Efficiency.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Electronic Load: HEWLETT-PACKARD,6050A,0,A.01.01\n",
      "\n",
      "Supply: HP6626A\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "from INST_6050A import INST_6050A\n",
    "from INST_6626A import INST_6626A\n",
    "\n",
    "load = INST_6050A(22)\n",
    "print (\"Electronic Load: \" + load.identify())\n",
    "psu = INST_6626A(3)\n",
    "print (\"Supply: \" + psu.identify())\n",
    "\n",
    "load.disable(1)\n",
    "psu.disable(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load testing from 0.0500A to 2.0000A in 0.0500A steps...\n",
      "and 15.0000V to 35.0000V in 20.0000V steps...\n",
      "\n",
      "V=15.0000\n",
      "I\tPin\tPout\tEff\n",
      "0.0520\t0.4379\t0.2673\t0.6104\n",
      "0.0990\t0.7336\t0.5089\t0.6937\n",
      "0.1510\t1.0468\t0.7761\t0.7414\n",
      "0.2000\t1.3648\t1.0280\t0.7532\n",
      "0.2500\t1.6627\t1.2850\t0.7728\n",
      "0.2990\t1.9726\t1.5369\t0.7791\n",
      "0.3490\t2.2512\t1.7939\t0.7969\n",
      "0.3980\t2.5508\t2.0457\t0.8020\n",
      "0.4510\t2.8384\t2.3181\t0.8167\n",
      "0.5000\t3.1037\t2.5700\t0.8281\n",
      "0.5500\t3.3422\t2.7885\t0.8343\n",
      "0.5990\t3.6045\t3.0369\t0.8425\n",
      "0.6490\t3.8700\t3.2904\t0.8502\n",
      "0.7010\t4.1340\t3.5541\t0.8597\n",
      "0.7480\t4.3827\t3.7924\t0.8653\n",
      "0.8000\t4.6497\t4.0560\t0.8723\n",
      "0.8500\t4.9155\t4.3095\t0.8767\n",
      "0.8990\t5.1822\t4.5579\t0.8795\n",
      "0.9460\t5.4341\t4.8114\t0.8854\n",
      "0.9980\t5.7015\t5.0599\t0.8875\n",
      "1.0480\t5.9730\t5.3134\t0.8896\n",
      "1.1000\t6.2404\t5.5770\t0.8937\n",
      "1.1470\t6.4961\t5.7350\t0.8828\n",
      "1.1990\t6.7680\t5.9800\t0.8836\n",
      "1.2490\t7.0420\t6.2450\t0.8868\n",
      "1.2980\t7.3120\t6.4900\t0.8876\n",
      "1.3480\t7.5825\t6.7400\t0.8889\n",
      "1.3970\t7.8415\t6.9850\t0.8908\n",
      "1.4470\t8.1120\t7.2350\t0.8919\n",
      "1.4990\t8.3865\t7.4950\t0.8937\n",
      "1.5490\t8.6589\t7.7450\t0.8945\n",
      "1.5950\t8.9184\t7.9750\t0.8942\n",
      "1.6480\t9.1929\t8.2400\t0.8963\n",
      "1.6970\t9.4674\t8.4850\t0.8962\n",
      "1.7470\t9.7455\t8.7350\t0.8963\n",
      "1.7960\t10.0050\t8.8720\t0.8868\n",
      "1.8460\t10.2811\t9.1190\t0.8870\n",
      "1.8980\t10.5630\t9.3760\t0.8876\n",
      "1.9480\t10.8420\t9.6230\t0.8876\n",
      "1.9970\t11.1202\t9.8650\t0.8871\n",
      "V=35.0000\n",
      "I\tPin\tPout\tEff\n",
      "0.0520\t0.5397\t0.2673\t0.4952\n",
      "0.0990\t0.9006\t0.5089\t0.5651\n",
      "0.1510\t1.2825\t0.7761\t0.6052\n",
      "0.2000\t1.6170\t1.0280\t0.6358\n",
      "0.2500\t1.9654\t1.2850\t0.6538\n",
      "0.2990\t2.4226\t1.5369\t0.6344\n",
      "0.3490\t2.6151\t1.7939\t0.6860\n",
      "0.3980\t3.1491\t2.0457\t0.6496\n",
      "0.4510\t3.5191\t2.3181\t0.6587\n",
      "0.5000\t3.8355\t2.5700\t0.6701\n",
      "0.5500\t4.1638\t2.8270\t0.6789\n",
      "0.5990\t4.5453\t3.0789\t0.6774\n",
      "0.6490\t4.8680\t3.3359\t0.6853\n",
      "0.7010\t5.2839\t3.6031\t0.6819\n",
      "0.7480\t5.5386\t3.8447\t0.6942\n",
      "0.8000\t5.8082\t4.0560\t0.6983\n",
      "0.8500\t6.0763\t4.3095\t0.7092\n",
      "0.8990\t6.3492\t4.5579\t0.7179\n",
      "0.9460\t6.6043\t4.8114\t0.7285\n",
      "0.9980\t6.8763\t5.0599\t0.7359\n",
      "1.0480\t7.1513\t5.3134\t0.7430\n",
      "1.1000\t7.4229\t5.5770\t0.7513\n",
      "1.1470\t7.6549\t5.8153\t0.7597\n",
      "1.1960\t7.9343\t6.0637\t0.7642\n",
      "1.2490\t8.2080\t6.3324\t0.7715\n",
      "1.2980\t8.4810\t6.5810\t0.7760\n",
      "1.3480\t8.7602\t6.8340\t0.7801\n",
      "1.3970\t9.0235\t6.9850\t0.7741\n",
      "1.4470\t9.2930\t7.2350\t0.7785\n",
      "1.4990\t9.5765\t7.4950\t0.7826\n",
      "1.5490\t9.8566\t7.7450\t0.7858\n",
      "1.5950\t10.1182\t7.9750\t0.7882\n",
      "1.6480\t10.4017\t8.2400\t0.7922\n",
      "1.6970\t10.6756\t8.4850\t0.7948\n",
      "1.7470\t10.9626\t8.7350\t0.7968\n",
      "1.7960\t11.2286\t8.9800\t0.7997\n",
      "1.8460\t11.5077\t9.2300\t0.8021\n",
      "1.8980\t11.8027\t9.4900\t0.8041\n",
      "1.9480\t12.0817\t9.7400\t0.8062\n",
      "1.9970\t12.3721\t9.8650\t0.7974\n"
     ]
    }
   ],
   "source": [
    "minV = 15\n",
    "maxV = 35\n",
    "stepV = 20\n",
    "\n",
    "minI = 0.05\n",
    "maxI = 2\n",
    "stepI = 0.05\n",
    "\n",
    "print(\"Load testing from {:0.4f}A to {:0.4f}A in {:0.4f}A steps...\".format(minI, maxI, stepI))\n",
    "print(\"and {:0.4f}V to {:0.4f}V in {:0.4f}V steps...\".format(minV, maxV, stepV))\n",
    "print(\"\")\n",
    "\n",
    "tests = []\n",
    "for V in np.arange(minV, maxV+stepV, stepV):\n",
    "    print(\"V={:0.4f}\".format(V))\n",
    "    print(\"I\\tPin\\tPout\\tEff\")\n",
    "    psu.setCurrent(4,2)\n",
    "    psu.setVoltage(4,V)\n",
    "    psu.enable(4)\n",
    "    load.set(3,\"CC\",0)\n",
    "    load.enable(3)\n",
    "    Pins = []\n",
    "    Pouts = []\n",
    "    Effs = []\n",
    "    Is = []\n",
    "    for I in np.arange(minI, maxI+stepI, stepI):\n",
    "        load.set(3,\"CC\",I)\n",
    "        time.sleep(1)\n",
    "        Pin = psu.getPower(4)\n",
    "        Pout = load.getPower(3)\n",
    "        Eff = Pout/Pin\n",
    "        Im=load.getCurrent(3)\n",
    "        print(\"{:0.4f}\\t{:0.4f}\\t{:0.4f}\\t{:0.4f}\".format(Im,Pin,Pout,Eff))\n",
    "        Pins.append(Pin)\n",
    "        Pouts.append(Pout)\n",
    "        Is.append(Im)\n",
    "        Effs.append(Eff)\n",
    "    load.disable(3)\n",
    "    tests.append([V,Is,Pins,Pout,Effs])\n"
   ]
  },
  {
   "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "for t in tests:\n",
    "    plt.plot(t[1],t[4], '-o', label='{:.4f}V'.format(t[0]))    \n",
    "plt.legend()\n",
    "plt.title('Efficiency')\n",
    "plt.xlabel('Load Current (A)')\n",
    "plt.ylabel('Efficiency $P_{out}$/$P_{in}$')\n",
    "plt.ylim(top=1)\n",
    "plt.show()"
   ]
  }
 ],
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