摘要
电能质量的分类和识别是电能质量控制的基础。为了高效识别电能质量问题类型,提出了1种基于小波变换和支持向量机的电能质量分类识别方法。该方法采用Db7小波基,应用两分类SVM算法,并采用样本熵提高系统的识别效率,经过理论分析和实际验证,所提方法的平均正确率达到97.92%,基本上能实现对电能质量问题的分类和识别。
The classification and identification of power quality is the basis of power quality control.In order to identify the type of power quality problems efficiently,this paper proposes a power quality classification and identification method based on wavelet transform(WT)and support vector machines(SVM),adopting Db7 as wavelet base and two-class SVM algorithms.The method also uses sample entropy to improve the identification efficiency of the system.Theoretical analysis and actual verification shows that the average accuracy of this method is 97.92%,basically realizing the classification and identification of the power quality problems.
作者
马嘉秀
徐玮浓
何复兴
邵诗韵
赵家乐
李宁
MA Jiaxiu;XU Weinong;HE Fuxing;SHAO Shiyun;ZHAO Jiale;LI Ning(School of Electronic Information and Optical Engineering,Nankai University,Tianjin 300071,China;Schoolof Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,China;School ofComputer Information Engineering,Jiangxi Normal University,Nanchang 330022,China)
出处
《智慧电力》
北大核心
2019年第3期16-22,37,共8页
Smart Power
基金
国家自然科学基金项目(51507140)
国家留学基金委国际清洁能源拔尖人才项目([2018]5046)
陕西省自然科学基础研究计划(2018JM5041)~~
