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{
"metadata": {
"name": "",
"signature": "sha256:89d211f2bab9ed7eb89e6e1658fcc2ecaf90b3f2ef927e14d02a2cb79920c159"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Ch-8, Hydro-Electric Plants"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"example 8.1 Page 134"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"h=100 #given height\n",
"q=200 #discharge\n",
"e=0.9 #efficiency\n",
"p=(735.5/75)*q*h*e\n",
"print \"\\npower developed by hydro plant is %0.2f MW\"%(p/1e3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"power developed by hydro plant is 176.52 MW\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"example 8.2 page 134"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"flow=[0, 1000, 800, 600 ,400 ,400 ,1200, 2400 ,2400, 1000 ,400 ,400 ,1000] #flow in matrix from in the order of months\n",
"y=range(0,13)\n",
"yy = [(0,1),(1,2),(2,3),(3,4),(4,5)]\n",
"xx = [(0,1),(1,2),(2,3),(3,4),(4,5)]\n",
"h=150\n",
"e=0.85\n",
"avg=sum(flow)/12\n",
"print \"\\naverage rate of inflow is %dcu-m/sec\"%(avg)\n",
"p=(735.5/75)*avg*h*e\n",
"print \"\\npower developed is %0.2f MW\"%(p/1e3)\n",
"%matplotlib inline\n",
"from matplotlib.pyplot import plot, title, xlabel, ylabel, show\n",
"plot(y,flow)\n",
"title('hydrograph')\n",
"xlabel('months')\n",
"ylabel('run in cu-m/sec')\n",
"show()\n",
"print \"hydrograph is ploted in figure\"\n",
"flow1 = range(0,12)\n",
"for x in range(0,12):\n",
" t=flow[x]\n",
" a=avg\n",
" if t<a or t==avg:\n",
" t=0\n",
" else:\n",
" t=t-1000\n",
" #end\n",
" flow1[x]=t \n",
" #end\n",
"sto=sum(flow1)\n",
"print \"\\nstorage capacity of given plant is %dsec-m-month\"%(sto)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"average rate of inflow is 1000cu-m/sec\n",
"\n",
"power developed is 1250.35 MW\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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HHli5a0vD3n4bjjgC3nsPNtss6WikPitWhLuNJ54Iw9ur2erVoZ/nzjs3/qU3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\nyZQ0RESkZEoaIiJSsv8PaXLtlrcgX/kAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7fb702908bd0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"hydrograph is ploted in figure\n",
"\n",
"storage capacity of given plant is 3000sec-m-month\n"
]
}
],
"prompt_number": 34
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"example 8.3 Page 135"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flow=[1500, 1000, 500, 500, 500, 1200, 2900, 2900, 1000, 400 ,600 ,1600]\n",
"cod=1000#constant demand\n",
"#plot2d2(flow)\n",
"%matplotlib inline\n",
"from matplotlib.pyplot import plot, title, xlabel, ylabel, show\n",
"plot(flow)\n",
"title('hydrograph for exp 8.3')\n",
"xlabel('months')\n",
"ylabel('run off in m**3/sec')\n",
"avg=sum(flow)/12\n",
"if cod<avg:\n",
" flow1 = range(0,6)\n",
" for x in range(0,6):\n",
" t=flow[x]\n",
" if t>cod|t==avg:\n",
" t=0\n",
" else:\n",
" t=cod-t\n",
" #end \n",
" flow1[x]=t \n",
" #end\n",
" \n",
" else:\n",
" flow = range(0,12)\n",
" flow1 = range(0,12)\n",
" for x in range(0,12):\n",
" t=flow[x]\n",
" a=cod\n",
" if t>a|t==avg:\n",
" t=0\n",
" else:\n",
" t=t-cod\n",
" #end\n",
" flow1[x]=t \n",
" #end\n",
"#end\n",
"\n",
"sto=sum(flow1)\n",
"print \"storage capacity of plant is %dsec-m-month\"%(sto)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"storage capacity of plant is -11934sec-m-month\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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JpwJXA+8GhgO/lFS6lYJd3yi/N95I3/a+9730RcH6btw41zWqcfPNaQ36Vh+b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\n",
"text": [
"<matplotlib.figure.Figure at 0x7fb702908050>"
]
}
],
"prompt_number": 35
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"example 8.4 Page 135"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flow=[1500, 1000, 500, 500, 500, 1200, 2900, 2900, 1000, 400 ,600 ,1600]\n",
"cod=1000#constant demand\n",
"#[m n]=size(flow)len\n",
"n=len(flow)\n",
"mf = range(0,n)\n",
"mf[0]=1500\n",
"for i in range(2,n):\n",
" mf[i]=mf[i-1]+flow[i]\n",
"%matplotlib inline\n",
"from matplotlib.pyplot import plot, title, xlabel, ylabel, show\n",
"\n",
"plot(mf)\n",
"from numpy import arange\n",
"dd= arange(1,mf[n-1],cod)\n",
"\n",
"avg=sum(flow)/12\n",
"if cod<avg:\n",
" for x in range(0,6):\n",
" t=flow[x]\n",
" if t>cod|t==avg:\n",
" t=0\n",
" else:\n",
" t=cod-t\n",
" #end\n",
" flow1[x]=t \n",
" #end\n",
" \n",
"else: \n",
" for x in range(0,12):\n",
" t=flow[x]\n",
" a=cod\n",
" if t>a|t==avg:\n",
" t=0\n",
" else:\n",
" t=t-cod\n",
" #end\n",
" flow1[x]=t \n",
" #end\n",
"#end\n",
"\n",
"sto=sum(flow1)\n",
"print \"storage capacity of plant is %dsec-m-month\"%(sto)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"storage capacity of plant is -5149sec-m-month\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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W5b1eDPRrzwB+8hPYYYdkBtKll8KJJ667FjBhgmsBZlaZSiEhZF/EAA44IPmm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\nZmYA/H9u+wc6AAAABElEQVS6oEf5Os2H4wAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fb702585410>"
]
}
],
"prompt_number": 36
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"example 8.5 Page 154"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flow=[80, 50 ,40 ,20 ,0 ,100, 150 ,200 ,250 ,120 ,100, 80]\n",
"h=100; e=80\n",
"%matplotlib inline\n",
"from matplotlib.pyplot import plot, title, xlabel, ylabel, show, subplot\n",
"\n",
"subplot(211)\n",
"plot(flow)\n",
"title('hydrograph')\n",
"xlabel('months')\n",
"ylabel('run off,millon m**3/month' )\n",
"fd=sorted(flow)\n",
"\n",
"subplot(212)\n",
"plot(fd)\n",
"title('flow duration')\n",
"xlabel('months')\n",
"ylabel('run off')\n",
"\n",
"t= range(0,12)\n",
"for x in range(1,10):\n",
" d=fd[x]\n",
" ad=fd[(x-1)]\n",
" if d==ad:\n",
" t[x]=[]\n",
" t[x-1]=t[x-1]+1\n",
" fd[x]=[]\n",
" #end\n",
"#end\n",
"ffw=fd+t\n",
"print \"load duration data is as under\"\n",
"print ffw\n",
"mf=sum(flow)*10**6/(30*24*3600)\n",
"print \"(a)\"\n",
"print \"meanflow is %fm**3-sec\"%(mf)\n",
"print \"(b)\"\n",
"p=(735.5/75)*mf*h*e\n",
"print \"power delevered in %dkW=%.3fMW\"%(p,p/1000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"load duration data is as under\n",
"[0, 20, 40, 50, 80, [], 100, [], 120, 150, 200, 250, 0, 1, 2, 3, 5, [], 7, [], 8, 9, 10, 11]\n",
"(a)\n",
"meanflow is 459.104938m**3-sec\n",
"(b)\n",
"power delevered in 36018312kW=36018.313MW\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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6zrlm++KLsFjt5puhVavQdfTAA759diXx5OCcy9qCBWFA+V//gkMP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\nJg4AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7fb70255f810>"
]
}
],
"prompt_number": 37
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"example 8.6 Page 156"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mh=205#mean height\n",
"a=1000*10**6#in miters\n",
"r=1.25#annual rain fall\n",
"er=0.8#efficiency\n",
"lf=0.75#load factor\n",
"hl=5#head loss\n",
"et=0.9#efficiency of turbine\n",
"eg=0.95#efficiency of generator\n",
"wu=a*r*er/(365*24*3600)\n",
"print \"\\nwater used is \\t\\t%fm**3/sec\"%(wu)\n",
"eh=mh-hl\n",
"print \"\\neffective head is \\t%dm\"%(eh)\n",
"p=(735.5/75)*(wu*eh*et*eg)\n",
"print \"\\npower generated is \\t%fkW =\\t%fMW\"%(p,p/1000)\n",
"pl=p/lf\n",
"print \"\\npeak load is \\t\\t%fMw \\ntherefore the MW rating of station is \\t%fMW\"%(pl/1000,pl/1000)\n",
"if eh<=200:\n",
" print \"\\nfor a head above 200m pelton turbine is suitable,\\nfrancis turbine is suitable in the range of 30m-200m.,\\nhowever pelton is most suitable\"\n",
"else:\n",
" print \"only pelton turbine is most suitable\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"water used is \t\t31.709792m**3/sec\n",
"\n",
"effective head is \t200m\n",
"\n",
"power generated is \t53175.418569kW =\t53.175419MW\n",
"\n",
"peak load is \t\t70.900558Mw \n",
"therefore the MW rating of station is \t70.900558MW\n",
"\n",
"for a head above 200m pelton turbine is suitable,\n",
"francis turbine is suitable in the range of 30m-200m.,\n",
"however pelton is most suitable\n"
]
}
],
"prompt_number": 38
}
],
"metadata": {}
}
]
}
|