Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model-Reference-Cited by-同舟云学术

Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model

Published:2023 Issue: Volume:11 Page:890-899
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Liu Mingping¹^ORCID,Chen Yue²,Zhang Zhen¹,Deng Suhui¹^ORCID

Affiliation:

1. School of Information Engineering, Nanchang University, Nanchang, China

2. School of Qianhu, Nanchang University, Nanchang, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangxi Province of China

Interdisciplinary Innovation Fund of Natural Science, Nanchang University

National College Students' Innovation and Entrepreneurship Training Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10004963.pdf?arnumber=10004963

Reference38 articles.

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2. An efficient algorithm for atomic decomposition of power quality disturbance signals using convolutional neural network

3. An advanced genetic algorithm with improved support vector machine for multi-class classification of real power quality events;choudhary;Electr Power Syst Res,2021

4. A comparison of methods for multiclass support vector machines

5. Power quality disturbance classification under noisy conditions using adaptive wavelet threshold and DBN-ELM hybrid model

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