融合CEEMDAN分解与敏感IMF精选的串联电弧故障检测

针对串联电弧故障检测困难，以及基于分解策略的检测方法难以捕获敏感判别分量的问题，提出一种融合自适应噪声的完备经验模态分解(CEEMDAN)和敏感本征模态函数(IMF)精选的串联电弧故障检测方法。本方法采用CEEMDAN算法对故障电弧电流进行完备分解；并定义了电弧电流的12个特征指标，以敏感性较强的峭度指标和能量特征作为判定依据，从而实现了IMF分量的频段划分；在此基础上，提出了基于时间窗的特征计算方法，通过获取各高频IMF分量的时间维度局部特征，并通过比较方差、均方根值等特征指标实现敏感IMF分量的准确选取。最后，针对电流特征集，采用主成分分析实现二次降维，并基于支持向量机(SVM)实现串联电弧故障检测。实验证明了所提方法的可行性和电弧故障检测的有效性。

Series arc fault detection combining CEEMDAN decomposition and sensitive IMF selection

Aiming at the difficulty of series arc fault detection and the difficulty of detection method based on decomposition strategy to capture sensitive discriminant components, a series fault arc detection method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition and sensitive intrinsic mode function( IMF) selection was proposed. In this paper, the CEEMDAN algorithm was first applied to complete decomposition of arc current in series faults. Then, 12 feature indicators of arc current were defined, and the frequency band division of IMF component was realized according to the kurtosis index and energy feature which were more sensitive. On this basis, a feature calculation method based on time window was proposed to obtain the local features of the time scale of each high-frequency IMF component. Accurate selection of sensitive IMF components was realized by comparing feature indexes such as variance and root mean square value. Finally, for the current feature set, the second dimension reduction was realized by principal component analysis, and the series fault arc detection was implemented based on SVM. The feasibility of the proposed method and the validity of fault arc detection were proved by practical experiments.