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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NOJ WakeModel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Try this yourself](https://colab.research.google.com/github/DTUWindEnergy/PyWake/blob/master/docs/notebooks/noj.ipynb) (requires google account)\n"
"execution_count": 0,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"# Install PyWake if needed\n",
"try:\n",
" import py_wake\n",
"except ModuleNotFoundError:\n",
" !pip install py_wake\n"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from py_wake.examples.data.hornsrev1 import V80, Hornsrev1Site, wt9_x, wt9_y\n",
"from py_wake.wake_models.noj import NOJ\n",
"windTurbines = V80()\n",
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `NOJ` is a subclass of the general `WakeModel` class, see documentation [here](https://topfarm.pages.windenergy.dtu.dk/PyWake/wake_models/WakeModel.html)\n",
"\n",
"It implements the wake model of Niels Otto Jensen, described in \"A note on wind generator interaction.\" (1983)\n",
"\n",
"The implementation of `WakeModel` is highly vectorized and therefore suffixes are used to indicate the dimension of variables. The suffixes used in this context are:\n",
"\n",
"- i: turbines ordered by id\n",
"- k: wind speeds\n",
"- l: wind directions\n",
"\n",
"This means that `WS_ilk[0,1,2]` holds the wind speed at the first turbine for the second wind direction and third wind speed\n"
]
},
{
"attachments": {
"noj_2wt_0_10.png": {
"image/png": 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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"`WakeModel` contains a method, [```calc_wake```](https://topfarm.pages.windenergy.dtu.dk/PyWake/wake_models/WakeModel.html#py_wake.wake_model.WakeModel.calc_wake), to calculate the effective wind speed, turbulence intensity (not implemented yet), power and thrust coefficient.\n",
"\n",
"Let us try to calculate the effective wind speed for two V80 turbines separated by 200m in 10m/s and wind direction parallel to a line between the two turbines\n",
""
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Turbine 0\n",
"Power production 1341000.00W\n",
"\n",
"Turbine 1\n",
"Power production 596326.74W\n",
"\n"
]
}
],
"source": [
"WS_eff_ilk, TI_eff_ilk, power_ilk, ct_ilk,*_ = wake_model.calc_wake(\n",
" x_i=[0,0],\n",
" y_i=[200,0],\n",
" ws=10,\n",
" wd=0)\n",
"\n",
"for i in [0,1]:\n",
" print ('Turbine', i)\n",
" print ('Effective wind speed %fm/s'%WS_eff_ilk[i,0,0])\n",
" print ('Power production %.2fW'%power_ilk[i,0,0])\n",
" print ('Thrust coefficient %f'%ct_ilk[i,0,0])\n",
" print()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To calculate this, `calc_wake`, uses two wake-model specific methods, `calc_deficit` and `calc_effective_WS`.\n",
"\n",
"`calc_deficit` calculates the deficit:"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.422344326030697\n"
]
}
],
"source": [
"deficit = wake_model.calc_deficit(\n",
" WS_lk=np.array([[10]]), # wind speed at current turbine\n",
" D_src_l=np.array([80]), # diameter of current turbine\n",
" D_dst_jl=np.array([[80]]), # diameter of downstream turbine(s)\n",
" dw_jl=np.array([[200]]), # down wind distance\n",
" cw_jl=np.array([[0]]), # cross wind distance (both horizontal and vertical)\n",
" ct_lk=np.array([[0.793000]])) # thrust coefficient\n",
"print (deficit[0,0,0])\n"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
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"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x = np.arange(-300,301,1.)\n",
"y = np.arange(-100,1001,1)\n",
"X,Y = np.meshgrid(x,y)\n",
"down_stream = Y>0\n",
"deficit_map=np.zeros_like(X)\n",
"\n",
"deficit_map[down_stream] = wake_model.calc_deficit(\n",
" WS_lk=np.array([[10]]), # wind speed at current turbine\n",
" D_src_l=np.array([80]), # diameter of current turbine\n",
" D_dst_jl=np.zeros_like(X)[down_stream][:,np.newaxis], # diameter of downstream turbine(s)\n",
" dw_jl=Y[down_stream][:,np.newaxis], # down wind distance\n",
" cw_jl=np.abs(X[down_stream])[:,np.newaxis], # cross wind distance (both horizontal and vertical)\n",
" ct_lk=np.array([[0.793000]]))[:,0,0] # thrust coefficient\n",
"\n",
"c = plt.contourf(Y,X,deficit_map,100)\n",
"plt.colorbar(c)\n",
"plt.plot([0,0],[-40,40],'r',lw=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"while `calc_effective_WS` calculates the effective wind speed by subtracting the deficits from all upstream turbines from the local wind speed. For `NOJ` it subtracts the square root of the sum of squared deficits"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[7.57765567]])"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wake_model.calc_effective_WS(\n",
" WS_lk=np.array([[10]]), \n",
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, `WakeModel`, contains the method `wake_map` to find the effective wind speed at arbitrary positions"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[7.57765567]]]\n"
]
}
],
"source": [
"#calculate the wake 200m down stream of a V80\n",
"X_j, Y_j, WS_eff_jlk, WS_jlk, P_ilk = wake_model.ws_map(x_j=[200], y_j=[0], h=70, \n",
" wt_x_i=[0], wt_y_i=[0], wt_type_i=[0], wt_h_i=[70], \n",
" wd=270, ws=10)\n",
"print (WS_eff_jlk)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For standard purposes, however, we do not call these methods manually. Instead we use the functions in `AEPCalculator`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Calculate AEP**"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AEP pr turbine: [9.00648938 9.17948543]\n"
]
},
{
"data": {
"text/plain": [
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from py_wake.aep_calculator import AEPCalculator\n",
"\n",
"site = Hornsrev1Site()\n",
"aep_calc = AEPCalculator(wake_model)\n",
"\n",
"aep_gwh_ilk = aep_calc.calculate_AEP(x_i=[0,0], y_i=[0,-200])\n",
"aep_gwh_noloss_ilk = aep_calc.calculate_AEP_no_wake_loss(x_i=[0,0], y_i=[0,-200])\n",
"\n",
"\n",
"# AEP pr turbine\n",
"print ('AEP pr turbine:', aep_gwh_ilk.sum((1,2)))\n",
"\n",
"# AEP pr wind direction\n",
"plt.plot(aep_gwh_noloss_ilk[:,:,7].sum(0), label='Without wake loss')\n",
"plt.plot(aep_gwh_ilk[:,:,7].sum(0), label='With wake loss')\n",
"plt.title('Wind speed: 10m/s')\n",
"plt.xlabel('Wind direction [deg]')\n",
"plt.ylabel('AEP [GWh]')\n",
"plt.legend()\n",
"\n",
"# AEP pr wind speed\n",
"plt.figure()\n",
"plt.plot(site.default_ws, aep_gwh_noloss_ilk[:,0].sum(0), label='Without wake loss')\n",
"plt.plot(site.default_ws, aep_gwh_ilk[:,0].sum(0), label='With wake loss')\n",
"plt.title('Wind direction: 0deg')\n",
"plt.xlabel('Wind speed [deg]')\n",
"plt.ylabel('AEP [GWh]')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Wake map**"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"x,y = wt9_x, wt9_y\n",
"aep_calc.plot_wake_map(wt_x=x, wt_y=y, wd=[0], ws=[10])\n",
"windTurbines.plot(x, y)\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Effective wind speed, power and thrust coefficient**"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Effective wind speed: wt0: 10.000000\twt1: 7.577656\n",
"Power: wt0: 1341000W\twt1: 596326W\n",
"Thrust coefficient: wt0: 0.793000\twt1: 0.805578\n",
"Probability of ws=10m/s and wd=0deg: 0.000103\n"
]
}
],
"source": [
"# wind from 0 deg(index=0) and 10m/s (index=7)\n",
"\n",
"aep_calc.calculate_AEP(x_i=[0,0], y_i=[0,-200])\n",
"print (\"Effective wind speed: wt0: %f\\twt1: %f\"%tuple(aep_calc.WS_eff_ilk[:,0,7])) \n",
"print (\"Power: wt0: %dW\\twt1: %dW\"%tuple(aep_calc.power_ilk[:,0,7])) \n",
"print (\"Thrust coefficient: wt0: %f\\twt1: %f\"%tuple(aep_calc.ct_ilk[:,0,7])) \n",
"print (\"Probability of ws=10m/s and wd=0deg: %f\"%aep_calc.P_ilk[0,0,7])"
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}