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Identification of transient power quality disturbances based on FCM

机译:基于FCM的暂态电能质量扰动识别

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This paper presents a new hierarchical identification method for transient power quality disturbance based on fuzzy C-mean clustering (FCM) algorithm. The method consists of five function modules. By use of the ensemble empirical mode decomposition (EEMD) and singular value decomposition method, we can extract effective feature vectors layer by layer, which are used as the input of FCM. In this way the optimized classified matrix and clustering centers are obtained. According to the Euclidean distance between the unknown-sample records and the known-sample ones, the disturbance type is identified. Simulation results of common kinds of transient disturbances indicate that this method is accurate and has a potential application in engineering.
机译:提出了一种基于模糊C均值聚类(FCM)算法的暂态电能质量扰动分级识别方法。该方法包括五个功能模块。通过使用集成经验模式分解(EEMD)和奇异值分解方法,我们可以逐层提取有效特征向量,将其用作FCM的输入。这样,获得了优化的分类矩阵和聚类中心。根据未知样本记录与已知样本记录之间的欧几里得距离,确定干扰类型。常见瞬态干扰的仿真结果表明,该方法是准确的,在工程上具有潜在的应用前景。

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