 2V , I = 40 mA (4 mA each, 40 mA total at 1000 W/m2)  url: https://es.aliexpress.com/item/4000512203943.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data into Jupyter Notebook \n",
    "\n",
    "filename = 'data/celdaSi_ligh_near_01.dat' # Under Light (White LED lamp with 500 W/m2 irradiance)\n",
    "np_data = np.loadtxt(filename, delimiter=',', skiprows =2) # Import as np.ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separate data in two arrays - V and J \n",
    "\n",
    "V = np_data[:,0] # voltage is in [V]\n",
    "J = np_data[:,1] # current density is in [mA/cm2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find Voc_approx and Jsc_approx\n",
    "\n",
    "a = [J > 0 ] #Voc_approx is located when J > 0 \n",
    "Voc_approx = np.min(V[(J>0)])\n",
    "Voc_approx = round(Voc_approx,4) \n",
    "\n",
    "b = (V < 0)  #Jsc_approx is located when V < 0 \n",
    "Jsc_approx = np.max(J[(V<0)])\n",
    "Jsc_approx = round(Jsc_approx,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the approx Fill Factor \n",
    "\n",
    "P = J*V # mW/cm2\n",
    "P_min = np.min(P)\n",
    "FF_approx = P_min/(Voc_approx*Jsc_approx)\n",
    "FF_approx = round(FF_approx,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot the JV curve with the approximated values of Voc and Jsc\n",
    "\n",
    "# Print data inside the Voc anc Jsc limits \n",
    "\n",
    "plt.plot(V,J,'or')\n",
    "plt.ylim(np.min(J),0)\n",
    "plt.xlim(0,Voc_approx) # Plot the 4th quadrant from 0 to Voc_approx\n",
    "\n",
    "plt.title('poly-Si solar cell, 500 $W/m^2$', fontsize =16)\n",
    "plt.ylabel('Current density $ \\:[mA/cm^2]$', fontsize=14)\n",
    "plt.xlabel('Voltage $[V]$', fontsize=14)\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voc_approx = 2.0999 V\n",
      "Jsc_approx = -0.5867 mA/cm2\n",
      "FF_approx  = 0.6888\n"
     ]
    }
   ],
   "source": [
    "#  This are the approximate values extracted from the light JV-curve \n",
    "\n",
    "print (f'Voc_approx = {Voc_approx} V')\n",
    "print (f'Jsc_approx = {Jsc_approx} mA/cm2')\n",
    "print (f'FF_approx  = {FF_approx}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Refine the Voc trough linear fitting \n",
    "\n",
    "#Import data near Voc to calculate de series resistance (slope)\n",
    "\n",
    "Vs = V[(V < (Voc_approx + 0.04)) & (V > (Voc_approx - 0.04))]\n",
    "Js = J[(V < (Voc_approx + 0.04)) & (V > (Voc_approx - 0.04))]\n",
    "\n",
    "#add the column of ones to the inputs if you want statsmodels to calculate the intercept 𝑏₀. \n",
    "Vs = sm.add_constant(Vs) \n",
    "\n",
    "# Create the model for the regression  and show results \n",
    "\n",
    "model = sm.OLS(Js, Vs) #Take care from the Js , Vs input order\n",
    "results = model.fit()\n",
    "\n",
    "# Extract the y = mx+b coefficients for Voc calculation \n",
    "\n",
    "b1 = results.params[0]\n",
    "m1 = results.params[1]\n",
    "\n",
    "# Linnear fitting of Voc  when mx+b = 0\n",
    "\n",
    "Voc_fit = -b1/m1\n",
    "Voc_fit = round(Voc_fit,4) \n",
    "\n",
    "# Calculation of series resistance (Rs) from the linear model \n",
    "\n",
    "Rs = 1/(m1*1e-3)\n",
    "Rs = round(Rs,2) # Series resistance \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Refine the Jsc trough linear fitting \n",
    "\n",
    "#Import data near Voc to calculate de series resistance (slope)\n",
    "\n",
    "Vp = V[(V < (0 + 0.04)) & (V > (0 - 0.04))]\n",
    "Jp = J[(V < (0 + 0.04)) & (V > (0 - 0.04))]\n",
    "\n",
    "#add the column of ones to the inputs if you want statsmodels to calculate the intercept 𝑏₀. \n",
    "Vp = sm.add_constant(Vp)  \n",
    "\n",
    "# Create the model for the regression  and show results \n",
    "\n",
    "model_sc = sm.OLS(Jp, Vp)\n",
    "results_sc = model_sc.fit()\n",
    "\n",
    "# Extract the y = mx+b coefficients for Voc calculation \n",
    "\n",
    "b1 = results_sc.params[0]\n",
    "m1 = results_sc.params[1]\n",
    "\n",
    "# Linnear fitting of Voc  when mx+b = 0\n",
    "\n",
    "Jsc_fit = b1\n",
    "Jsc_fit = round(Jsc_fit,4) \n",
    "\n",
    "# Calculation of series resistance (Rs) from the linear model \n",
    "\n",
    "Rp = 1/(m1*1e-3)\n",
    "Rp = round(Rp,2) # Series resistance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the accurate Fill Factor (FF)\n",
    "\n",
    "P = J*V # mW/cm2\n",
    "P_min = np.min(P)\n",
    "FF_fit = P_min/(Voc_fit*Jsc_fit)\n",
    "FF_fit = round(FF_fit,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the whole J(V) curve and use vline and hline to delimite the 4-cuadrant\n",
    "# Print data inside the Voc_fit anc Jsc_fit limits \n",
    "\n",
    "plt.plot(V,J,'or')\n",
    "plt.ylim(np.min(J)-0.05,0+0.05)  #This y_limit can be modify for your values\n",
    "plt.xlim(0-0.1,Voc_fit+0.1)          #This x_limit can be modify for your values\n",
    "\n",
    "plt.title('poly-Si solar cell', fontsize =16)\n",
    "plt.ylabel('Current density $ \\:[mA/cm^2]$', fontsize=14)\n",
    "plt.xlabel('Voltage $[V]$', fontsize=14)\n",
    "\n",
    "plt.vlines(0, -0.7, 0.1, linestyle=\"dashed\")\n",
    "plt.hlines(0 , 0-0.2, Voc_fit+0.2, linestyle=\"dashed\")\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voc_fit = 2.0794 V\n",
      "Jsc_fit = -0.5863 mA/cm2\n",
      "FF_fit  = 0.696\n"
     ]
    }
   ],
   "source": [
    "#This are the accurate  PV parameters from the light JV-curve\n",
    "\n",
    "print (f'Voc_fit = {Voc_fit} V')\n",
    "print (f'Jsc_fit = {Jsc_fit} mA/cm2')\n",
    "print (f'FF_fit  = {FF_fit}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "fffd23037aa978196e94c512c7a73c2a2a1965fdec9e67b8f558484b87e4317c"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 64-bit ('base': conda)",
   "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": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}