基于多分辨奇异值分解包和随机森林的电能质量扰动分类研究

Zhang Jianing, Luo Yuewan,Guo linming, Yang Xiaomei

Computer Applications and Software（2023）

Cited 0|Views0

No score

Abstract

为解决电能质量扰动的分类问题,利用多分辨奇异值分解(Singular Value Decomposition,SVD)的信号逐层分解方式,提出基于多分辨SVD包与随机森林(Multi-Resolution SVD and Random Forest,MRSVD-RF)的电能质量扰动分类方法.通过实验证明了该算法对单一和复合电能质量信号的分类效果明显优于分解结构相似的基于的小波包的信号分解方式,比较了分类器模型的选择和特征提取数量对算法性能的影响.

Key words

Singular value decomposition,Multi-resolution SVD package,Power quality,Disturbance classification,Random forest

AI Read Science

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Chat Paper

Summary is being generated by the instructions you defined