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@dsoto
Created February 7, 2014 23:01
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{
"metadata": {
"name": "generator linearity"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Coteaux Kohler Generator\n",
"\n",
"- from data sheet in haiti folder\n",
"- coteaux kohler\n",
"- percent load, standby lph, prime lph\n",
"- 100%, 44.3, 40.6\n",
"- 75%, 35.1, 32.3\n",
"- 50%, 26.3, 24.0\n",
"- 25%, 16.2, 14.4\n",
"- no load consumption not given\n",
"- from report: rated active power 125kW\n",
"- Manufactured 1994. \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"power_kW = 125\n",
"load = array((1.0, 0.75, 0.5, 0.25))\n",
"consumption = array((40.6, 32.3, 24.0, 14.4))\n",
"plot(power_kW * load, consumption / load / power_kW)\n",
"xlabel('Load (kW)')\n",
"ylabel('Fuel consumption (liters/kWh)')\n",
"ylim((0.2, 0.5))\n",
"show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
"For more information, type 'help(pylab)'.\n"
]
},
{
"output_type": "display_data",
"png": 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PP/+MadOm4ZdffpG1X4XKoUFERNU9+Af14sWLTT6W0dDYsGEDVCoV3n//fcNn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\nMTSIiEi2/w90qOhp7sIdMQAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 52
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Port-A-Piment FGWilson Generator\n",
"\n",
"- portapiment FGWilson\n",
"- @50Hz percent load, standby lph, prime lph\n",
"- 110%, -, 54.3\n",
"- 100%, 45.3, 49.4\n",
"- 75%, 41.0, 37.4\n",
"- 50%, 28.0, 25.6\n",
"- how do i quantify how close to linear?\n",
"- can i calculate $/kWh at each given point?\n",
"- from report: rated active power 199 kW\n",
"- Manufactured 2009.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"power_kW = 199\n",
"load = array((1.1, 1.0, 0.75, 0.50))\n",
"consumption = array((54.3, 49.4, 37.4, 25.6))\n",
"plot(power_kW * load, consumption / load / power_kW)\n",
"xlabel('Load (kW)')\n",
"ylabel('Fuel consumption (liters/kWh)')\n",
"ylim((0.2, 0.5))\n",
"show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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LRET246bUhlUqlc1t9+7di/Xr1yMzM1N2XzltHdGCBQuauoQGYf1Ny5nrd+ba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\n4CAiIlkYHEREJMv/A7tsOgV+Pj9aAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
}
],
"metadata": {}
}
]
}
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