diff --git a/notebooks/B01_wind_turbine_generic.ipynb b/notebooks/B01_wind_turbine_generic.ipynb new file mode 100644 index 000000000..f4613d4a0 --- /dev/null +++ b/notebooks/B01_wind_turbine_generic.ipynb @@ -0,0 +1,656 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compatibility of wind turbines with RAS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License\n", + "\n", + "```\n", + "Wind turbine compatibility with RAS observations.\n", + "Copyright (C) 2015+ Benjamin Winkel (bwinkel@mpifr.de)\n", + "\n", + "This program is free software; you can redistribute it and/or\n", + "modify it under the terms of the GNU General Public License\n", + "as published by the Free Software Foundation; either version 2\n", + "of the License, or (at your option) any later version.\n", + "\n", + "This program is distributed in the hope that it will be useful,\n", + "but WITHOUT ANY WARRANTY; without even the implied warranty of\n", + "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n", + "GNU General Public License for more details.\n", + "\n", + "You should have received a copy of the GNU General Public License\n", + "along with this program; if not, write to the Free Software\n", + "Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import itertools\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from astropy import units as u\n", + "from pycraf import conversions as cnv\n", + "from pycraf import pathprof, protection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "Wind turbines are not radio services in the strict sense, because they don't emit wanted radio emission. However, having large heights they are visible over large distances and electrical devices (e.g., the generator, or digital control units) may be placed in the hub. Even when radio telescopes are put to a remote location, ideally well-shielded by terrain, the wind turbine might be above the local horizon. In this example, we will show how one can calculate separation distances (aka exclusion zones) to protect RAS observations from emission.\n", + "\n", + "For a case, where no frequency allocation is applicable, we have to find some other way to define protection criteria. For industrial devices there is CISPR standard, which contains permitted emission limits." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CISPR-11\n", + "\n", + "For an industrial plant the electrical field limits are defined for a reference distance of 30 m; see Table 17 of the European standard CISPR-11 (= EN 55011 in Germany) for devices of group 1 class A:\n", + "\n", + "$$\n", + "E_\\mathrm{lim}~\\left[\\mathrm{dB}_{\\mu \\mathrm{V} / \\mathrm{m}}\\right]= \\begin{cases}\n", + "30 & \\text{for}\\,~ f < 230~\\mathrm{MHz} \\\\\n", + "37 & \\text{for}\\,~ 230~\\mathrm{MHz} < f < 1~\\mathrm{GHz}\n", + "\\end{cases}\n", + "$$\n", + "\n", + "No limits are provided for $f > 1~\\mathrm{GHz}$, unfortunately. Therefore, we will simply use the limit of $37~\\mathrm{dB(\\mu V/m)}$ for all $f > 1~\\mathrm{GHz}$.\n", + "\n", + "The CISPR standard detector is a quasi-peak (QP) detector with a bandwidth of 120 kHz. How the QP detector converts to an RMS (which is more suitable to compare with the RA.769 thresholds) or AV (average) detector depends strongly on the input signal. For a continouus-wave signal (CW), the QP and RMS produce the same results. For stochastic signals, to convert to an RMS detector one has to subtract 5.5 dB. As we have no further information, we assume that the signals are CW-like, which is the expectation for the kind of equipment used in wind turbines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note, that the CISPR detector has only a bandwidth of 120 kHz in contrast to the RAS bandwidths of several MHz (continuum). This would be OK, if we assumed that only one peak would be present in the protected bands (which may be reasonable for wind turbine equipment, which could be more of a line spectrum). However, there is the possibility that the full RAS band is polluted and we will assume this for our calculations of the separation distances, as a worst-case scenario. It would be straight-forward to calculate the limits for a one-channel (120 kHz) interference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Minimal coupling loss\n", + "We will start by calculating the *minimal coupling loss*, i.e., the least amount of path attenuation (propagation loss), which would be necessary to ensure compatibility. Since the path attenuation is dependend on the distance between interferer and victim service, the MCL translates to a separation distance, which we will infer further below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we query the RA.769 limits for continuum in the lower-frequency bands. It is unlikely, that a wind turbine will be problematic at high frequencies only, so we restrict ourselves to the low frequencies for now." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "<Table length=5>\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
frequencybandwidthT_AT_rxT_rmsP_rms_nuPlimPlim_nuSlimSlim_nuEfieldEfield_norm
MHzMHzKKmKdB(W / Hz)dB(W)dB(W / Hz)dB(W / m2)dB(W / (Hz m2))dB(uV2 / m2)dB(uV2 / m2)
float64float64float64float64float64float64float64float64float64float64float64float64
325740600.870-259.2-201.0-269.2-189.3-257.5-43.5-51.7
408425600.962-258.8-202.9-268.8-189.2-255.1-43.4-49.3
611620600.730-260.0-202.2-270.0-185.0-252.8-39.2-47.0
14142712100.095-268.8-204.5-278.8-180.1-254.4-34.3-48.6
16651012100.156-266.7-206.7-276.7-180.8-250.8-35.0-45.0
" + ], + "text/plain": [ + "\n", + "frequency bandwidth T_A ... Slim_nu Efield Efield_norm \n", + " MHz MHz K ... dB(W / (Hz m2)) dB(uV2 / m2) dB(uV2 / m2)\n", + " float64 float64 float64 ... float64 float64 float64 \n", + "--------- --------- ------- ... --------------- ------------ ------------\n", + " 325 7 40 ... -257.5 -43.5 -51.7\n", + " 408 4 25 ... -255.1 -43.4 -49.3\n", + " 611 6 20 ... -252.8 -39.2 -47.0\n", + " 1414 27 12 ... -254.4 -34.3 -48.6\n", + " 1665 10 12 ... -250.8 -35.0 -45.0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ra769_tab_db = protection.ra769_limits(mode='continuum')[4:9]\n", + "ra769_tab_db" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate CISPR power limit for these frequencies:\n", + "\n", + "1. Assuming a single line polluting the RAS band\n", + "2. Assuming pollution over the full RAS band\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 37. 37. 37. 37. 37.] dB(uV2 / m2) @ 120.0 kHz\n" + ] + } + ], + "source": [ + "freqs = ra769_tab_db['frequency']\n", + "ras_bw = ra769_tab_db['bandwidth']\n", + "\n", + "detector_dist=30 * u.m\n", + "# we query the QP values and assume that they equal 'RMS';\n", + "# as discussed above\n", + "detector_type='QP' \n", + "\n", + "# case 1\n", + "cispr11_lim, cispr11_bw = protection.cispr11_limits(\n", + " freqs, detector_type=detector_type, detector_dist=detector_dist\n", + " )\n", + "print(cispr11_lim, '@', cispr11_bw)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's convert this to the emitted power levels for both cases. In the first case, it's straight forward. In the second case, we assume that each 120-kHz channel in the RAS band is \"using\" the maximum CISPR-11 level." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "case 1: [-68.22578194 -68.22578194 -68.22578194 -68.22578194 -68.22578194] dB(W)\n", + "case 2: [-50.82215504 -53.10694833 -51.23608189 -44.70395675 -49.0175944 ] dB(W)\n" + ] + } + ], + "source": [ + "cispr11_pow_lim_1 = cnv.ptx_from_efield(cispr11_lim, detector_dist, 0 * cnv.dBi)\n", + "print('case 1:', cispr11_pow_lim_1.to(cnv.dB_W))\n", + "\n", + "# case 2\n", + "cispr11_pow_lim_2 = cispr11_pow_lim_1 * ras_bw / cispr11_bw\n", + "print('case 2:', cispr11_pow_lim_2.to(cnv.dB_W))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, in case 1 all numbers are equal (because the electrical field limit was also the same for all these frequencies)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MCL follows directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-68.22578194, -68.22578194, -68.22578194, -68.22578194, -68.22578194])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cispr11_pow_lim_1.to(cnv.dB_W).value" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-201.00659898, -202.85480637, -202.18266254, -204.52327303,\n", + " -206.68009185])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Plim = ra769_tab_db['Plim']\n", + "Plim.data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 132.78081705 134.62902443 133.9568806 136.2974911 138.45430992] dB\n", + "[ 150.18444394 149.74785804 150.94658065 159.81931628 157.66249746] dB\n" + ] + } + ], + "source": [ + "MCL_1 = (\n", + " cispr11_pow_lim_1.to_value(cnv.dB_W) - \n", + " ra769_tab_db['Plim'].data\n", + " ) * cnv.dB\n", + "MCL_2 = (\n", + " cispr11_pow_lim_2.to_value(cnv.dB_W) - \n", + " ra769_tab_db['Plim'].data\n", + " ) * cnv.dB\n", + "\n", + "print(MCL_1)\n", + "print(MCL_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+/ZkzZw5btmxh4cKFvPvuu+WmVEeOHElOTg6bN2/mrbfeol+/fuW+m5OTw/bt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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.close()\n", + "fig = plt.figure(figsize=(10, 8))\n", + "ax1 = fig.add_axes((0.1, 0.5, 0.8, 0.4))\n", + "ax2 = fig.add_axes((0.1, 0.1, 0.8, 0.4))\n", + "ax1.plot(freqs, cispr11_pow_lim_2.to(cnv.dB_W), 'o', label='CISPR-11, full RAS band')\n", + "ax1.plot(freqs, cispr11_pow_lim_1.to(cnv.dB_W), 'o', label='CISPR-11, single chan')\n", + "ax1.plot(freqs, ra769_tab_db['Plim'].data, 'o', label='RA 769')\n", + "ax2.plot(freqs, MCL_2.to(cnv.dB), 'o', label='CISPR-11, full RAS band')\n", + "ax2.plot(freqs, MCL_1.to(cnv.dB), 'o', label='CISPR-11, single chan')\n", + "ax1.legend(*ax1.get_legend_handles_labels(), loc='center right', numpoints=1, fontsize=12)\n", + "for ax in [ax1, ax2]:\n", + " ax.grid()\n", + " ax.set_xlim((301, 1690))\n", + "\n", + "ax1.tick_params(axis='x', which='both', labelbottom='off')\n", + "ax2.set_xlabel('Frequency [MHz]', fontsize=12)\n", + "ax1.set_ylabel('Power [dB(Watt)]', fontsize=12)\n", + "ax2.set_ylabel('MCL [dB]', fontsize=12)\n", + "ax1.set_ylim((-219, -30))\n", + "ax2.set_ylim((125, 165))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It can be seen, that the CISPR-11 levels refer to relatively high permitted transmission power levels. As a consequence, the MCL is large." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Path attenuation and separation distances\n", + "Calculating the path propagation loss is fairly simple with pycraf, and since it has been explained in the other notebooks in detail, we will just post the necessary code without much explanation here.\n", + "\n", + "In this notebook, we'll start with a generic (aka flat-Earth) analysis. In [the next notebook](B02_wind_turbine_terrain.ipynb) a site-specific study is performed. For this example, we will look at the 610 MHz band." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MCL: 150.9 dB\n" + ] + } + ], + "source": [ + "MCL_610 = MCL_2[2] # 2nd scenario (full RAS band) as worst-case\n", + "print('MCL: {:.1f}'.format(MCL_610))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "distances = np.arange(0., 200., 0.1) # 200 km in steps of 100-m\n", + "zero_hprof = np.zeros_like(distances)\n", + "\n", + "# define dummy coordinates\n", + "lon_t, lon_r = 0 * u.deg, 0 * u.deg\n", + "lat_t, lat_r = 50 * u.deg, 50 * u.deg\n", + "h_tg, h_rg = 160 * u.m, 50 * u.m # Tx and Rx are well above clutter\n", + "G_t, G_r = 0. * cnv.dBi, 0. * cnv.dBi\n", + "\n", + "frequency = 610 * u.MHz # choose any frequency, you like\n", + "temperature = 293.15 * u.K\n", + "pressure = 1013. * u.hPa\n", + "\n", + "time_percent = 2 * u.percent\n", + "DN, N0 = 38. * cnv.dimless / u.km, 324. * cnv.dimless\n", + "\n", + "zone_t, zone_r = pathprof.CLUTTER.UNKNOWN, pathprof.CLUTTER.UNKNOWN" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# look at path attenuation as a function of distance\n", + "\n", + "attens = np.zeros(\n", + " distances.shape, \n", + " dtype=np.dtype([\n", + " ('LOS', 'f8'), ('Diffraction', 'f8'), ('Troposcatter', 'f8'), \n", + " ('Ducting', 'f8'), ('Total', 'f8'),\n", + " ('Total w. clutter', 'f8'), ('Total w. clutter/gain', 'f8')\n", + " ])\n", + " )\n", + "attens[:5] = 1000. # dB; initialize to huge value \n", + "\n", + "for idx in range(5, len(distances)):\n", + "\n", + " pprop = pathprof.PathProp(\n", + " frequency,\n", + " temperature, pressure,\n", + " lon_t, lat_t,\n", + " lat_t, lat_r,\n", + " h_tg, h_rg,\n", + " 30. * u.m, # dummy height resolution\n", + " time_percent,\n", + " zone_t=zone_t, zone_r=zone_r,\n", + " delta_N=DN, N0=N0,\n", + " hprof_dists=distances[:idx] * u.km,\n", + " hprof_heights=zero_hprof[:idx] * u.m,\n", + " hprof_bearing=90 * u.deg, # have to provide dummy bearings\n", + " hprof_backbearing=-90 * u.deg,\n", + " )\n", + " \n", + " tot_loss = pathprof.loss_complete(pprop, G_t, G_r)\n", + " attens[idx] = u.Quantity(tot_loss).value" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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2n/An/rRXr6kAJjICgrEY73d3m2ErHrrWD7GhcZHDwRFpaRwevx2UkoJLnQhF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UmSJEnSOoomgM1mNgDtmzQlmhTF25IkSZJUZIrjRsx1ifnMB2DXphU5rkaSJEnKrdraWmbOnElN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\npJT5P36fP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.close()\n", + "fig = plt.figure(figsize=(14, 8))\n", + "ax = fig.add_axes((0.1, 0.1, 0.8, 0.8))\n", + "for (name, style) in zip(\n", + " attens.dtype.names[:5], \n", + " ['b-', 'r-', 'c-', 'm-', 'k--']\n", + " ):\n", + " ax.plot(distances[5:], attens[name][5:], style, label=name, lw=2)\n", + "\n", + "ax.legend(\n", + " *ax.get_legend_handles_labels(), \n", + " loc='lower right', fontsize=8, handlelength=3\n", + " )\n", + "ax.set_xlim((0, 199))\n", + "ax.set_ylim((71, 179))\n", + "ax.set_xlabel('Distance [km]')\n", + "ax.set_ylabel('Path attenuation [dB]')\n", + "ax.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The path attenuation is very sensitive to the wind turbine hub height:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "heights = [80, 120, 160, 200] * u.m\n", + "\n", + "attens = np.zeros(\n", + " distances.shape, \n", + " dtype=np.dtype([\n", + " ('80 m', 'f8'), ('120 m', 'f8'), ('160 m', 'f8'), ('200 m', 'f8'), \n", + " ])\n", + " )\n", + "heigths_name = attens.dtype.names\n", + "attens[:5] = 1000. # dB; initialize to huge value " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for idx, (_h_tg, hname) in itertools.product(\n", + " range(5, distances.size), zip(heights, heigths_name)\n", + " ):\n", + " \n", + " pprop = pathprof.PathProp(\n", + " frequency,\n", + " temperature, pressure,\n", + " lon_t, lat_t,\n", + " lat_t, lat_r,\n", + " _h_tg, h_rg,\n", + " 30. * u.m, # dummy height resolution\n", + " time_percent,\n", + " zone_t=zone_t, zone_r=zone_r,\n", + " delta_N=DN, N0=N0,\n", + " hprof_dists=distances[:idx] * u.km,\n", + " hprof_heights=zero_hprof[:idx] * u.m,\n", + " hprof_bearing=90 * u.deg, # have to provide dummy bearings\n", + " hprof_backbearing=-90 * u.deg,\n", + " )\n", + " \n", + " res = pathprof.loss_complete(pprop, G_t, G_r)\n", + " \n", + " attens[hname][idx] = res[4].value # assign total path loss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the margin tells us, how large the necessary separation distance would need to be:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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zc1nzww/YgQBPTyKDgni6XTtiGjcmRJseSw1Q4BMRkTorsyCTeenzmJs+l0UZ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XW27dmzh0GDBjFgwABWrVrV4LlWrFjBmjVr6txHTEwMcXFxvP/++02Otz6WSvgqBdrZ7dzTq7uv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\nUkopi9OETymllFLK4v4/Ksl9yuJH3lMAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.close()\n", + "fig = plt.figure(figsize=(10, 10))\n", + "ax = fig.add_axes((0.1, 0.1, 0.8, 0.8))\n", + "for (name, style) in zip(attens.dtype.names, ['g-', 'b-', 'r-', 'c-']):\n", + " ax.plot(\n", + " distances[5:],\n", + " -MCL_610.to_value(cnv.dB) + attens[name][5:],\n", + " style, label=name\n", + " )\n", + "\n", + "ax.axhline(0., c='0.3', ls='--', lw=2, label='CISPR')\n", + "ax.axhline(-10., c='0.5', ls='--', lw=2, label='CISPR $-$ 10 dB')\n", + "ax.axhline(-20., c='0.7', ls='--', lw=2, label='CISPR $-$ 20 dB')\n", + "# ax.axvline(P_lim, c='r', lw=2, ls='--', label='RA.769')\n", + "ax.legend(*ax.get_legend_handles_labels(), loc='lower right')\n", + "ax.set_xlim((0, 199))\n", + "ax.set_ylim((-69, 19))\n", + "ax.set_xlabel('Distance [km]')\n", + "ax.set_ylabel('Margin [dB]')\n", + "ax.text(\n", + " 0.05, 0.1, 'Frequency: {:.0f}'.format(frequency), \n", + " va='top', ha='left', transform=ax.transAxes\n", + " )\n", + "ax.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "So, if the CISPR-11 levels were used to the maximum, the necessary protection zone would need to have a radius of about 150 km!" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}