基于改进CEEMDAN分解与时空特征的低压供电线路 串联故障电弧检测

针对低压线路中的串联故障电弧检测难题，提出基于改进CEEMDAN分解与时空特征的串联故障电弧检测方法。首先，采用CEEMDAN算法实现电流信号的完备分解，并以各IMF分量的峭度指标、裕度指标、能量特征和能量熵特征为判定依据，实现高频段信号粗选。然后，提出空间尺度和时间尺度相融合的特征构建方法，捕获各粗选高频IMF分量的局部特征，增强电流特征对比度和判别力。最后，采用子空间变换算法实现电流时空特征集合的二次降维，并基于SVM实现串联故障电弧检测。实际试验证明，所提算法的平均故障电弧检测准确率达88.33%，能够实现高效的串联故障电弧检测。

series fault arc detection; CEEMDAN decomposition; rough selection of frequency band; spatial-temporal features; SVM

There is a problem of series arc fault detection in low voltage lines. Thus a series arc fault detection method based on improved CEEMDAN decomposition and spatial-temporal features is proposed. First, the CEEMDAN algorithm is used to complete the decomposition of the current signal, and the rough selection of the high-frequency signal is realized based on the kurtosis index, margin index, energy feature and energy entropy feature of each IMF component. Then, a feature construction method combining spatial and temporal scales is proposed to capture the local feature of each high-frequency IMF component. This enhances the contrast and discriminants of the current feature. Finally, some subspace transformation algorithms are used to implement the second dimension reduction of the current spatial-temporal feature set, and the series fault arc detection is realized based on SVM. The actual test shows that the average fault arc detection accuracy of the proposed algorithm is 88.33%, which is efficient for series fault arc detection. This work is supported by the National Natural Science Foundation of China (No. 52077221).