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Diffstat (limited to 'ROCOF test data generator.ipynb')
| -rw-r--r-- | ROCOF test data generator.ipynb | 235 |
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diff --git a/ROCOF test data generator.ipynb b/ROCOF test data generator.ipynb new file mode 100644 index 0000000..df94a7b --- /dev/null +++ b/ROCOF test data generator.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ROCOF test waveform library\n", + "\n", + "This is a re-implementation of the ROCOF test waveforms described in https://zenodo.org/record/3559798\n", + "\n", + "**This file is exported as a python module and loaded from other notebooks here, so please make sure to re-export when changing it.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import itertools\n", + "\n", + "import numpy as np\n", + "from scipy import signal\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def sample_waveform(generator, duration:\"s\"=10, sampling_rate:\"sp/s\"=10000, frequency:\"Hz\"=50):\n", + " samples = int(duration*sampling_rate)\n", + " phases = np.linspace(0, 2*np.pi, 6, endpoint=False)\n", + " omega_t = np.linspace(phases, phases + 2*np.pi*duration*frequency, samples)\n", + " fundamental = np.sin(omega_t)\n", + " return generator(omega_t, fundamental, sampling_rate=sampling_rate, duration=duration, frequency=frequency).swapaxes(0, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def gen_harmonics(amplitudes, phases=[]):\n", + " return lambda omega_t, fundamental, **_: fundamental + np.sum([\n", + " a*np.sin((p if p else 0) + i*omega_t)\n", + " for i, (a, p) in enumerate(itertools.zip_longest(amplitudes, phases), start=2)\n", + " ], axis=0)\n", + "\n", + "def test_harmonics():\n", + " return gen_harmonics([0.02, 0.05, 0.01, 0.06, 0.005, 0.05, 0.005, 0.015, 0.005, 0.035, 0.005, 0.003])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def gen_interharmonic(amplitudes, ih=[], ih_phase=[]):\n", + " def gen(omega_t, fundamental, **_):\n", + " return fundamental + np.sum([\n", + " a*np.sin(omega_t * ih + (p if p else 0))\n", + " for a, ih, p in itertools.zip_longest(amplitudes, ih, ih_phase)\n", + " ], axis=0)\n", + " return gen\n", + "\n", + "def test_interharmonics():\n", + " return gen_interharmonic([0.1], [15.01401], [np.pi])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def gen_noise(amplitude=0.2, fmax:'Hz'=4.9e3, fmin:'Hz'=100, filter_order=6):\n", + " def gen(omega_t, fundamental, sampling_rate, **_):\n", + " noise = np.random.normal(0, amplitude, fundamental.shape)\n", + " b, a = signal.butter(filter_order,\n", + " [fmin, min(fmax, sampling_rate//2-1)],\n", + " btype='bandpass',\n", + " fs=sampling_rate)\n", + " return fundamental + signal.lfilter(b, a, noise, axis=0)\n", + " return gen\n", + "\n", + "def test_noise():\n", + " return gen_noise()\n", + "\n", + "def test_noise_loud():\n", + " return gen_noise(amplitude=0.5, fmin=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 406, + "metadata": {}, + "outputs": [], + "source": [ + "def gen_steps(size_amplitude=0.1, size_phase=0.1*np.pi, steps_per_sec=1):\n", + " def gen(omega_t, fundamental, duration, **_):\n", + " n = int(steps_per_sec * duration)\n", + " indices = np.random.randint(0, len(omega_t), n)\n", + " amplitudes = np.random.normal(1, size_amplitude, (n, 6))\n", + " phases = np.random.normal(0, size_phase, (n, 6))\n", + " amplitude = np.ones(omega_t.shape)\n", + " for start, end, a, p in zip(indices, indices[1:], amplitudes, phases):\n", + " omega_t[start:end] += p\n", + " amplitude[start:end] = a\n", + " return amplitude*np.sin(omega_t)\n", + " return gen\n", + "\n", + "def test_amplitude_steps():\n", + " return gen_steps(size_amplitude=0.4, size_phase=0)\n", + "\n", + "def test_phase_steps():\n", + " return gen_steps(size_amplitude=0, size_phase=0.1)\n", + "\n", + "def test_amplitude_and_phase_steps():\n", + " return gen_steps(size_amplitude=0.2, size_phase=0.07)" + ] + }, + { + "cell_type": "code", + "execution_count": 418, + "metadata": {}, + "outputs": [], + "source": [ + "def step_gen(shape, stdev, duration, steps_per_sec=1.0, mean=0.0):\n", + " samples, channels = shape\n", + " n = int(steps_per_sec * duration)\n", + " indices = np.random.randint(0, samples, n)\n", + " phases = np.random.normal(mean, stdev, (n, 6))\n", + " amplitude = np.ones((samples, channels))\n", + " out = np.zeros(shape)\n", + " for start, end, a in zip(indices, indices[1:], amplitude):\n", + " out[start:end] = a\n", + " return out\n", + "\n", + "def gen_chirp(fmin, fmax, period, dwell_time=1.0, amplitude=None, phase_steps=None):\n", + " def gen(omega_t, fundamental, sampling_rate, duration, **_):\n", + " samples = int(duration*sampling_rate)\n", + " phases = np.linspace(0, 2*np.pi, 6, endpoint=False)\n", + " \n", + " c = (fmax-fmin)/period\n", + " t = np.linspace(0, duration, samples)\n", + " \n", + " x = np.repeat(np.reshape(2*np.pi*fmin*t, (-1,1)), 6, axis=1)\n", + " data = (phases + x)[:int(sampling_rate*dwell_time)]\n", + " current_phase = 2*np.pi*fmin*dwell_time\n", + " direction = 'up'\n", + " \n", + " for idx in range(int(dwell_time*sampling_rate), samples, int(2*period*sampling_rate)):\n", + " t1 = np.linspace(0, period, int(period*sampling_rate))\n", + " t2 = np.linspace(0, period, int(period*sampling_rate))\n", + " chirp_phase = np.hstack((\n", + " 2*np.pi*(c/2 * t1**2 + fmin * t1),\n", + " 2*np.pi*(-c/2 * t2**2 + fmax * t2 - (c/2 * period**2 + fmin * period))\n", + " ))\n", + " chirp_phase = np.repeat(np.reshape(chirp_phase, (-1, 1)), 6, axis=1)\n", + " new = phases + chirp_phase + current_phase\n", + " current_phase = chirp_phase[-1]\n", + " data = np.vstack((data, new))\n", + " \n", + " data = data[:len(fundamental)]\n", + " \n", + " if phase_steps:\n", + " (step_amplitude, steps_per_sec) = phase_steps\n", + " steps = step_gen(data.shape, step_amplitude, duration, steps_per_sec)\n", + " data += steps\n", + " \n", + " if amplitude is None:\n", + " return np.sin(data)\n", + " else:\n", + " return fundamental + amplitude*np.sin(data)\n", + " return gen\n", + "\n", + "def test_close_interharmonics_and_flicker():\n", + " return gen_chirp(90.0, 150.0, 10, 1, amplitude=0.1)\n", + "\n", + "def test_off_frequency():\n", + "# return gen_chirp(48.0, 52.0, 0.25, 1)\n", + " return gen_chirp(48.0, 52.0, 10, 1)\n", + "\n", + "def test_sweep_phase_steps():\n", + " return gen_chirp(48.0, 52.0, 10, 1, phase_steps=(0.1, 1))\n", + "# return gen_chirp(48.0, 52.0, 0.25, 1, phase_steps=(0.1, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_tests = [test_harmonics, test_interharmonics, test_noise, test_noise_loud, test_amplitude_steps, test_phase_steps, test_amplitude_and_phase_steps, test_close_interharmonics_and_flicker, test_off_frequency, test_sweep_phase_steps]" + ] + } + ], + "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": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} |