基于欧氏距离最佳K均值聚类的超级电容组故障在线鉴别方法  

Online fault identification method for supercapacitor group of optimal K-means cluster based on Euclidean distance

作　　者：于鹏[1,2] 杨仁刚[1] 

机构地区：[1]中国农业大学信息与电气工程学院,北京100083 [2]渤海大学工学院,锦州121013

出　　处：《农业工程学报》2016年第2期186-192,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家863高技术基金项目(2012AA050217)

摘　　要：为了提高超级电容组运行可靠性需要对故障电容进行在线鉴别,针对现有超级电容故障鉴别方法参数识别难度高和采集数据量大的问题,该文采用最佳K均值聚类方法在线检测故障超级电容器,并提出了最佳聚类的欧氏距离指标。该方法首先对在线电压信号数据进行预处理,采用奇异值分解提取特征值进行K-Means动态聚类并计算相应的欧氏距离指标,由最佳聚类结果鉴别出故障单体。针对该文提出方法设计了超级电容组充放电仿真试验进行验证。试验结果表明基于欧氏距离指标最佳K均值动态聚类的超级电容组故障在线鉴别方法可以根据串联单体电压信号进行故障检测。该文可为超级电容在线故障检测系统的开发与研制提供参考。In order to keep the energy storage system which is based on supercapacitor group with series connection work reliably, the fault groups of supercapacitors are necessary to be identified. A fault state identification method of K-means cluster was presented in this paper. A Euclidean distance index was proposed to choose K value automatically. In this method, the voltage signal data are preprocessed to form the sample array. The singular value decomposition is applied to project out a shadow subset of the sample array. The K-means method is used to cluster the shadow subset for fault state identification. The fault subsets are detected in the cluster result. The largest cluster is identified as normal state and the others are abnormal state. The Euclidean distance index was proposed to decide the optimal K value automatically after enumeration of all possible K. This index is based on the Euclidean distance of pairwise data points and pairwise cluster centers. The minimize value of index is bonded to the optimal K value. Adjustable coefficients are used to improve the adaptability of this index. Based on the principle of K-means cluster method and Euclidean distance index, the fault state identification process was introduced. In this process, after sampling the voltage of supercapacitor cells, the difference voltage array is established to form the feature space. The singular value decomposition is used on the difference voltage array to form the sample subset. The variance of sample subset is compared to set limitation. If the variance overrides the limitation, K-means algorithm will be used to cluster the sample subset, and the Euclidean distance index will be used to decide the optimal K value. By counting the group amount of sample subset, the fault state capacitors can be distinguished. An experiment system was designed to verify the efficiency and validity of the method and index. The experiment environment was MATLAB-Simulink. Two experiments were carried out based on the experiment system. The first exper

关 键 词：故障检测 信号分析 模型 超级电容 动态聚类 有效性指标 

分 类 号：TM53[电气工程—电器]