Noisy and non-stationary power quality disturbance classification based on adaptive segmentation empirical wavelet transform and support vector machine-Reference-Cited by-同舟云学术

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Noisy and non-stationary power quality disturbance classification based on adaptive segmentation empirical wavelet transform and support vector machine

Published:2024-08 Issue: Volume:118 Page:109346
ISSN:0045-7906
Container-title:Computers and Electrical Engineering
language:en
Short-container-title:Computers and Electrical Engineering

Author:

Khetarpal Poras,Nagpal Neelu^ORCID,Alhelou Hassan Haes,Siano Pierluigi,Al-Numay Mohammed

Funder

King Saud University

Publisher

Elsevier BV

Reference41 articles.

1. A critical and comprehensive review on power quality disturbance detection and classification;Khetarpal;Sustain Comput Inform Syst,2020

2. Decentralised stochastic disturbance observer-based optimal frequency control method for interconnected power systems with high renewable shares;Alhelou;IEEE Trans Ind Inf,2021

3. Analysis of nonstationary power-quality waveforms using iterative Hilbert huang transform and SAX algorithm;Afroni;IEEE Trans Power Deliv,2013

4. Dynamic state estimation for improving observation and resiliency of interconnected power systems;Alhelou;IEEE Trans Ind Appl,2024

5. Crackle sounds analysis by empirical mode decomposition;Charleston-Villalobos;IEEE Eng Med Biol Mag,2007

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