基于FCM的暂态电能质量扰动识别  

Identification of transient power quality disturbances based on FCM

作　　者：韩玉环[1] 赵庆生[1] 郭贺宏 王振起 张学军[3] 

机构地区：[1]太原理工大学电力系统运行与控制山西省重点实验室,山西太原030024 [2]国网晋中供电公司,山西晋中030600 [3]山西大学,山西太原030006

出　　处：《电力系统保护与控制》2016年第9期62-68,共7页Power System Protection and Control

基　　金：山西省自然科学基金项目(2015011057);国家自然科学青年基金项目(51505317)~~

摘　　要：提出一种应用模糊C均值聚类(FCM)对暂态电能质量扰动进行识别的新方法。该识别方法分层实现,第一层判断信号中是否包含暂态振荡扰动,第二层判断是否包含暂态脉冲扰动,第三层判断是否包含幅值扰动及综合判断出各种复合扰动的类型。通过与集合经验模态分解(EEMD)和奇异值分解方法的结合,分层提取出有效特征量,并将其作为FCM的输入,得到聚类中心和隶属度矩阵。最后通过计算待测样本与已知样本的聚类中心的欧氏距离实现扰动类型识别。通过仿真分析,该分层识别方法准确可行。A new method to identify the transient power quality disturbance based on FCM is proposed. This recognition method is implemented hierarchically. The first layer can judge whether transient oscillation disturbance is included in the signal. The second layer judges whether transient oscillation pulse is contained in the signal. The third layer judges whether the signal contains the magnitude of the disturbance and has a comprehensive judgment on the specific type of complex disturbances. Through combination with the ensemble empirical mode decomposition（EEMD） and singular value decomposition method, effective feature vectors can be extracted hierarchically, which is used as the input of FCM. In this way, the optimized classified matrix and clustering centers are obtained. Calculating the Euclidean distance between the unknown-sample samples and the known-sample ones, the disturbance type is identified. The simulation result indicates that this method is accurate and feasible.

关 键 词：模糊C均值聚类算法 暂态识别 集合经验模态分解 奇异值分解 分层识别 

分 类 号：TM711[电气工程—电力系统及自动化]