宿 磊,沈 煜,杨 帆,徐丙垠,薛永端,王 玮,邹国锋.融合 CEEMDAN 分解与敏感 IMF 精选的 串联电弧故障检测[J].电子测量与仪器学报,2022,36(10):173-180
融合 CEEMDAN 分解与敏感 IMF 精选的 串联电弧故障检测
Series arc fault detection combining CEEMDANdecomposition and sensitive IMF selection
  
DOI:
中文关键词:  电弧检测  CEEMDAN 分解  频段划分  敏感 IMF 选取  时间窗特征计算  支持向量机
英文关键词:arc detection  CEEMDAN decomposition  frequency division  sensitive IMF selection  time window feature calculation  SVM
基金项目:国家自然科学基金项目(52077221)、国网湖北省电力有限公司科技项目(52153220001V)资助
作者单位
宿 磊 1. 国网湖北省电力有限公司电力科学研究院 
沈 煜 1. 国网湖北省电力有限公司电力科学研究院 
杨 帆 1. 国网湖北省电力有限公司电力科学研究院 
徐丙垠 2. 山东理工大学电气与电子工程学院,3. 山东科汇电力自动化股份有限公司 
薛永端 4. 中国石油大学(华东)新能源学院 
王 玮 2. 山东理工大学电气与电子工程学院 
邹国锋 2. 山东理工大学电气与电子工程学院 
AuthorInstitution
Su Lei 1. Electric Power Research Institute of State Grid Hubei Electric Power Co. , Ltd. 
Shen Yu 1. Electric Power Research Institute of State Grid Hubei Electric Power Co. , Ltd. 
Yang Fan 1. Electric Power Research Institute of State Grid Hubei Electric Power Co. , Ltd. 
Xu Bingyin 2. School of Electrical and Electronic Engineering, Shandong University of Technology,3. Shandong Kehui Power Automation Co. , Ltd. 
Xue Yongduan 4. College of New Energy, China University of Petroleum (East China) 
Wang Wei 2. School of Electrical and Electronic Engineering, Shandong University of Technology 
Zou Guofeng 2. School of Electrical and Electronic Engineering, Shandong University of Technology 
摘要点击次数: 647
全文下载次数: 774
中文摘要:
      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。
英文摘要:
      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.
查看全文  查看/发表评论  下载PDF阅读器