Power Quality Data Compression and Disturbances Recognition Based on Deep CS-BiLSTM Algorithm With Cloud-Edge Collaboration

The current disturbance classification of power quality data often has the problem of low disturbance recognition accuracy due to its large volume and difficult feature extraction. This paper proposes a hybrid model based on distributed compressive sensing and a bi-directional long-short memory netw...

ver descrição completa

Na minha lista:
Detalhes bibliográficos
Main Authors: Xin Xia, Chuanliang He, Yingjie Lv, Bo Zhang, ShouZhi Wang, Chen Chen, Haipeng Chen
Formato: Artigo
Idioma:Inglês
Publicado em: Frontiers Media S.A. 2022-04-01
Colecção:Frontiers in Energy Research
Assuntos:
distributed compressed sensing
power quality disturbance classification
bidirectional long-short memory network
edge algorithm and cloud edge collaboration
parameter optimization
DDPG algorithm
Acesso em linha:https://www.frontiersin.org/articles/10.3389/fenrg.2022.874351/full
Tags: Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!

Registos relacionados