基于混沌关联维特征的电能表计量多维数据聚类方法

Multi-dimensional data clustering method for electric energy meter measurement based on chaotic correlation dimension characteristics

中文关键词: 电能计量  多维数据  混沌特征提取  关联维  聚类算法

英文关键词: electric energy measurement  multidimensional data  chaotic feature extraction  correlation dimension  clustering algorithm

中文摘要:

      针对传统电能计量多维数据的聚类分析算法存在的计算过程复杂、效率低下、系统耗能高等问题，设计出一套适用的解决方案。该方案基于大数据平台进行计算存储，通过混沌关联维聚类分析方法对电能计量数据进行多维分析。该方法吸收了传统大数据聚类算法和混沌特征提取的优点，通过选取交互信息量的第一个最小值作为最佳时间延迟，采用虚假最近邻点算法选择最佳的嵌入维数 u 重构相空间。然后基于相空间重构提取出混沌关联维特征，并糅合聚类算法对电能计量多维数据进行聚类。实验证明，本研究的混沌关联维聚类分析法效率高、过程简单且能耗低。

英文摘要:

      Aiming at the problems of complex calculation process, low efficiency and high energy consumption of the traditional energy metering multi-dimensional data clustering analysis algorithm, a set of suitable solutions is designed. The solution is based on a big data platform for calculation and storage, and multidimensional analysis of electric energy measurement data is carried out through the chaotic correlation dimension clustering analysis method. This method absorbs the advantages of traditional big data clustering algorithms and chaotic feature extraction, selectethe first minimum value of the interactive information as the best time delay and uses the false nearest neighbor algorithm to select the best embedding dimension u reconstruction phase space. Then,based on the phase space reconstruction, the chaotic correlation dimension characteristics are extracted and the clustering algorithmis combined to cluster the multi-dimensional data of electric energy measurement. Experiments prove that the chaotic correlation dimension clustering analysis method in this study is highly efficient,simple in process and low in energy consumption.