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Diffstat (limited to 'notebooks/rocof_test_data.py')
| -rw-r--r-- | notebooks/rocof_test_data.py | 174 |
1 files changed, 174 insertions, 0 deletions
diff --git a/notebooks/rocof_test_data.py b/notebooks/rocof_test_data.py new file mode 100644 index 0000000..ccb19a0 --- /dev/null +++ b/notebooks/rocof_test_data.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # ROCOF test waveform library +# +# This is a re-implementation of the ROCOF test waveforms described in https://zenodo.org/record/3559798 +# +# **This file is exported as a python module and loaded from other notebooks here, so please make sure to re-export when changing it.** + +# In[ ]: + + +import math +import itertools + +import numpy as np +from scipy import signal +from matplotlib import pyplot as plt + + +# In[ ]: + + +def sample_waveform(generator, duration:"s"=10, sampling_rate:"sp/s"=10000, frequency:"Hz"=50): + samples = int(duration*sampling_rate) + phases = np.linspace(0, 2*np.pi, 6, endpoint=False) + omega_t = np.linspace(phases, phases + 2*np.pi*duration*frequency, samples) + fundamental = np.sin(omega_t) + return generator(omega_t, fundamental, sampling_rate=sampling_rate, duration=duration, frequency=frequency).swapaxes(0, 1) + + +# In[ ]: + + +def gen_harmonics(amplitudes, phases=[]): + return lambda omega_t, fundamental, **_: fundamental + np.sum([ + a*np.sin((p if p else 0) + i*omega_t) + for i, (a, p) in enumerate(itertools.zip_longest(amplitudes, phases), start=2) + ], axis=0) + +def test_harmonics(): + 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]) + + +# In[ ]: + + +def gen_interharmonic(amplitudes, ih=[], ih_phase=[]): + def gen(omega_t, fundamental, **_): + return fundamental + np.sum([ + a*np.sin(omega_t * ih + (p if p else 0)) + for a, ih, p in itertools.zip_longest(amplitudes, ih, ih_phase) + ], axis=0) + return gen + +def test_interharmonics(): + return gen_interharmonic([0.1], [15.01401], [np.pi]) + + +# In[ ]: + + +def gen_noise(amplitude=0.2, fmax:'Hz'=4.9e3, fmin:'Hz'=100, filter_order=6): + def gen(omega_t, fundamental, sampling_rate, **_): + noise = np.random.normal(0, amplitude, fundamental.shape) + b, a = signal.butter(filter_order, + [fmin, min(fmax, sampling_rate//2-1)], + btype='bandpass', + fs=sampling_rate) + return fundamental + signal.lfilter(b, a, noise, axis=0) + return gen + +def test_noise(): + return gen_noise() + +def test_noise_loud(): + return gen_noise(amplitude=0.5, fmin=10) + + +# In[406]: + + +def gen_steps(size_amplitude=0.1, size_phase=0.1*np.pi, steps_per_sec=1): + def gen(omega_t, fundamental, duration, **_): + n = int(steps_per_sec * duration) + indices = np.random.randint(0, len(omega_t), n) + amplitudes = np.random.normal(1, size_amplitude, (n, 6)) + phases = np.random.normal(0, size_phase, (n, 6)) + amplitude = np.ones(omega_t.shape) + for start, end, a, p in zip(indices, indices[1:], amplitudes, phases): + omega_t[start:end] += p + amplitude[start:end] = a + return amplitude*np.sin(omega_t) + return gen + +def test_amplitude_steps(): + return gen_steps(size_amplitude=0.4, size_phase=0) + +def test_phase_steps(): + return gen_steps(size_amplitude=0, size_phase=0.1) + +def test_amplitude_and_phase_steps(): + return gen_steps(size_amplitude=0.2, size_phase=0.07) + + +# In[418]: + + +def step_gen(shape, stdev, duration, steps_per_sec=1.0, mean=0.0): + samples, channels = shape + n = int(steps_per_sec * duration) + indices = np.random.randint(0, samples, n) + phases = np.random.normal(mean, stdev, (n, 6)) + amplitude = np.ones((samples, channels)) + out = np.zeros(shape) + for start, end, a in zip(indices, indices[1:], amplitude): + out[start:end] = a + return out + +def gen_chirp(fmin, fmax, period, dwell_time=1.0, amplitude=None, phase_steps=None): + def gen(omega_t, fundamental, sampling_rate, duration, **_): + samples = int(duration*sampling_rate) + phases = np.linspace(0, 2*np.pi, 6, endpoint=False) + + c = (fmax-fmin)/period + t = np.linspace(0, duration, samples) + + x = np.repeat(np.reshape(2*np.pi*fmin*t, (-1,1)), 6, axis=1) + data = (phases + x)[:int(sampling_rate*dwell_time)] + current_phase = 2*np.pi*fmin*dwell_time + direction = 'up' + + for idx in range(int(dwell_time*sampling_rate), samples, int(2*period*sampling_rate)): + t1 = np.linspace(0, period, int(period*sampling_rate)) + t2 = np.linspace(0, period, int(period*sampling_rate)) + chirp_phase = np.hstack(( + 2*np.pi*(c/2 * t1**2 + fmin * t1), + 2*np.pi*(-c/2 * t2**2 + fmax * t2 - (c/2 * period**2 + fmin * period)) + )) + chirp_phase = np.repeat(np.reshape(chirp_phase, (-1, 1)), 6, axis=1) + new = phases + chirp_phase + current_phase + current_phase = chirp_phase[-1] + data = np.vstack((data, new)) + + data = data[:len(fundamental)] + + if phase_steps: + (step_amplitude, steps_per_sec) = phase_steps + steps = step_gen(data.shape, step_amplitude, duration, steps_per_sec) + data += steps + + if amplitude is None: + return np.sin(data) + else: + return fundamental + amplitude*np.sin(data) + return gen + +def test_close_interharmonics_and_flicker(): + return gen_chirp(90.0, 150.0, 10, 1, amplitude=0.1) + +def test_off_frequency(): +# return gen_chirp(48.0, 52.0, 0.25, 1) + return gen_chirp(48.0, 52.0, 10, 1) + +def test_sweep_phase_steps(): + return gen_chirp(48.0, 52.0, 10, 1, phase_steps=(0.1, 1)) +# return gen_chirp(48.0, 52.0, 0.25, 1, phase_steps=(0.1, 1)) + + +# In[ ]: + + +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] + |