基于HHT的电能质量扰动定位与分类  

Power quality disturbance for location and classification based on HHT

作　　者：田振果 傅成华[1] 吴浩[1] 李莺[1] 

机构地区：[1]四川理工学院自动化与电子信息学院,四川自贡643000

出　　处：《电力系统保护与控制》2015年第16期36-42,共7页Power System Protection and Control

基　　金：四川省科技厅支撑项目(2013GZ0030);人工智能四川省重点实验室项目(2012RYY06;2013RYY01);四川省教育厅项目(11ZB100);四川理工学院培育项目(2012PY18);自贡市科技局项目(2012D08)

摘　　要：针对电能质量扰动定位和识别分类的需求,提出了一种基于HHT的电能质量扰动定位与分类的新方法。采用HHT算法对电能质量扰动信号进行变换,获得瞬时幅值、Hilbert谱和边际谱,并利用Hilbert谱对扰动信号进行定位。从瞬时幅值、Hilbert谱和边际谱中提取特征量,为决策分类树提供判断依据以便进行分类识别。仿真实验结果表明,采用HHT算法与决策分类树相结合的电能质量扰动定位与分类不需训练,提取的特征量少而有效,分类识别的效果较好,具有良好的抗噪性能。According to the demands of using power quality disturbance for localization, recognition and classification, this paper proposes a new method of using power quality disturbance for localization and classification based on HHT. Firstly, power quality disturbance signals are transformed by using the HHT algorithm to get instantaneous amplitudes, Hilbert spectra and marginal spectra. Secondly, disturbance signals can be located by using instantaneous frequency to record the beginning time and the ending time of disturbance. Thirdly, characteristic parameters can be extracted from instantaneous amplitude, Hilbert spectrum and marginal spectrum, serving as evidence for using the decision-making tree for recognition and classification. The simulation results show that the margin of error can be reduced by using Hilbert spectrum for localization. By combining the HHT algorithm and the decision-making tree, no extra training is needed in using power quality disturbance for recognition and classification. Other advantages include extracting effective but fewer characteristic parameters, more desirable effects of recognition and classification, as well as its favorable nnise-proof capability.

关 键 词：HHT 电能质量扰动 定位 分类 

分 类 号：TM711[电气工程—电力系统及自动化]