python实现故障电弧的分析算法、以及故障电弧的串扰分析的详细算法实现

以下是Python实现故障电弧分析算法及串扰分析的详细实现，包含核心函数分解和代码注释说明。所有代码均基于标准库和科学计算工具链实现。

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数据预处理模块

import numpy as np
from scipy.signal import butter, filtfilt

def load_arc_data(file_path):
    """加载原始电弧数据CSV文件"""
    return np.genfromtxt(file_path, delimiter=',', skip_header=1)

def apply_bandpass(data, lowcut=1000, highcut=10000, fs=25000, order=4):
    """巴特沃斯带通滤波"""
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    b, a = butter(order, [low, high], btype='band')
    return filtfilt(b, a, data)

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特征提取模块

def calculate_rms(signal):
    """计算信号有效值RMS"""
    return np.sqrt(np.mean(np.square(signal)))

def extract_wavelet_features(signal, wavelet='db4', level=5):
    """小波包分解能量特征"""
    import pywt
    coeffs = pywt.wavedec(signal, wavelet, level=level)
    return [np.sum(np.square(c)) for c in coeffs]

def find_peak_frequency(signal, fs):
    """FFT频谱峰值检测"""
    n = len(signal)
    fft_vals = np.fft.rfft(signal)
    freqs = np.fft.rfftfreq(n, 1/fs)
    peak_idx = np.argmax(np.abs(fft_vals))
    return freqs[peak_idx]

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故障电弧检测算法

from sklearn.ensemble import IsolationForest

def train_arc_detector(X_train):
    """基于孤立森林的异常检测"""
    clf = IsolationForest(contamination=0.1, random_state=42)
    clf.fit(X_train)
    return clf

def detect_arc(signal, clf, threshold=0.65):
    """实时检测函数"""
    features = [
        calculate_rms(signal),
        find_peak_frequency(signal, fs=25000),
        *extract_wavelet_features(signal)
    ]
    score = clf.decision_function([features])
    return score < threshold

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串扰分析模块

def cross_correlation(signal1, signal2):
    """时域互相关分析"""
    return np.correlate(signal1, signal2, mode='full')

def frequency_coupling_analysis(signal1, signal2, fs):
    """频域耦合分析"""
    fft1 = np.fft.fft(signal1)
    fft2 = np.fft.fft(signal2)
    return np.abs(fft1 * np.conj(fft2)) / (len(signal1)*fs)

def crosstalk_coefficient(signal1, signal2):
    """串扰系数计算"""
    energy1 = np.sum(np.square(signal1))
    energy2 = np.sum(np.square(signal2))
    return np.sum(signal1 * signal2) / np.sqrt(energy1 * energy2)

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完整处理流程示例

# 示例使用流程
raw_data = load_arc_data("arc_samples.csv")
filtered = apply_bandpass(raw_data[:, 0])

# 特征提取
features = [
    calculate_rms(filtered),
    find_peak_frequency(filtered, 25000),
    *extract_wavelet_features(filtered)
]

# 串扰分析示例
signal1 = raw_data[:, 0]
signal2 = raw_data[:, 1]
print(f"Crosstalk coefficient: {crosstalk_coefficient(signal1, signal2):.4f}")

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关键算法说明

1. 小波包分解：使用Daubechies-4小波进行5层分解，获取不同频带的能量分布
2. 孤立森林检测：通过无监督学习识别异常放电模式
3. 频域耦合分析：计算互功率谱密度，识别通道间能量传递特性
4. 实时检测优化：所有函数设计支持流式处理，适合嵌入式部署

实际工程应用中需考虑采样率校准、电磁兼容性补偿等硬件相关参数调整。建议配合硬件测试平台验证算法有效性。