摘要
提出了基于最优参数选择的EEMD方法进行电能质量识别。通过分析EEMD方法的两个参数对电能质量信号分解的影响,选定最优参数来提取特征值。实验表明,该算法可进一步克服EEMD方法的模态混叠现象和减少不必要的计算量,结合模糊C均值算法能快速、准确地对暂态电能质量扰动进行分类。
The paper proposes a method to detect power quality disturbances based on the EEMD method of optimal parameter selection. By analyzing the influence of the two parameters of the eemd method on the decomposition of the power quality signal,it can select the optimal parameter to extract the feature vectors. Experiment results show that the proposed algorithm can overcome fur. ther the mode mixing problem of EEMD method and reduce needless calculated amount. Combining with the fuzzy C-means algo. rithm,the proposed algorithm can quickly and accurately classify disturbance signal.
作者
赵乾坤
曹玲芝
ZHAO Qiankun;CAO Lingzhi(Zhengzhou University of Light Industry,Zhengzhou 450002)
出处
《计算机与数字工程》
2019年第5期1067-1071,共5页
Computer & Digital Engineering
关键词
集合经验模态分解
最优参数选择
模糊C均值聚类
ensemble empirical mode decomposition
optimal parameter selection
fuzzy C-means clustering
