The use of deep learning and 2-D wavelet scalograms for power quality disturbances classification-Reference-Cited by-同舟云学术

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The use of deep learning and 2-D wavelet scalograms for power quality disturbances classification

Published:2023-01 Issue: Volume:214 Page:108834
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Salles Rafael S.^ORCID,Ribeiro Paulo F.^ORCID

Funder

Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Instituto Nacional de Energia Elétrica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference49 articles.

1. Signal Processing of Power Quality Disturbances;Bollen,2006

2. Guest editorial introduction to the special issue on “advanced signal processing techniques and telecommunications network infrastructures for smart grid analysis, monitoring, and management”;Bracale;EURASIP J. Adv. Signal Process.,2015

3. Power quality following deregulation;Arrillaga;Proc. IEEE,2000

4. Power systems signal processing for smart grids,2013

5. Deep learning in electrical utility industry: A comprehensive review of a decade of research;Mishra;Eng. Appl. Artif. Intell.,2020

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1. Classification of complex power quality disturbances under noisy environment using Root-Music and least square techniques;Electric Power Systems Research;2025-01

2. A framework for complex power quality disturbances classification and parameter estimation using convolutional neural network and square-root cubature Kalman filter in compressed sensing domain;Electric Power Systems Research;2024-11

3. Sample-by-sample Power Quality Disturbance classification based on Sliding Window Recursive Discrete Fourier Transform;Electric Power Systems Research;2024-10

4. A method for disturbance identification in power quality based on cross-attention fusion of temporal and spatial features;Electric Power Systems Research;2024-09

5. A new deep learning method for classification of power quality disturbances using DWT-MRA in utility smart grid;Computers and Electrical Engineering;2024-07

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