理解有源谐波滤波器（AHF）的运行
这段 Python 代码演示了 有源谐波滤波器 （AHF）的工作原理。AHF 用于治理电气系统中的谐波畸变。通过分析电流波形与频谱，代码展示 AHF 降低谐波含量、改善电能质量的能力。
代码概述：
- 代码从文本文件读取谐波参数，并生成谐波波形。
- 计算总谐波畸变（THD），并绘制电流波形、频谱与 AHF 输出。
- 图表展示 AHF 降低谐波畸变的效果，使电流波形更平滑，并改善系统性能。
AHF 的优势：
- 降低电气系统谐波畸变。
- 改善电能质量与系统可靠性。
- 提高敏感设备效率。
有源谐波滤波器（AHF）Python 代码：
import numpy as np
import matplotlib.pyplot as plt
# Read harmonic parameters from a text file
def read_harmonic_parameters(file_path):
try:
with open(file_path, 'r') as file:
lines = file.readlines()
harmonic_parameters = [list(map(float, line.strip().split())) for line in lines]
return harmonic_parameters
except Exception as e:
print(f"Error reading harmonic parameters: {e}")
return None
# Read harmonic parameters
file_path = 'harmonic_parameters.txt'
harmonic_parameters = read_harmonic_parameters(file_path)
if harmonic_parameters:
# Parse harmonic parameters
harmonic_multipliers, harmonic_amplitudes = zip(*harmonic_parameters)
# Initial current parameters
fundamental_frequency = 50
display_time = 0.05
sampling_frequency = 10000
sampling_points = int(sampling_frequency * display_time)
time = np.linspace(0, display_time, sampling_points, endpoint=False)
fundamental_amplitude = 100
# Convert harmonic multipliers to actual frequency values
harmonic_frequencies = np.array(harmonic_multipliers) * fundamental_frequency
# Generate current waveforms for fundamental and harmonics
fundamental_waveform = fundamental_amplitude * np.sin(2 * np.pi * fundamental_frequency * time)
harmonic_waveforms = [amp * np.sin(2 * np.pi * freq * time) for freq, amp in zip(harmonic_frequencies, harmonic_amplitudes)]
# Total current waveform is the sum of fundamental and harmonics
total_current_waveform = fundamental_waveform + np.sum(harmonic_waveforms, axis=0)
# Calculate THD
fundamental_rms = np.sqrt(np.mean(fundamental_waveform**2))
harmonic_rms_values = [np.sqrt(np.mean(harmonic_waveform**2)) for harmonic_waveform in harmonic_waveforms]
thd_percent = (np.sqrt(np.sum(np.array(harmonic_rms_values)**2)) / fundamental_rms) * 100
# Calculate the sum of harmonic currents excluding the fundamental
harmonic_current_sum = np.sum(harmonic_waveforms, axis=0)
# Calculate the negative of harmonic current sum
negative_harmonic_current_sum = -harmonic_current_sum
# Plot current waveforms and annotate THDi value
plt.subplot(4, 1, 1)
plt.plot(time, total_current_waveform, label='Total Current')
# plt.plot(time, fundamental_waveform, label='Fundamental')
plt.title(f'Current Waveforms (THD={thd_percent:.2f}%)')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.legend()
plt.grid(True)
# Plot fundamental and harmonic currents in the second subplot
plt.subplot(4, 1, 2)
plt.plot(time, fundamental_waveform, label='Fundamental')
plt.plot(time, harmonic_current_sum, label='Harmonic Current Sum (Excluding Fundamental)')
plt.title('Fundamental and Harmonic Currents')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.legend()
plt.grid(True)
# Plot a bar chart of amplitudes within the frequency range
plt.subplot(4, 1, 3)
plt.bar(harmonic_frequencies, harmonic_amplitudes, width=8, align='center', label='Harmonics')
plt.bar(fundamental_frequency, fundamental_amplitude, width=8, align='center', color='red', label='Fundamental')
plt.title('Frequency Spectrum')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude (A)')
plt.legend()
plt.grid(True)
# Plot the negative of harmonic current sum
plt.subplot(4, 1, 4)
plt.plot(time, negative_harmonic_current_sum, label='Negative Harmonic Current Sum')
plt.title('Active Harmonic Filter Output Current')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
else:
print("Failed to read harmonic parameters from the file.")
Python 运行结果
