基于改进CEEMD和RF的低压串联故障电弧识别方法

了解决完整集合经验模态分解(complete ensemble empirical mode decomposition, CEEMD)得到的固有模态函数分量数目及其频段不固定，以及故障电弧特征难以准确提取导致故障识别准确率低的不足，引入T检验和方差贡献率形成了一种改进CEEMD方法，进一步提出一种基于改进CEEMD和随机森林(random forest, RF)的串联故障电弧识别方法。首先，依托串联电弧故障试验平台，采集不同负载的电流信号。然后，采用改进CEEMD对信号进行分析并提取故障特征量，以TreeBagger函数进行特征降维，形成特征向量样本集。最后，结合RF构建故障电弧诊断模型，对样本集进行分类识别。结果表明：改进CEEMD能有效地提取不同负载电流的故障特征，所提故障电弧识别方法的识别准确率达到97.50%。通过进行不同特征提取方法和不同分类模型对诊断结果影响的消融实验，进一步证明了所提方法的可行性。

Identification method of low voltage series fault arc based on improved CEEMD decomposition and RF

In order to solve the deficiencies that the number of intrinsic mode function component and its frequency band obtained by the decomposition of the complete ensemble empirical mode decomposition (CEEMD) are not fixed, which makes it difficult to accurately extract fault arc characteristics thus leading to low accuracy of fault identification, the T-test and variance contribution rate are introduced to form an improved CEEMD method, and a series fault arc identification method based on improved CEEMD and random forest (RF) is proposed. First, the current signals under different loads are collected by the series arc fault test platform. Second, the improved CEEMD is used to analyze the signal and extract the fault feature quantity. Then, the TreeBagger function is used to reduce the feature dimension to form the feature vector sample set. Finally, a fault arc diagnosis model is constructed combined with RF to classify and identify the sample set. Experimental results show that the improved CEEMD can effectively extract fault features of different load currents, and the identification accuracy of the proposed fault arc recognition method reaches 97.50%. Ablation experiments of the influence of different feature extraction methods and different classification models on the diagnostic results is carried out, the feasibility of the proposed method is further proved.