notebooks/ma: Fix some major sampling rate mistake
This commit is contained in:
jaseg
parent
a6f8d8be2d
commit
3dd5789800
18 changed files with 263 additions and 257 deletions
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"(129, 470)"
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"3.90625"
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"version_major": 2,
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" 492.1875 , 496.09375, 500. ])"
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"execution_count": 21,
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"execution_count": 10,
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"execution_count": 37,
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"execution_count": 11,
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"metadata": {},
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"output_type": "display_data"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"slice(12, 15, None)\n",
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"slice(12, 15, None)\n",
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"slice(12, 15, None)\n",
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"slice(12, 15, None)\n"
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]
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
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" warnings.warn('Covariance of the parameters could not be estimated',\n"
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]
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"slice(12, 15, None)\n",
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"slice(12, 15, None)\n",
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"slice(12, 15, None)\n",
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"slice(12, 15, None)\n",
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"slice(12, 15, None)\n",
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"slice(12, 15, None)\n"
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]
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}
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],
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"source": [
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BIN
lab-windows/fig_out/dsss_thf_amplitude_56_fw_impl.pdf
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lab-windows/fig_out/dsss_thf_sensitivity_56_fw_impl.pdf
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"execution_count": 6,
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<ipython-input-33-4419e570bd12>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
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" fig, (top, bottom) = plt.subplots(2, figsize=(9,6))\n"
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"data": {
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Window length: 405 sp, zero-padded to 405 sp\n"
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"Window length: 202 sp, zero-padded to 202 sp\n"
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]
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}
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],
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"fs = sampling_rate # Hz\n",
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"ff = 50 # Hz\n",
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"\n",
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"analysis_periods = 20\n",
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"analysis_periods = 10\n",
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"window_len = fs * analysis_periods/ff\n",
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"nfft_factor = 1\n",
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"sigma = window_len/8 # samples\n",
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"execution_count": 15,
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<ipython-input-64-467ca72791b1>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
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" fig, ax = plt.subplots(figsize=(9, 3))\n"
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"cell_type": "code",
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"execution_count": 45,
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"execution_count": 16,
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"execution_count": 56,
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"execution_count": 17,
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<ipython-input-56-4e12d8913585>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
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" fig, ax = plt.subplots(figsize=(9, 5), sharex=True)\n"
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"execution_count": 57,
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<ipython-input-57-8b77e38496af>:9: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
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" fig, (ax2, ax1) = plt.subplots(2, figsize=(9,7))\n"
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]
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"data": {
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"version_major": 2,
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"version_minor": 0
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-57-8b77e38496af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;31m# Cut out first 10min of filtered data to give filters time to settle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0mrms_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_t\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 59\u001b[0m \u001b[0mrms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrms_slice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'RMS (band-pass): {rms*1e3:.3f}mHz'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransAxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'white'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m<ipython-input-18-8b77e38496af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;31m# Cut out first 10min of filtered data to give filters time to settle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0mrms_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_t\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 59\u001b[0m \u001b[0mrms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrms_slice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'RMS (band-pass): {rms*1e3:.3f}mHz'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransAxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'white'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
|
||||
]
|
||||
}
|
||||
|
|
@ -598,7 +566,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
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"execution_count": 69,
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||||
"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -617,7 +585,7 @@
|
|||
},
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{
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||||
"cell_type": "code",
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"execution_count": 73,
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||||
"execution_count": null,
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"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -643,39 +611,9 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 77,
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"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-77-2f4bcf6b2d33>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, ax = plt.subplots(figsize=(6, 3))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "8b689c4f96fa40ffb5012764afb57564",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
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"text/plain": [
|
||||
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
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"text": [
|
||||
"The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
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"fig, ax = plt.subplots(figsize=(6, 3))\n",
|
||||
"fig.tight_layout()\n",
|
||||
|
|
@ -699,32 +637,9 @@
|
|||
},
|
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{
|
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"cell_type": "code",
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"execution_count": 84,
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"execution_count": null,
|
||||
"metadata": {},
|
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"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
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"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-84-936ca777d145>:26: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, ax = plt.subplots()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "b58b8858dea1485fae236c9fbb6954d5",
|
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"version_major": 2,
|
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"version_minor": 0
|
||||
},
|
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"text/plain": [
|
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Number of samplepoints\n",
|
||||
"newcopy = np.copy(f_mean[1:-2])\n",
|
||||
|
|
@ -768,24 +683,9 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
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"execution_count": 18,
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||||
"execution_count": null,
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"metadata": {},
|
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"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "671ae919bf124e72b54144310ea1602d",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"ax.plot(np.linspace(0, (len(f_mean)-3)/10, len(f_mean)-3) , f_mean[1:-2])\n",
|
||||
|
|
|
|||
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|
|
@ -579,10 +579,10 @@ P}{\Delta f}$, called \emph{Overall Network Power Frequency Characteristic} by E
|
|||
\SI{25}{\giga\watt\per\hertz}.
|
||||
|
||||
We can derive general design parameter for any system utilizing grid frequency as a communications channel from the
|
||||
policies of ENTSO-E\cite{entsoe02,entsoe03}. % FIXME introduce ENTSO-E on first use
|
||||
Probably any such system should stay below a modulation amplitude of \SI{100}{\milli\hertz} which is the threshold
|
||||
defined in the ENTSO-E incidents classification scale for a Scale 0-1 (from "Anomaly" to "Noteworthy Incident" scale)
|
||||
frequency degradation incident\cite{entsoe03} in the continental europe synchronous area.
|
||||
policies of ENTSO-E\cite{entsoe02,entsoe03}. Probably any such system should stay below a modulation amplitude of
|
||||
\SI{100}{\milli\hertz} which is the threshold defined in the ENTSO-E incidents classification scale for a Scale 0-1
|
||||
(from "Anomaly" to "Noteworthy Incident" scale) frequency degradation incident\cite{entsoe03} in the continental europe
|
||||
synchronous area.
|
||||
|
||||
\subsubsection{Control systems coupled to grid frequency}
|
||||
|
||||
|
|
@ -677,10 +677,17 @@ spectrum of any grid frequency modulation system should not exhibit any notable
|
|||
of spectral energy in certain frequency ranges.
|
||||
|
||||
\subsubsection{Overall system parameters}
|
||||
|
||||
% FIXME
|
||||
|
||||
\subsubsection{An outline of practical implementation}
|
||||
% FIXME
|
||||
|
||||
\section{From grid frequency to a reliable communications channel}
|
||||
% FIXME
|
||||
|
||||
\subsection{Channel properties}
|
||||
% FIXME
|
||||
|
||||
|
||||
\subsection{Modulation and its parameters}
|
||||
|
|
@ -905,6 +912,8 @@ realistically be up to $\mathcal O\left(10^3\right)$, which is easily enough for
|
|||
\chapter{Practical implementation}
|
||||
\section{Cryptographic validation}
|
||||
|
||||
%FIXME
|
||||
|
||||
\section{Data collection for channel validation}
|
||||
|
||||
To design a solid system we needed to parametrize mains frequency variations under normal conditions. To set modulation
|
||||
|
|
@ -1141,8 +1150,6 @@ acknowledgement packet to the sensor. When the sensor receives the acknowledgeme
|
|||
from the transmission packet ringbuffer. When the host detects an incorrect checksum it simply stays quiet and waits for
|
||||
the sensor to resume with retransmission when the next ADC buffer has been received.
|
||||
|
||||
% FIXME make actual error rate measurements
|
||||
|
||||
The serial interface logic presents most of the complexity of the sensor firmware. This complexity is necessary since
|
||||
we need reliable, error-checked transmission to the host. Though rare, bit errors on a serial interface do happen and
|
||||
data corruption is unacceptable. The packet-layer queueing on the sensor is necessary since the host is not a realtime
|
||||
|
|
@ -1150,7 +1157,8 @@ system and unpredictable latency spikes of several hundred milliseconds are poss
|
|||
|
||||
The host in our recording setup is a Raspberry Pi 3 model B running a Python script. The Python script handles serial
|
||||
communication and logs data and errors into an SQLite database file. SQLite has been chosen for its simple yet flexible
|
||||
interface and its good tolerance of system resets due to unexpected power loss.
|
||||
interface and its good tolerance of system resets due to unexpected power loss. Overall our setup performed adequately
|
||||
with IO contention on the raspberry PI/linux side causing only 16 skipped sample packets over a 68-hour recording span.
|
||||
|
||||
\subsection{Frequency sensor measurement results}
|
||||
|
||||
|
|
@ -1208,7 +1216,7 @@ Captured raw waveform data is processed in the Jupyter Lab environment\cite{kluy
|
|||
extracted as described in sec. \ref{frequency_estimation} using the \textcite{gasior01} technique. Appendix
|
||||
\ref{grid_freq_estimation_notebook} contains the Jupyter notebook we used for frequency measurement.
|
||||
|
||||
% FIXME comparison against reference measurements?
|
||||
% TODO comparison against reference measurements?
|
||||
|
||||
\section{Channel simulation and parameter validation}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue